## ----setup, include=FALSE-------------------------------------------
## use collapse for bookdown, to collapse all the source and output
## blocks from one code chunk into a single block
time0 <- Sys.time()
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
options(width = 70)
require(BiocStyle)
require(pander)


## https://bookdown.org/yihui/rmarkdown-cookbook/time-chunk.html
all_times <- list()  # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      now <<- Sys.time()
    } else {
      res <- difftime(Sys.time(), now, units = "secs")
      all_times[[options$label]] <<- as.double(res)
    }
  }
}))

knitr::opts_chunk$set(time_it = TRUE)

## ----frelats, eval=TRUE,echo=FALSE, fig.cap="Relationships between the main functions in OncoSimulR."----
knitr::include_graphics("relfunct.png")

## ----firstload------------------------------------------------------
## Load the package
library(OncoSimulR) 

## ----rzx0, echo=FALSE-----------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----ex-dag-inf-----------------------------------------------------
## For reproducibility
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## Simulate a DAG
g1 <- simOGraph(4, out = "rT")

## Simulate 10 evolutionary trajectories
s1 <- oncoSimulPop(10, allFitnessEffects(g1, drvNames = 1:4),
                   onlyCancer = TRUE,
                   mc.cores = 2, ## adapt to your hardware
                   seed = NULL) ## for reproducibility of vignette

## Sample those data uniformly, and add noise
d1 <- samplePop(s1, timeSample = "unif", propError = 0.1)

## You would now run the appropriate inferential method and
## compare observed and true. For example

## require(Oncotree)
## fit1 <- oncotree.fit(d1)

## Now, you'd compare fitted and original. This is well beyond 
## the scope of this document (and OncoSimulR itself).


## ----hidden-rng-exochs, echo = FALSE--------------------------------
## set.seed(NULL)

## ----hiddenochs, echo=FALSE-----------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----exochs---------------------------------------------------------
## For reproducibility
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## Specify fitness effects. 

## Numeric values arbitrary, but set the intermediate genotype en
## route to ui as mildly deleterious so there is a valley.

## As in Ochs and Desai, the ui and uv genotypes
## can never appear. 

u <- 0.2; i <- -0.02; vi <- 0.6; ui <- uv <- -Inf

od <- allFitnessEffects(
    epistasis = c("u" = u,  "u:i" = ui,
                  "u:v" = uv, "i" = i,
                  "v:-i" = -Inf, "v:i" = vi))

## For the sake of extending this example, also turn i into a
## mutator gene

odm <- allMutatorEffects(noIntGenes = c("i" = 50))

## How do mutation and fitness look like for each genotype?
evalAllGenotypesFitAndMut(od, odm, addwt = TRUE)

## ----exochsb--------------------------------------------------------
## Set a small initSize, as o.w. unlikely to pass the valley
initS <- 10
## The number of replicates is tiny, for the sake of speed
## of creation of the vignette. Even fewer in Windows, since we run on a single
## core

if(.Platform$OS.type == "windows") {
    nruns <- 4
} else {
    nruns <- 10
}

od_sim <- oncoSimulPop(nruns, od, muEF = odm,
                       fixation = c("u", "i, v"), initSize = initS,
                       model = "McFL",
                       mu = 1e-4, detectionDrivers = NA, 
					   finalTime = NA,
                       detectionSize = NA, detectionProb = NA,
                       onlyCancer = TRUE, 
					   mc.cores = 2, ## adapt to your hardware
                       seed = NULL) ## for reproducibility
## What is the frequency of each final genotype?
sampledGenotypes(samplePop(od_sim))

## ----hidden-rng-exochs33, echo = FALSE------------------------------
## set.seed(NULL)

## ----hiddenrng0szen, echo=FALSE-------------------------------------
set.seed(7)
RNGkind("L'Ecuyer-CMRG")

## ----exszendro------------------------------------------------------
## For reproducibility
set.seed(7)
RNGkind("L'Ecuyer-CMRG")


## Repeat the following loop for different combinations of whatever
## interests you, such as number of genes, or distribution of the
## c and sd (which affect how rugged the landscape is), or 
## reference genotype, or evolutionary model, or stopping criterion, 
## or sampling procedure, or ...

##  Generate a random fitness landscape, from the Rough Mount
##  Fuji model, with g genes, and c ("slope" constant) and
##  reference chosen randomly (reference is random by default and 
##  thus not specified below). Require a minimal number of  
##  accessible genotypes

g <- 6
c <- runif(1, 1/5, 5)
rl <- rfitness(g, c = c, min_accessible_genotypes = g)

## Plot it if you want; commented here as it takes long for a
## vignette

## plot(rl)

## Obtain landscape measures from MAGELLAN. Export to MAGELLAN and
## call your own copy of MAGELLAN's binary

## to_Magellan(rl, file = "rl1.txt") ## (Commented out here to avoid writing files)

## or use the binary copy provided with OncoSimulR
## see also below.

Magellan_stats(rl) ## (Commented out here to avoid writing files)

## Simulate evolution in that landscape many times (here just 10)
simulrl <- oncoSimulPop(10, allFitnessEffects(genotFitness = rl),
                        keepPhylog = TRUE, keepEvery = 1,
                        onlyCancer = TRUE,
                        initSize = 4000,
                        seed = NULL, ## for reproducibility
                        mc.cores = 2) ## adapt to your hardware

## Obtain measures of evolutionary predictability
diversityLOD(LOD(simulrl))
diversityPOM(POM(simulrl))
sampledGenotypes(samplePop(simulrl, typeSample = "whole"))

## ----hidden-rng-exszend, echo = FALSE-------------------------------
## set.seed(NULL)

## ----ex-tomlin1-----------------------------------------------------
sd <- 0.1 ## fitness effect of drivers
sm <- 0 ## fitness effect of mutator
nd <- 20 ## number of drivers
nm <- 5  ## number of mutators
mut <- 10 ## mutator effect

fitnessGenesVector <- c(rep(sd, nd), rep(sm, nm))
names(fitnessGenesVector) <- 1:(nd + nm)
mutatorGenesVector <- rep(mut, nm)
names(mutatorGenesVector) <- (nd + 1):(nd + nm)

ft <- allFitnessEffects(noIntGenes = fitnessGenesVector,
                        drvNames = 1:nd)
mt <- allMutatorEffects(noIntGenes = mutatorGenesVector)


## ----hiddentom, echo=FALSE------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----ex-tomlin2-----------------------------------------------------
## For reproducibility
set.seed(2)
RNGkind("L'Ecuyer-CMRG")


ddr <- 4
st <- oncoSimulPop(4, ft, muEF = mt,
                   detectionDrivers = ddr,
                   finalTime = NA,
                   detectionSize = NA,
                   detectionProb = NA,
                   onlyCancer = TRUE,
                   keepEvery = NA, 
                   mc.cores = 2, ## adapt to your hardware
                   seed = NULL) ## for reproducibility

## How long did it take to reach cancer?
unlist(lapply(st, function(x) x$FinalTime))


## ----hidden-rng-tom, echo = FALSE-----------------------------------
## set.seed(NULL)

## ----exusagebau-----------------------------------------------------
K <- 4
sp <- 1e-5
sdp <- 0.015
sdplus <- 0.05
sdminus <- 0.1

cnt <- (1 + sdplus)/(1 + sdminus)
prod_cnt <- cnt - 1
bauer <- data.frame(parent = c("Root", rep("D", K)),
                    child = c("D", paste0("s", 1:K)),
                    s = c(prod_cnt, rep(sdp, K)),
                    sh = c(0, rep(sp, K)),
                    typeDep = "MN")
fbauer <- allFitnessEffects(bauer)
(b1 <- evalAllGenotypes(fbauer, order = FALSE, addwt = TRUE))

## How does the fitness landscape look like?
plot(b1, use_ggrepel = TRUE) ## avoid overlapping labels

## ----hiddenbau, echo=FALSE------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----exusagebau2----------------------------------------------------
## For reproducibility
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

totalpops <- 5
initSize <- 100
sb1 <- oncoSimulPop(totalpops, fbauer, model = "Exp",
                    initSize = initSize,
                    onlyCancer = FALSE, 
					mc.cores = 2, ## adapt to your hardware
                    seed = NULL) ## for reproducibility
					
## What proportion of the simulations reach 4x initSize?
sum(summary(sb1)[, "TotalPopSize"] > (4 * initSize))/totalpops


## ----hidden-rng-exbau, echo = FALSE---------------------------------
## set.seed(NULL)

## ----hiddenbau22, echo=FALSE----------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----hhhhbbbb22-----------------------------------------------------

totalpops <- 5
initSize <- 100
sb2 <- oncoSimulPop(totalpops, fbauer, model = "Exp",
                    initSize = initSize,
                    onlyCancer = TRUE,
					detectionSize = 10 * initSize,
					mc.cores = 2, ## adapt to your hardware
                    seed = NULL) ## for reproducibility
				
## How long did it take to reach cancer?
unlist(lapply(sb2, function(x) x$FinalTime))

## ----hidden-rng-exbau22, echo = FALSE-------------------------------
## set.seed(NULL)

## ----oex1intro------------------------------------------------------
## Order effects involving three genes.

## Genotype "D, M" has different fitness effects
## depending on whether M or D mutated first.
## Ditto for genotype "F, D, M".

## Meaning of specification: X > Y means
## that X is mutated before Y.


o3 <- allFitnessEffects(orderEffects = c(
                            "F > D > M" = -0.3,
                            "D > F > M" = 0.4,
                            "D > M > F" = 0.2,
                            "D > M"     = 0.1,
                            "M > D"     = 0.5))

## With the above specification, let's double check
## the fitness of the possible genotypes

(oeag <- evalAllGenotypes(o3, addwt = TRUE, order = TRUE))


## ----hiddoef, echo=FALSE--------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----exusageoe2-----------------------------------------------------
## For reproducibility
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

totalpops <- 5
soe1 <- oncoSimulPop(totalpops, o3, model = "Exp",
                    initSize = 500,
                    onlyCancer = FALSE,
					mc.cores = 2, ## adapt to your hardware
                    seed = NULL) ## for reproducibility
					
## What proportion of the simulations do not end up extinct?
sum(summary(soe1)[, "TotalPopSize"] > 0)/totalpops


## ----hidden-rng-exoef, echo = FALSE---------------------------------
## set.seed(NULL)

## ----sswm-rfitness-funct--------------------------------------------
## oncoSimul object  -> measures of clonal interference
##    they are not averaged over time. One value for sampled time
clonal_interf_per_time <- function(x) {
    x <- x$pops.by.time
    y <- x[, -1, drop = FALSE]
    shannon <- apply(y, 1, OncoSimulR:::shannonI)
    tot <- rowSums(y)
    half_tot <- tot * 0.5
    five_p_tot <- tot * 0.05
    freq_most_freq <- apply(y/tot, 1, max)
    single_more_half <- rowSums(y > half_tot)
    ## whether more than 1 clone with more than 5% pop.
    how_many_gt_5p <- rowSums(y > five_p_tot)
    several_gt_5p <- (how_many_gt_5p > 1)
    return(cbind(shannon, ## Diversity of clones
                 freq_most_freq, ## Frequency of the most freq. clone
                 single_more_half, ## Any clone with a frequency > 50%?
                 several_gt_5p, ## Are there more than 1 clones with
                                ## frequency > 5%?
                 how_many_gt_5p ## How many clones are there with
                                ## frequency > 5%
                 ))
}

## ----sswm-rfitness0, eval=FALSE-------------------------------------
#  set.seed(1)
#  r7b <- rfitness(7, scale = c(1.2, 0, 1))
#  
#  ## Large pop sizes: clonal interference
#  (sr7b <- oncoSimulIndiv(allFitnessEffects(genotFitness = r7b),
#                         model = "McFL",
#                         mu = 1e-6,
#                         onlyCancer = FALSE,
#                         finalTime = 400,
#                         initSize = 1e7,
#                         keepEvery = 4,
#                         detectionSize = 1e10))
#  
#  plot(sr7b, show = "genotypes")
#  
#  colMeans(clonal_interf_per_time(sr7b))

## ----sswm-rfitness, eval=FALSE--------------------------------------
#  ## Small pop sizes: a single clone most of the time
#  (sr7c <- oncoSimulIndiv(allFitnessEffects(genotFitness = r7b),
#                         model = "McFL",
#                         mu = 1e-6,
#                         onlyCancer = FALSE,
#                         finalTime = 60000,
#                         initSize = 1e3,
#                         keepEvery = 4,
#                         detectionSize = 1e10))
#  
#  plot(sr7c, show = "genotypes")
#  
#  colMeans(clonal_interf_per_time(sr7c))
#  
#  
#  
#  ## Even smaller fitness differences, but large pop. sizes
#  set.seed(1); r7b2 <- rfitness(7, scale = c(1.05, 0, 1))
#  
#  (sr7b2 <- oncoSimulIndiv(allFitnessEffects(genotFitness = r7b2),
#                         model = "McFL",
#                         mu = 1e-6,
#                         onlyCancer = FALSE,
#                         finalTime = 3500,
#                         initSize = 1e7,
#                         keepEvery = 4,
#                         detectionSize = 1e10))
#  sr7b2
#  plot(sr7b2, show = "genotypes")
#  colMeans(clonal_interf_per_time(sr7b2))
#  
#  
#  ## Increase pop size further
#  (sr7b3 <- oncoSimulIndiv(allFitnessEffects(genotFitness = r7b2),
#                         model = "McFL",
#                         mu = 1e-6,
#                         onlyCancer = FALSE,
#                         finalTime = 1500,
#                         initSize = 1e8,
#                         keepEvery = 4,
#                         detectionSize = 1e10))
#  sr7b3
#  plot(sr7b3, show = "genotypes")
#  colMeans(clonal_interf_per_time(sr7b3))
#  

## ----rzx011, results="hide",message=FALSE, echo=TRUE, include=TRUE----
library(OncoSimulR)
library(graph)
library(igraph)
igraph_options(vertex.color = "SkyBlue2")

## ----rzx02, echo=FALSE, results='hide'----------------------------
options(width = 68)

## -----------------------------------------------------------------
packageVersion("OncoSimulR")

## ----f7b3v, fig.width=6.5, fig.height=10--------------------------
## 1. Fitness effects: here we specify an 
##    epistatic model with modules.
sa <- 0.1
sb <- -0.2
sab <- 0.25
sac <- -0.1
sbc <- 0.25
sv2 <- allFitnessEffects(epistasis = c("-A : B" = sb,
                                       "A : -B" = sa,
                                       "A : C" = sac,
                                       "A:B" = sab,
                                       "-A:B:C" = sbc),
                         geneToModule = c(
                             "A" = "a1, a2",
                             "B" = "b",
                             "C" = "c"),
                         drvNames = c("a1", "a2", "b", "c"))
evalAllGenotypes(sv2, addwt = TRUE)

## 2. Simulate the data. Here we use the "McFL" model and set
##    explicitly parameters for mutation rate, initial size, size
##    of the population that will end the simulations, etc

RNGkind("Mersenne-Twister")
set.seed(983)
ep1 <- oncoSimulIndiv(sv2, model = "McFL",
                      mu = 5e-6,
                      sampleEvery = 0.025,
                      keepEvery = 0.5,
                      initSize = 2000,
                      finalTime = 3000,
                      onlyCancer = FALSE)

## ----iep1x1,fig.width=6.5, fig.height=4.5, fig.cap="Plot of drivers of an epistasis simulation."----
## 3. We will not analyze those data any further. We will only plot
## them.  For the sake of a small plot, we thin the data.
plot(ep1, show = "drivers", xlim = c(0, 1500),
     thinData = TRUE, thinData.keep = 0.5)

## ----fepancr1, fig.width=5----------------------------------------
## 1. Fitness effects: 
pancr <- allFitnessEffects(
    data.frame(parent = c("Root", rep("KRAS", 4), 
                   "SMAD4", "CDNK2A", 
                   "TP53", "TP53", "MLL3"),
               child = c("KRAS","SMAD4", "CDNK2A", 
                   "TP53", "MLL3",
                   rep("PXDN", 3), rep("TGFBR2", 2)),
               s = 0.1,
               sh = -0.9,
               typeDep = "MN"),
    drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53", 
	             "MLL3", "TGFBR2", "PXDN"))

## ----figfpancr1, fig.width=5, fig.cap="Plot of DAG corresponding to fitnessEffects object."----
## Plot the DAG of the fitnessEffects object
plot(pancr)

## ----theformerunnamed6--------------------------------------------
## 2. Simulate from it. We change several possible options. 

set.seed(1) ## Fix the seed, so we can repeat it
## We set a small finalTime to speed up the vignette


ep2 <- oncoSimulIndiv(pancr, model = "McFL",
                     mu = 1e-6,
                     sampleEvery = 0.02,
                     keepEvery = 1,
                     initSize = 1000,
                     finalTime = 20000,
                     detectionDrivers = 3,
                     onlyCancer = FALSE)


## ----iep2x2, fig.width=6.5, fig.height=5, fig.cap= "Plot of genotypes of a simulation from a DAG."----
## 3. What genotypes and drivers we get? And play with limits
##    to show only parts of the data. We also aggressively thin
##    the data.
par(cex = 0.7)
plot(ep2, show = "genotypes", xlim = c(500, 1800), 
     ylim = c(0, 2400),
     thinData = TRUE, thinData.keep = 0.3)

## -----------------------------------------------------------------
citation("OncoSimulR")

## ----colnames_benchmarks, echo = FALSE, eval = TRUE---------------

data(benchmark_1)
data(benchmark_1_0.05)
data(benchmark_2)
data(benchmark_3)

colnames(benchmark_1)[
    match(c(
	"time_per_simul",
    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean",
	"TotalPopSize.Max.", "keepEvery",  "Attempts.Median",
	"Attempts.Mean", "Attempts.Max.",
	"PDBaseline", "n2", "onlyCancer"),
	 colnames(benchmark_1)
	)] <- c("Elapsed Time, average per simulation (s)",
	              "Object Size, average per simulation (MB)",
				  "Number of Clones, median",
				  "Number of Iterations, median",
				  "Final Time, median",
				  "Total Population Size, median",
				   "Total Population Size, mean",
				  "Total Population Size, max.",
				  "keepEvery",
				  "Attempts until Cancer, median",
				  "Attempts until Cancer, mean",
				  "Attempts until Cancer, max.",
				  "PDBaseline", "n2", "onlyCancer"
				  )
				  
	
colnames(benchmark_1_0.05)[
    match(c("time_per_simul",
    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
	"TotalPopSize.Max.",
	"keepEvery",
	"PDBaseline", "n2", "onlyCancer", "Attempts.Median"),
	colnames(benchmark_1_0.05))] <- c("Elapsed Time, average per simulation (s)",
	              "Object Size, average per simulation (MB)",
				  "Number of Clones, median",
				  "Number of Iterations, median",
				  "Final Time, median",
				  "Total Population Size, median",
				  "Total Population Size, mean",
				  "Total Population Size, max.",
				  "keepEvery",
				  "PDBaseline", "n2", "onlyCancer",
				  "Attempts until Cancer, median"
				  )


colnames(benchmark_2)[match(c("Model", "fitness", "time_per_simul",
    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
	"TotalPopSize.Max."), colnames(benchmark_2))] <-  c("Model",
				  "Fitness",
	"Elapsed Time, average per simulation (s)",
	              "Object Size, average per simulation (MB)",
				  "Number of Clones, median",
				  "Number of Iterations, median",
				  "Final Time, median",
				  "Total Population Size, median",
				  "Total Population Size, mean",
				  "Total Population Size, max."
				  )	
				  
colnames(benchmark_3)[match(c("Model", "fitness", "time_per_simul",
    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
	"TotalPopSize.Max."), colnames(benchmark_3))] <-  c("Model",
				  "Fitness",
	"Elapsed Time, average per simulation (s)",
	              "Object Size, average per simulation (MB)",
				  "Number of Clones, median",
				  "Number of Iterations, median",
				  "Final Time, median",
				  "Total Population Size, median",
				  "Total Population Size, mean",
				  "Total Population Size, max."
				  )					  

## ----timing1, eval=FALSE------------------------------------------
#  ## Specify fitness
#  pancr <- allFitnessEffects(
#      data.frame(parent = c("Root", rep("KRAS", 4),
#                     "SMAD4", "CDNK2A",
#                     "TP53", "TP53", "MLL3"),
#                 child = c("KRAS","SMAD4", "CDNK2A",
#                     "TP53", "MLL3",
#                     rep("PXDN", 3), rep("TGFBR2", 2)),
#                 s = 0.1,
#                 sh = -0.9,
#                 typeDep = "MN"),
#      drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53",
#  	             "MLL3", "TGFBR2", "PXDN"))
#  
#  Nindiv <- 100 ## Number of simulations run.
#                ## Increase this number to decrease sampling variation
#  
#  ## keepEvery = 1
#  t_exp1 <- system.time(
#      exp1 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = "default",
#                           detectionSize = NA,
#                           detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = 1,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  
#  
#  t_mc1 <- system.time(
#      mc1 <- oncoSimulPop(Nindiv, pancr,
#                          onlyCancer = TRUE,
#                          detectionProb = "default",
#                          detectionSize = NA,
#                          detectionDrivers = NA,
#                             finalTime = NA,
#                             keepEvery = 1,
#                             model = "McFL",
#                             mc.cores = 1))["elapsed"]/Nindiv
#  
#  ## keepEvery = NA
#  t_exp2 <- system.time(
#      exp2 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = "default",
#                           detectionSize = NA,
#                              detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = NA,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  
#  
#  t_mc2 <- system.time(
#      mc2 <- oncoSimulPop(Nindiv, pancr,
#                          onlyCancer = TRUE,
#                          detectionProb = "default",
#                          detectionSize = NA,
#                             detectionDrivers = NA,
#                             finalTime = NA,
#                             keepEvery = NA,
#                             model = "McFL",
#                             mc.cores = 1))["elapsed"]/Nindiv
#  
#  

## ----eval=FALSE---------------------------------------------------
#  cat("\n\n\n t_exp1 = ", t_exp1, "\n")
#  object.size(exp1)/(Nindiv * 1024^2)
#  cat("\n\n")
#  summary(unlist(lapply(exp1, "[[", "NumClones")))
#  summary(unlist(lapply(exp1, "[[", "NumIter")))
#  summary(unlist(lapply(exp1, "[[", "FinalTime")))
#  summary(unlist(lapply(exp1, "[[", "TotalPopSize")))

## ----bench1, eval=TRUE, echo = FALSE------------------------------

panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 8) ## For PDF

set.alignment('right')
panderOptions('round', 2)
panderOptions('big.mark', ',')
panderOptions('digits', 2)
				          
pander(benchmark_1[1:4, c("Elapsed Time, average per simulation (s)", 
 	              "Object Size, average per simulation (MB)",
 				  "Number of Clones, median",
 				  "Number of Iterations, median",
 				  "Final Time, median",
 				  "Total Population Size, median",
 				  "Total Population Size, max.",
 				  "keepEvery")],
				  justify = c('left', rep('right', 8)), ##  o.w. hlines not right
				  ## caption = "\\label{tab:bench1}Benchmarks of Exp and McFL  models using the default `detectionProb` with two settings of `keepEvery`."
				  )

## ----timing2, eval = FALSE----------------------------------------
#  t_exp3 <- system.time(
#      exp3 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = c(PDBaseline = 5e4,
#                                             p2 = 0.1, n2 = 5e5,
#                                             checkSizePEvery = 20),
#                           detectionSize = NA,
#                           detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = 1,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  
#  t_exp4 <- system.time(
#      exp4 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = c(PDBaseline = 5e4,
#                                             p2 = 0.1, n2 = 5e5,
#                                             checkSizePEvery = 20),
#                           detectionSize = NA,
#                           detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = NA,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  
#  
#  
#  t_exp5 <- system.time(
#      exp5 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = c(PDBaseline = 5e5,
#                                             p2 = 0.1, n2 = 5e7),
#                           detectionSize = NA,
#                           detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = 1,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  
#  t_exp6 <- system.time(
#      exp6 <- oncoSimulPop(Nindiv, pancr,
#                           onlyCancer = TRUE,
#                           detectionProb = c(PDBaseline = 5e5,
#                                             p2 = 0.1, n2 = 5e7),
#                           detectionSize = NA,
#                           detectionDrivers = NA,
#                              finalTime = NA,
#                              keepEvery = NA,
#                              model = "Exp",
#                              mc.cores = 1))["elapsed"]/Nindiv
#  

## ----bench1b, eval=TRUE, echo = FALSE-----------------------------
panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 8) ## For PDF

set.alignment('right')
panderOptions('round', 2)
panderOptions('big.mark', ',')
panderOptions('digits', 2)

pander(benchmark_1[5:8, c("Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)",
 				  "Number of Clones, median",
 				  "Number of Iterations, median",
 				  "Final Time, median",
 				  "Total Population Size, median",
 				  "Total Population Size, max.",
 				  "keepEvery",
				  "PDBaseline",
				  "n2")], 
				  justify = c('left', rep('right', 10)), ##  o.w. hlines not right
## 				  round = c(rep(2, 3), rep(0, 7)),
## 				  digits = c(rep(2, 3), rep(1, 7)),
	  ## caption = "\\label{tab:bench1b}Benchmarks of Exp and McFL models modifying the default `detectionProb` with two settings of `keepEvery`."
    )


## ----bench1c, eval=TRUE, echo = FALSE-----------------------------
panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 12) ## For PDF
set.alignment('right')
panderOptions('round', 2)
panderOptions('big.mark', ',')
panderOptions('digits', 2)

pander(benchmark_1[1:8, c(
"Attempts until Cancer, median", 
"Attempts until Cancer, mean", 
"Attempts until Cancer, max.", 
				  "PDBaseline",
				  "n2")], 
				  justify = c('left', rep('right', 5)), ##  o.w. hlines not right
## 				  round = c(rep(2, 3), rep(0, 7)),
## 				  digits = c(rep(2, 3), rep(1, 7)),
	  ## caption = "\\label{tab:bench1c}Number of attempts until cancer."
    )
## ## data(benchmark_1)
## knitr::kable(benchmark_1[1:8, c("Attempts.Median",
##                                 "PDBaseline", "n2"), drop = FALSE], 
##     booktabs = TRUE,
## 	row.names = TRUE,
## 	col.names = c("Attempts until cancer", "PDBaseline", "n2"),
##     caption = "Median number of attempts until cancer.", 
## 	align = "r")
	

## ----bench1d, eval=TRUE, echo = FALSE-----------------------------
panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 8) ## For PDF
panderOptions('table.split.cells', 15) ## does not fit otherwise
set.alignment('right')
panderOptions('round', 3)

pander(benchmark_1[9:16, 
    c("Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)",
 				  "Number of Clones, median",
 				  "Number of Iterations, median",
 				  "Final Time, median",
 				  "Total Population Size, median",
				  "Total Population Size, mean",
 				  "Total Population Size, max.",
 				  "keepEvery",
				  "PDBaseline",
				  "n2")],
				  justify = c('left', rep('right', 11)), ##  o.w. hlines not right
## caption = "\\label{tab:timing3} Benchmarks of models in Table \\@ref(tab:bench1) and \\@ref(tab:bench1b) when run with `onlyCancer = FALSE`."
				  )	
	

## ----bench1dx0, eval=TRUE, echo = FALSE---------------------------
panderOptions('table.split.table', 99999999)
## panderOptions('table.split.cells', 900)  ## For HTML
panderOptions('table.split.cells', 19)

set.alignment('right') 
panderOptions('round', 3)
	
pander(benchmark_1[ , c("Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)", 
				  "Number of Clones, median", 
				  "Number of Iterations, median", 
				  "Final Time, median", "Total Population Size, median", 
				  "Total Population Size, mean", "Total Population Size, max.",
 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 
				  justify = c('left', rep('right', 12)), ##  o.w. hlines not right
				  ## caption = "\\label{tab:allr1bck}Benchmarks of all models in Tables \\@ref(tab:bench1), \\@ref(tab:bench1b),  and \\@ref(tab:timing3)."  
				  )  

## ----bench1dx, eval=TRUE, echo = FALSE----------------------------
## data(benchmark_1_0.05)
## knitr::kable(benchmark_1_0.05[, c("time_per_simul",
##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
## 	"TotalPopSize.Max.",
## 	"keepEvery",
## 	"PDBaseline", "n2", "onlyCancer")], 
##     booktabs = TRUE,
## 	col.names = c("Elapsed Time, average per simulation (s)",
## 	              "Object Size, average per simulation (MB)",
## 				  "Number of Clones, median",
## 				  "Number of Iterations, median",
## 				  "Final Time, median",
## 				  "Total Population Size, median",
## 				  "Total Population Size, mean",
## 				  "Total Population Size, max.",				  
## 				  "keepEvery",
## 				  "PDBaseline", "n2", "onlyCancer"
## 				  ),
## ##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and
## ##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 
## 	align = "c")
	
panderOptions('table.split.table', 99999999)
## panderOptions('table.split.cells', 900)  ## For HTML
panderOptions('table.split.cells', 19)

set.alignment('right') 
panderOptions('round', 3)
	
pander(benchmark_1_0.05[ , c("Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)", 
				  "Number of Clones, median", 
				  "Number of Iterations, median", 
				  "Final Time, median", 
				  "Total Population Size, median", 
				  "Total Population Size, mean", "Total Population Size, max.",
 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 
				  justify = c('left', rep('right', 12)), ##  o.w. hlines not right
 	              ## caption = "\\label{tab:timing3xf}Benchmarks of all models in Table \\@ref(tab:allr1bck) using $s=0.05$ (instead of $s=0.1$)."  
)  
				  

## ----fitusualb, echo = TRUE, eval = FALSE-------------------------
#  pancr <- allFitnessEffects(
#      data.frame(parent = c("Root", rep("KRAS", 4),
#                     "SMAD4", "CDNK2A",
#                     "TP53", "TP53", "MLL3"),
#                 child = c("KRAS","SMAD4", "CDNK2A",
#                     "TP53", "MLL3",
#                     rep("PXDN", 3), rep("TGFBR2", 2)),
#                 s = 0.1,
#                 sh = -0.9,
#                 typeDep = "MN"),
#      drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53",
#  	             "MLL3", "TGFBR2", "PXDN"))
#  
#  
#  ## Random fitness landscape with 6 genes
#  ## At least 50 accessible genotypes
#  rfl6 <- rfitness(6, min_accessible_genotypes = 50)
#  attributes(rfl6)$accessible_genotypes ## How many accessible
#  rf6 <- allFitnessEffects(genotFitness = rfl6)
#  
#  
#  ## Random fitness landscape with 12 genes
#  ## At least 200 accessible genotypes
#  rfl12 <- rfitness(12, min_accessible_genotypes = 200)
#  attributes(rfl12)$accessible_genotypes ## How many accessible
#  rf12 <- allFitnessEffects(genotFitness = rfl12)
#  
#  
#  
#  
#  ## Independent genes; positive fitness from exponential distribution
#  ## with mean around 0.1, and negative from exponential with mean
#  ## around -0.02. Half of genes positive fitness effects, half
#  ## negative.
#  
#  ng <- 200 re_200 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10),
#  -rexp(ng/2, 50)))
#  
#  ng <- 500
#  re_500 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10),
#                                             -rexp(ng/2, 50)))
#  
#  ng <- 2000
#  re_2000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10),
#                                              -rexp(ng/2, 50)))
#  
#  ng <- 4000
#  re_4000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10),
#                                              -rexp(ng/2, 50)))
#  

## ----exp-usual-r, eval = FALSE, echo = TRUE-----------------------
#  
#  oncoSimulPop(Nindiv,
#              fitness,
#              detectionProb = NA,
#              detectionSize = 1e6,
#              initSize = 500,
#              detectionDrivers = NA,
#              keepPhylog = TRUE,
#              model = "Exp",
#              errorHitWallTime = FALSE,
#              errorHitMaxTries = FALSE,
#              finalTime = 5000,
#              onlyCancer = FALSE,
#              mc.cores = 1,
#              sampleEvery = 0.5,
#  			keepEvery = 1)

## ----mc-usual-r, eval = FALSE, echo = TRUE------------------------
#  initSize <- 1000
#  oncoSimulPop(Nindiv,
#                fitness,
#                 detectionProb = c(
#                     PDBaseline = 1.4 * initSize,
#                     n2 = 2 * initSize,
#                     p2 = 0.1,
#                     checkSizePEvery = 4),
#                 initSize = initSize,
#                 detectionSize = NA,
#                 detectionDrivers = NA,
#                 keepPhylog = TRUE,
#                 model = "McFL",
#                 errorHitWallTime = FALSE,
#                 errorHitMaxTries = FALSE,
#                 finalTime = 5000,
#                 max.wall.time = 10,
#                 onlyCancer = FALSE,
#                 mc.cores = 1,
#  			   keepEvery = 1)
#  

## ----benchustable, eval=TRUE, echo = FALSE------------------------
## data(benchmark_2)

## knitr::kable(benchmark_2[, c("Model", "fitness", "time_per_simul",
##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
## 	"TotalPopSize.Max.")], 
##     booktabs = TRUE,
## 	col.names = c("Model",
## 				  "Fitness",
## 	"Elapsed Time, average per simulation (s)",
## 	              "Object Size, average per simulation (MB)",
## 				  "Number of Clones, median",
## 				  "Number of Iterations, median",
## 				  "Final Time, median",
## 				  "Total Population Size, median",
## 				  "Total Population Size, mean",
## 				  "Total Population Size, max."
## 				  ),
## 	align = "c")

panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 8) ## For PDF

## set.alignment('right', row.names = 'center')
panderOptions('table.alignment.default', 'right')

panderOptions('round', 3)

pander(benchmark_2[ , c(
    "Model", "Fitness",
    "Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)",
 				  "Number of Clones, median",
 				  "Number of Iterations, median",
 				  "Final Time, median",
 				  "Total Population Size, median",
 				  "Total Population Size, mean",				  
 				  "Total Population Size, max.")], 
				  justify = c('left', 'left', rep('right', 8)),
				  ## caption = "\\label{tab:timingusual}Benchmarks under some common use cases, set 1." 
				  )	
	

## ----benchustable2, eval=TRUE, echo = FALSE-----------------------
## data(benchmark_3)

## knitr::kable(benchmark_3[, c("Model", "fitness", "time_per_simul",
##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
## 	"TotalPopSize.Max.")], 
##     booktabs = TRUE,
## 	col.names = c("Model",
## 				  "Fitness", "Elapsed Time, average per simulation (s)",
## 	              "Object Size, average per simulation (MB)",
## 				  "Number of Clones, median",
## 				  "Number of Iterations, median",
## 				  "Final Time, median",
## 				  "Total Population Size, median",
## 				  "Total Population Size, mean",
## 				  "Total Population Size, max."
## 				  ),
## 	align = "c")

panderOptions('table.split.table', 99999999)
panderOptions('table.split.cells', 900)  ## For HTML
## panderOptions('table.split.cells', 8) ## For PDF


panderOptions('round', 3)
panderOptions('table.alignment.default', 'right')

pander(benchmark_3[ , c(
    "Model", "Fitness",
    "Elapsed Time, average per simulation (s)",
 	              "Object Size, average per simulation (MB)",
 				  "Number of Clones, median",
 				  "Number of Iterations, median",
 				  "Final Time, median",
 				  "Total Population Size, median",
 				  "Total Population Size, mean",				  
 				  "Total Population Size, max.")],
				  justify = c('left', 'left', rep('right', 8)),
				  ## caption = "\\label{tab:timingusual2}Benchmarks under some common use cases, set 2."
				  )	

## ----exp10000, echo = TRUE, eval = FALSE--------------------------
#  ng <- 10000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  
#  t_e_10000 <- system.time(
#      e_10000 <- oncoSimulPop(5, u, model = "Exp", mu = 1e-7,
#                              detectionSize = 1e6,
#                              detectionDrivers = NA,
#                              detectionProb = NA,
#                              keepPhylog = TRUE,
#                              onlyCancer = FALSE,
#                              mutationPropGrowth = TRUE,
#                              mc.cores = 1))

## ----exp10000-out, echo = TRUE, eval = FALSE----------------------
#  t_e_10000
#  ##    user  system elapsed
#  ##   4.368   0.196   4.566
#  
#  summary(e_10000)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      5017      1180528       415116       143    7547
#  ## 2      3726      1052061       603612       131    5746
#  ## 3      4532      1100721       259510       132    6674
#  ## 4      4150      1283115       829728        99    6646
#  ## 5      4430      1139185       545958       146    6748
#  
#  print(object.size(e_10000), units = "MB")
#  ## 863.9 Mb
#  

## ----exp10000b, eval = FALSE, echo = TRUE-------------------------
#  t_e_10000b <- system.time(
#      e_10000b <- oncoSimulPop(5,
#                               u,
#                               model = "Exp",
#                               mu = 1e-7,
#                               detectionSize = 1e6,
#                               detectionDrivers = NA,
#                               detectionProb = NA,
#                               keepPhylog = TRUE,
#                               onlyCancer = FALSE,
#                               keepEvery = NA,
#                               mutationPropGrowth = TRUE,
#                               mc.cores = 1
#                               ))
#  

## ----exp10000b-out, echo = TRUE, eval = FALSE---------------------
#  t_e_10000b
#  ##    user  system elapsed
#  ##   5.484   0.100   5.585
#  
#  summary(e_10000b)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      2465      1305094       727989        91    6447
#  ## 2      2362      1070225       400329       204    8345
#  ## 3      2530      1121164       436721       135    8697
#  ## 4      2593      1206293       664494       125    8149
#  ## 5      2655      1186994       327835       191    8572
#  
#  print(object.size(e_10000b), units = "MB")
#  ## 488.3 Mb
#  

## ----exp50000, echo = TRUE, eval = FALSE--------------------------
#  ng <- 50000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  t_e_50000 <- system.time(
#      e_50000 <- oncoSimulPop(5,
#                              u,
#                              model = "Exp",
#                              mu = 1e-7,
#                              detectionSize = 1e6,
#                              detectionDrivers = NA,
#                              detectionProb = NA,
#                              keepPhylog = TRUE,
#                              onlyCancer = FALSE,
#                              keepEvery = NA,
#                              mutationPropGrowth = FALSE,
#                              mc.cores = 1
#                              ))
#  
#  
#  t_e_50000
#  ##    user  system elapsed
#  ##  44.192   1.684  45.891
#  
#  summary(e_50000)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      7367      1009949       335455     75.00   18214
#  ## 2      8123      1302324       488469     63.65   17379
#  ## 3      8408      1127261       270690     72.57   21144
#  ## 4      8274      1138513       318152     80.59   20994
#  ## 5      7520      1073131       690814     70.00   18569
#  
#  print(object.size(e_50000), units = "MB")
#  ## 7598.6 Mb

## ----exp50000np, echo = TRUE, eval = FALSE------------------------
#  ng <- 50000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  t_e_50000np <- system.time(
#      e_50000np <- oncoSimulPop(5,
#                                u,
#                                model = "Exp",
#                                mu = 1e-7,
#                                detectionSize = 1e6,
#                                detectionDrivers = NA,
#                                detectionProb = NA,
#                                keepPhylog = TRUE,
#                                onlyCancer = FALSE,
#                                keepEvery = 1,
#                                mutationPropGrowth = FALSE,
#                                mc.cores = 1
#                                ))
#  
#  t_e_50000np
#  ##   user  system elapsed
#  ## 42.316   2.764  45.079
#  
#  summary(e_50000np)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1     13406      1027949       410074     71.97   19469
#  ## 2     12469      1071325       291852     66.00   17834
#  ## 3     11821      1089834       245720     90.00   16711
#  ## 4     14008      1165168       505607     77.61   19675
#  ## 5     14759      1074621       205954     87.68   20597
#  
#  print(object.size(e_50000np), units = "MB")
#  ## 12748.4 Mb
#  

## ----exp50000mpg, echo = TRUE, eval = FALSE-----------------------
#  
#  ng <- 50000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  
#  t_e_50000c <- system.time(
#      e_50000c <- oncoSimulPop(5,
#                               u,
#                               model = "Exp",
#                               mu = 1e-7,
#                               detectionSize = 1e6,
#                               detectionDrivers = NA,
#                               detectionProb = NA,
#                               keepPhylog = TRUE,
#                               onlyCancer = FALSE,
#                               keepEvery = NA,
#                               mutationPropGrowth = TRUE,
#                               mc.cores = 1
#                               ))
#  
#  t_e_50000c
#  ##    user  system elapsed
#  ## 84.228   2.416  86.665
#  
#  summary(e_50000c)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1     11178      1241970       344479     84.74   27137
#  ## 2     12820      1307086       203544     91.94   33448
#  ## 3     10592      1126091       161057     83.81   26064
#  ## 4     11883      1351114       148986     65.68   25396
#  ## 5     10518      1101392       253523     99.79   26082
#  
#  print(object.size(e_50000c), units = "MB")
#  ## 10904.9 Mb
#  

## ----sizedetail, eval = FALSE, echo = TRUE------------------------
#  r1 <- oncoSimulIndiv(u,
#                       model = "Exp",
#                       mu = 1e-7,
#                       detectionSize = 1e6,
#                       detectionDrivers = NA,
#                       detectionProb = NA,
#                       keepPhylog = TRUE,
#                       onlyCancer = FALSE,
#                       mutationPropGrowth = TRUE
#                       )
#  summary(r1)[c(1, 8)]
#  ##   NumClones  FinalTime
#  ## 1      3887        345
#  
#  print(object.size(r1), units = "MB")
#  ## 160 Mb
#  
#  ## Size of the two largest objects inside:
#  sizes <- lapply(r1, function(x) object.size(x)/(1024^2))
#  sort(unlist(sizes), decreasing = TRUE)[1:2]
#  ## Genotypes pops.by.time
#  ##       148.28        10.26
#  
#  dim(r1$Genotypes)
#  ## [1] 10000  3887

## ----mc50000_1, echo = TRUE, eval = FALSE-------------------------
#  ng <- 50000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  
#  t_mc_50000_nmpg <- system.time(
#      mc_50000_nmpg <- oncoSimulPop(5,
#                                    u,
#                                    model = "McFL",
#                                    mu = 1e-7,
#                                    detectionSize = 1e6,
#                                    detectionDrivers = NA,
#                                    detectionProb = NA,
#                                    keepPhylog = TRUE,
#                                    onlyCancer = FALSE,
#                                    keepEvery = NA,
#                                    mutationPropGrowth = FALSE,
#                                    mc.cores = 1
#                                    ))
#  t_mc_50000_nmpg
#  ##   user  system elapsed
#  ##  30.46    0.54   31.01
#  
#  
#  summary(mc_50000_nmpg)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      1902      1002528       582752     284.2   31137
#  ## 2      2159      1002679       404858     274.8   36905
#  ## 3      2247      1002722       185678     334.5   42429
#  ## 4      2038      1009606       493574     218.4   32519
#  ## 5      2222      1004661       162628     291.0   38470
#  
#  print(object.size(mc_50000_nmpg), units = "MB")
#  ## 2057.6 Mb
#  

## ----mc50000_kp, echo = TRUE, eval = FALSE------------------------
#  t_mc_50000_nmpg_k <- system.time(
#      mc_50000_nmpg_k <- oncoSimulPop(5,
#                                      u,
#                                      model = "McFL",
#                                      mu = 1e-7,
#                                      detectionSize = 1e6,
#                                      detectionDrivers = NA,
#                                      detectionProb = NA,
#                                      keepPhylog = TRUE,
#                                      onlyCancer = FALSE,
#                                      keepEvery = 1,
#                                      mutationPropGrowth = FALSE,
#                                      mc.cores = 1
#                                      ))
#  
#  t_mc_50000_nmpg_k
#  ##    user  system elapsed
#  ##  30.000   1.712  31.714
#  
#  summary(mc_50000_nmpg_k)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      8779      1000223       136453     306.7   38102
#  ## 2      7442      1006563       428150     345.3   35139
#  ## 3      8710      1003509       224543     252.3   35659
#  ## 4      8554      1002537       103889     273.7   36783
#  ## 5      8233      1003171       263005     301.8   35236
#  
#  print(object.size(mc_50000_nmpg_k), units = "MB")
#  ## 8101.4 Mb

## ----mc50000_popx, echo = TRUE, eval = FALSE----------------------
#  ng <- 50000
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2),
#                                        rep(-0.1, ng/2)))
#  
#  t_mc_50000_nmpg_3e6 <- system.time(
#      mc_50000_nmpg_3e6 <- oncoSimulPop(5,
#                                        u,
#                                        model = "McFL",
#                                        mu = 1e-7,
#                                        detectionSize = 3e6,
#                                        detectionDrivers = NA,
#                                        detectionProb = NA,
#                                        keepPhylog = TRUE,
#                                        onlyCancer = FALSE,
#                                        keepEvery = NA,
#                                        mutationPropGrowth = FALSE,
#                                        mc.cores = 1
#                                        ))
#  t_mc_50000_nmpg_3e6
#  ##    user  system elapsed
#  ##  77.240   1.064  78.308
#  
#  summary(mc_50000_nmpg_3e6)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      5487      3019083       836793     304.5   65121
#  ## 2      4812      3011816       789146     286.3   53087
#  ## 3      4463      3016896      1970957     236.6   45918
#  ## 4      5045      3028142       956026     360.3   63464
#  ## 5      4791      3029720       916692     358.1   55012
#  
#  print(object.size(mc_50000_nmpg_3e6), units = "MB")
#  ## 4759.3 Mb
#  												

## ----mc50000_mux, echo = TRUE, eval = FALSE-----------------------
#  
#  t_mc_50000_nmpg_5mu <- system.time(
#      mc_50000_nmpg_5mu <- oncoSimulPop(5,
#                                        u,
#                                        model = "McFL",
#                                        mu = 5e-7,
#                                        detectionSize = 1e6,
#                                        detectionDrivers = NA,
#                                        detectionProb = NA,
#                                        keepPhylog = TRUE,
#                                        onlyCancer = FALSE,
#                                        keepEvery = NA,
#                                        mutationPropGrowth = FALSE,
#                                        mc.cores = 1
#                                        ))
#  
#  t_mc_50000_nmpg_5mu
#  ##    user  system elapsed
#  ## 167.332   1.796 169.167
#  
#  summary(mc_50000_nmpg_5mu)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      7963      1004415       408352     99.03   57548
#  ## 2      8905      1010751       120155    130.30   74738
#  ## 3      8194      1005465       274661     96.98   58546
#  ## 4      9053      1014049       119943    112.23   75379
#  ## 5      8982      1011817        95047     99.95   76757
#  
#  print(object.size(mc_50000_nmpg_5mu), units = "MB")
#  ## 8314.4 Mb

## ----mcf5muk, echo = TRUE, eval = FALSE---------------------------
#  t_mc_50000_nmpg_5mu_k <- system.time(
#      mc_50000_nmpg_5mu_k <- oncoSimulPop(5,
#                                          u,
#                                          model = "McFL",
#                                          mu = 5e-7,
#                                          detectionSize = 1e6,
#                                          detectionDrivers = NA,
#                                          detectionProb = NA,
#                                          keepPhylog = TRUE,
#                                          onlyCancer = FALSE,
#                                          keepEvery = 1,
#                                          mutationPropGrowth = FALSE,
#                                          mc.cores = 1
#                                          ))
#  												
#  t_mc_50000_nmpg_5mu_k
#  ##    user  system elapsed
#  ## 174.404   5.068 179.481
#  
#  summary(mc_50000_nmpg_5mu_k)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1     25294      1001597       102766     123.4   74524
#  ## 2     23766      1006679       223010     124.3   71808
#  ## 3     21755      1001379       203638     114.8   62609
#  ## 4     24889      1012103       161003     119.3   75031
#  ## 5     21844      1002927       255388     108.8   64556
#  
#  print(object.size(mc_50000_nmpg_5mu_k), units = "MB")
#  ## 22645.8 Mb
#  

## ----mc50000_2, echo = TRUE, eval = FALSE-------------------------
#  
#  t_mc_50000 <- system.time(
#      mc_50000 <- oncoSimulPop(5,
#                               u,
#                               model = "McFL",
#                               mu = 1e-7,
#                               detectionSize = 1e6,
#                               detectionDrivers = NA,
#                               detectionProb = NA,
#                               keepPhylog = TRUE,
#                               onlyCancer = FALSE,
#                               keepEvery = NA,
#                               mutationPropGrowth = TRUE,
#                               mc.cores = 1
#                               ))
#  
#  t_mc_50000
#  ##    user  system elapsed
#  ## 303.352   2.808 306.223
#  
#  summary(mc_50000)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1     13928      1010815       219814     210.9   91255
#  ## 2     12243      1003267       214189     178.1   67673
#  ## 3     13880      1014131       124354     161.4   88322
#  ## 4     14104      1012941        75521     205.7   98583
#  ## 5     12428      1005594       232603     167.4   70359
#  
#  print(object.size(mc_50000), units = "MB")
#  ## 12816.6 Mb
#  

## ----mcf5muk005, echo = TRUE, eval = FALSE------------------------
#  t_mc_50000_nmpg_5mu_k <- system.time(
#      mc_50000_nmpg_5mu_k <- oncoSimulPop(2,
#                                          u,
#                                          model = "McFL",
#                                          mu = 5e-7,
#                                          detectionSize = 1e6,
#                                          detectionDrivers = NA,
#                                          detectionProb = NA,
#                                          keepPhylog = TRUE,
#                                          onlyCancer = FALSE,
#                                          keepEvery = 1,
#                                          mutationPropGrowth = FALSE,
#                                          mc.cores = 1
#                                          ))
#  t_mc_50000_nmpg_5mu_k
#  ##    user  system elapsed
#  ## 305.512   5.164 310.711
#  
#  summary(mc_50000_nmpg_5mu_k)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1     61737      1003273       104460  295.8731  204214
#  ## 2     65072      1000540       133068  296.6243  210231
#  
#  print(object.size(mc_50000_nmpg_5mu_k), units = "MB")
#  ## 24663.6 Mb
#  

## ----mc50000_1_005, echo = TRUE, eval = FALSE---------------------
#  t_mc_50000_nmpg <- system.time(
#      mc_50000_nmpg <- oncoSimulPop(5,
#                                    u,
#                                    model = "McFL",
#                                    mu = 1e-7,
#                                    detectionSize = 1e6,
#                                    detectionDrivers = NA,
#                                    detectionProb = NA,
#                                    keepPhylog = TRUE,
#                                    onlyCancer = FALSE,
#                                    keepEvery = NA,
#                                    mutationPropGrowth = FALSE,
#                                    mc.cores = 1
#                                    ))
#  t_mc_50000_nmpg
#  ##    user  system elapsed
#  ## 111.236   0.596 111.834
#  
#  summary(mc_50000_nmpg)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      2646      1000700       217188   734.475  108566
#  ## 2      2581      1001626       209873   806.500  107296
#  ## 3      2903      1001409       125148   841.700  120859
#  ## 4      2310      1000146       473948   906.300   91519
#  ## 5      2704      1001290       448409   838.800  103556
#  
#  print(object.size(mc_50000_nmpg), units = "MB")
#  ## 2638.3 Mb
#  

## ----filog_exp50000_1, echo = TRUE, eval = FALSE------------------
#  head(e_50000[[1]]$other$PhylogDF)
#  ##   parent child   time
#  ## 1         3679 0.8402
#  ## 2         4754 1.1815
#  ## 3        20617 1.4543
#  ## 4        15482 2.3064
#  ## 5         4431 3.7130
#  ## 6        41915 4.0628
#  
#  tail(e_50000[[1]]$other$PhylogDF)
#  ##                          parent                            child time
#  ## 20672               3679, 20282               3679, 20282, 22359 75.0
#  ## 20673        3679, 17922, 22346        3679, 17922, 22346, 35811 75.0
#  ## 20674                2142, 3679                2142, 3679, 25838 75.0
#  ## 20675        3679, 17922, 19561        3679, 17922, 19561, 43777 75.0
#  ## 20676 3679, 15928, 19190, 20282 3679, 15928, 19190, 20282, 49686 75.0
#  ## 20677         2142, 3679, 16275         2142, 3679, 16275, 24201 75.0

## ----noplotlconephylog, echo = TRUE, eval = FALSE-----------------
#  plotClonePhylog(e_50000[[1]]) ## plot not shown
#  

## ----ex-large-pop-size, eval = FALSE, echo = TRUE-----------------
#  ng <- 50
#  u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng)))

## ----ex-large-mf, eval = FALSE, echo = TRUE-----------------------
#  t_mc_k_50_1e11 <- system.time(
#      mc_k_50_1e11 <- oncoSimulPop(5,
#                                   u,
#                                   model = "McFL",
#                                   mu = 1e-7,
#                                   detectionSize = 1e11,
#                                   initSize = 1e5,
#                                   detectionDrivers = NA,
#                                   detectionProb = NA,
#                                   keepPhylog = TRUE,
#                                   onlyCancer = FALSE,
#                                   mutationPropGrowth = FALSE,
#                                   keepEvery = 1,
#                                   finalTime = 5000,
#                                   mc.cores = 1,
#                                   max.wall.time = 600
#                                   ))
#  
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  
#  t_mc_k_50_1e11
#  ## user  system elapsed
#  ## 613.612   0.040 613.664
#  
#  summary(mc_k_50_1e11)[, c(1:3, 8, 9)]
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      5491 100328847809  44397848771  1019.950  942764
#  ## 2      3194 100048090441  34834178374   789.675  888819
#  ## 3      5745 100054219162  24412502660   927.950  929231
#  ## 4      4017 101641197799  60932177160   750.725  480938
#  ## 5      5393 100168156804  41659212367   846.250  898245
#  
#  ## print(object.size(mc_k_50_1e11), units = "MB")
#  ## 177.8 Mb
#  

## ----ex-large-exp, eval = FALSE, echo = TRUE----------------------
#  t_exp_k_50_1e11 <- system.time(
#      exp_k_50_1e11 <- oncoSimulPop(5,
#                                    u,
#                                    model = "Exp",
#                                    mu = 1e-7,
#                                    detectionSize = 1e11,
#                                    initSize = 1e5,
#                                    detectionDrivers = NA,
#                                    detectionProb = NA,
#                                    keepPhylog = TRUE,
#                                    onlyCancer = FALSE,
#                                    mutationPropGrowth = FALSE,
#                                    keepEvery = 1,
#                                    finalTime = 5000,
#                                    mc.cores = 1,
#                                    max.wall.time = 600,
#                                    errorHitWallTime = FALSE,
#                                    errorHitMaxTries = FALSE
#                                    ))
#  
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Hitted wall time. Exiting.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Hitted wall time. Exiting.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Recoverable exception ti set to DBL_MIN. Rerunning.
#  ## Hitted wall time. Exiting.
#  ## Hitted wall time. Exiting.
#  
#  t_exp_k_50_1e11
#  ##     user   system  elapsed
#  ## 2959.068    0.128 2959.556
#  try(summary(exp_k_50_1e11)[, c(1:3, 8, 9)])
#  ##   NumClones TotalPopSize LargestClone FinalTime NumIter
#  ## 1      6078  65172752616  16529682757  235.7590 1883438
#  ## 2      5370 106476643712  24662446729  232.0000 2516675
#  ## 3      2711  21911284363  17945303353  224.8608  543698
#  ## 4      2838  13241462284   2944300245  216.8091  372298
#  ## 5      7289  76166784312  10941729810  240.0217 1999489
#  
#  print(object.size(exp_k_50_1e11), units = "MB")
#  ## 53.5 Mb
#  

## ----n73----------------------------------------------------------
m4 <- data.frame(G = c("WT", "A", "B", "A, B"), F = c(1, 2, 3, 4))

## ----n74----------------------------------------------------------
fem4 <- allFitnessEffects(genotFitness = m4)

## ----n75----------------------------------------------------------
try(plot(fem4))

## ----n76, fig.width=6.5, fig.height = 6.5-------------------------
plotFitnessLandscape(evalAllGenotypes(fem4))

## ----n78----------------------------------------------------------
evalAllGenotypes(fem4, addwt = TRUE)

## ----n79----------------------------------------------------------
plotFitnessLandscape(evalAllGenotypes(fem4))

## ----n80----------------------------------------------------------
m6 <- cbind(c(1, 1), c(1, 0), c(2, 3))
fem6 <- allFitnessEffects(genotFitness = m6)
evalAllGenotypes(fem6, addwt = TRUE)
## plot(fem6)

## ----n81, fig.width=6.5, fig.height = 6.5-------------------------
plotFitnessLandscape(evalAllGenotypes(fem6))

## ----rj68v3x------------------------------------------------------
r <- data.frame(Genotype = c("WT", "A", "B", "A, B"), 
                 Fitness = c("10 * f_", 
                             "10 * f_1", 
                             "50 * f_2", 
                             "200 * (f_1 + f_2) + 50 * f_1_2"))

afe <- allFitnessEffects(genotFitness = r, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

plotFitnessLandscape(evalAllGenotypes(afe,
                                      spPopSizes = c(WT = 2500, A = 2000,
                                                     B = 5500, "A, B" = 700)))


## ----j8hhkiu------------------------------------------------------
r <- data.frame(Genotype = c("WT", "A", "B", "A, B"), 
                 Fitness = c("10 * f_", 
                             "10 * f_A", 
                             "50 * f_B", 
                             "200 * (f_A + f_B) + 50 * f_A_B"))


## ----mcflparam----------------------------------------------------
sp <- 1e-3 
spp <- -sp/(1 + sp)

## ----nx82---------------------------------------------------------

ai1 <- evalAllGenotypes(allFitnessEffects(
    noIntGenes = c(0.05, -.2, .1), frequencyDependentFitness = FALSE), order = FALSE)

## ----nx83---------------------------------------------------------
ai1

## ----nx84---------------------------------------------------------
all(ai1[, "Fitness"]  == c( (1 + .05), (1 - .2), (1 + .1),
       (1 + .05) * (1 - .2),
       (1 + .05) * (1 + .1),
       (1 - .2) * (1 + .1),
       (1 + .05) * (1 - .2) * (1 + .1)))


## ----nx85---------------------------------------------------------
(ai2 <- evalAllGenotypes(allFitnessEffects(
    noIntGenes = c(0.05, -.2, .1)), order = TRUE,
    addwt = TRUE))


## ----nx091, fig.height=4------------------------------------------
data(examplesFitnessEffects)
plot(examplesFitnessEffects[["cbn1"]])

## ----nx092--------------------------------------------------------

cs <-  data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = 0.1,
                 sh = -0.9,
                 typeDep = "MN")

cbn1 <- allFitnessEffects(cs)


## ----nx093, fig.height=3------------------------------------------
plot(cbn1)

## ----nx094, fig.height=5------------------------------------------
plot(cbn1, "igraph")

## ----nx095, fig.height=5------------------------------------------
library(igraph) ## to make the reingold.tilford layout available
plot(cbn1, "igraph", layout = layout.reingold.tilford)

## ----nx096--------------------------------------------------------
gfs <- evalAllGenotypes(cbn1, order = FALSE, addwt = TRUE)

gfs[1:15, ]

## ----nx097--------------------------------------------------------

c1 <- data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, rep(0.2, 3)),
                 sh = c(rep(0, 4), c(-.1, -.2), c(-.05, -.06, -.07)),
                 typeDep = "MN")

try(fc1 <- allFitnessEffects(c1))


## ----nx098--------------------------------------------------------
c1 <- data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, rep(0.2, 3)),
                 sh = c(rep(0, 4), c(-.9, -.9), rep(-.95, 3)),
                 typeDep = "MN")

cbn2 <- allFitnessEffects(c1)


## ----nx099--------------------------------------------------------
gcbn2 <- evalAllGenotypes(cbn2, order = FALSE)
gcbn2[1:10, ]

## ----nx0100-------------------------------------------------------
gcbn2o <- evalAllGenotypes(cbn2, order = TRUE, max = 1956)
gcbn2o[1:10, ]

## ----nx0101-------------------------------------------------------
all.equal(
        gcbn2[c(1:21, 22, 28, 41, 44, 56, 63 ) , "Fitness"],
        c(1.01, 1.02, 0.1, 1.03, 1.04, 0.05,
          1.01 * c(1.02, 0.1, 1.03, 1.04, 0.05),
          1.02 * c(0.10, 1.03, 1.04, 0.05),
          0.1 * c(1.03, 1.04, 0.05),
          1.03 * c(1.04, 0.05),
          1.04 * 0.05,
          1.01 * 1.02 * 1.1,
          1.01 * 0.1 * 0.05,
          1.03 * 1.04 * 0.05,
          1.01 * 1.02 * 1.1 * 0.05,
          1.03 * 1.04 * 1.2 * 0.1, ## notice this
          1.01 * 1.02 * 1.03 * 1.04 * 1.1 * 1.2
          ))

## ----nx0102-------------------------------------------------------
gcbn2[56, ] 
all.equal(gcbn2[56, "Fitness"], 1.03 * 1.04 * 1.2 * 0.10)

## ----nx0103-------------------------------------------------------

s1 <- data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, rep(0.2, 3)),
                 sh = c(rep(0, 4), c(-.9, -.9), rep(-.95, 3)),
                 typeDep = "SM")

smn1 <- allFitnessEffects(s1)


## ----nx0104, fig.height=3-----------------------------------------
plot(smn1)

## ----nx0105-------------------------------------------------------
gsmn1 <- evalAllGenotypes(smn1, order = FALSE)


## ----nx0106-------------------------------------------------------
gcbn2[c(8, 12, 22), ]
gsmn1[c(8, 12, 22), ]

gcbn2[c(20:21, 28), ]
gsmn1[c(20:21, 28), ]

## ----nx0107-------------------------------------------------------

x1 <- data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, rep(0.2, 3)),
                 sh = c(rep(0, 4), c(-.9, -.9), rep(-.95, 3)),
                 typeDep = "XMPN")

xor1 <- allFitnessEffects(x1)


## ----nx0108, fig.height=3-----------------------------------------
plot(xor1)

## ----nx0109-------------------------------------------------------

gxor1 <- evalAllGenotypes(xor1, order = FALSE)


## ----nx0110-------------------------------------------------------
gxor1[c(22, 41), ] 
c(1.01 * 1.02 * 0.1, 1.03 * 1.04 * 0.05)

## ----nx0111-------------------------------------------------------
gxor1[28, ] 
1.01 * 1.1 * 1.2

## ----nx0112-------------------------------------------------------
gxor1[44, ] 
1.01 * 1.02 * 0.1 * 1.2

## ----nx0113-------------------------------------------------------

p3 <- data.frame(
    parent = c(rep("Root", 4), "a", "b", "d", "e", "c", "f"),
    child = c("a", "b", "d", "e", "c", "c", "f", "f", "g", "g"),
    s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
    sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
    typeDep = c(rep("--", 4), 
                "XMPN", "XMPN", "MN", "MN", "SM", "SM"))
fp3 <- allFitnessEffects(p3)

## ----nx0114, fig.height=3-----------------------------------------
plot(fp3)

## ----nx0115, fig.height=6-----------------------------------------
plot(fp3, "igraph", layout.reingold.tilford)

## ----nx0116-------------------------------------------------------

gfp3 <- evalAllGenotypes(fp3, order = FALSE)


## ----nx0117-------------------------------------------------------
gfp3[c(9, 24, 29, 59, 60, 66, 119, 120, 126, 127), ]

c(1.01 * 1.1, 1.03 * .05, 1.01 * 1.02 * 0.1, 0.1 * 0.05 * 1.3,
  1.03 * 1.04 * 1.2, 1.01 * 1.02 * 0.1 * 0.05,
  0.1 * 1.03 * 1.04 * 1.2 * 1.3,
  1.01 * 1.02 * 0.1 * 1.03 * 1.04 * 1.2,
  1.02 * 1.1 * 1.03 * 1.04 * 1.2 * 1.3,
  1.01 * 1.02 * 1.03 * 1.04 * 0.1 * 1.2 * 1.3)


## ----ny0111-------------------------------------------------------
s <- 0.2
sboth <- (1/(1 + s)) - 1
m0 <- allFitnessEffects(data.frame(
    parent = c("Root", "Root", "a1", "a2"),
    child = c("a1", "a2", "b", "b"),
    s = s,
    sh = -1,
    typeDep = "OR"),
                        epistasis = c("a1:a2" = sboth))
evalAllGenotypes(m0, order = FALSE, addwt = TRUE)

## ----ny01112------------------------------------------------------
s <- 0.2
m1 <- allFitnessEffects(data.frame(
    parent = c("Root", "A"),
    child = c("A", "B"),
    s = s,
    sh = -1,
    typeDep = "OR"),
                        geneToModule = c("Root" = "Root",
                                         "A" = "a1, a2",
                                         "B" = "b1"))
evalAllGenotypes(m1, order = FALSE, addwt = TRUE)

## ----ny0113-------------------------------------------------------
fnme <- allFitnessEffects(epistasis = c("A" = 0.1,
                                        "B" = 0.2),
                          geneToModule = c("A" = "a1, a2",
                                           "B" = "b1"))
evalAllGenotypes(fnme, order = FALSE, addwt = TRUE)

## ----ny0114-------------------------------------------------------
fnme2 <- allFitnessEffects(epistasis = c("A" = 0.1,
                                        "B" = 0.2),
                          geneToModule = c(
                              "Root" = "Root",
                              "A" = "a1, a2",
                              "B" = "b1"))
evalAllGenotypes(fnme, order = FALSE, addwt = TRUE)

## ----ny0115-------------------------------------------------------
p4 <- data.frame(
    parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
    child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
    s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
    sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
    typeDep = c(rep("--", 4), 
                "XMPN", "XMPN", "MN", "MN", "SM", "SM"))

fp4m <- allFitnessEffects(
    p4,
    geneToModule = c("Root" = "Root", "A" = "a1",
                     "B" = "b1, b2", "C" = "c1",
                     "D" = "d1, d2", "E" = "e1",
                     "F" = "f1, f2", "G" = "g1"))

## ----ny0116, fig.height=3-----------------------------------------
plot(fp4m)

## ----ny0117, fig.height=3-----------------------------------------
plot(fp4m, expandModules = TRUE)

## ----ny0118, fig.height=6-----------------------------------------
plot(fp4m, "igraph", layout = layout.reingold.tilford, 
     expandModules = TRUE)


## ----ny0119-------------------------------------------------------
gfp4 <- evalAllGenotypes(fp4m, order = FALSE, max = 1024)

## ----ny0120-------------------------------------------------------
gfp4[c(12, 20, 21, 40, 41, 46, 50, 55, 64, 92,
       155, 157, 163, 372, 632, 828), ]

c(1.01 * 1.02, 1.02, 1.02 * 1.1, 0.1 * 1.3, 1.03, 
  1.03 * 1.04, 1.04 * 0.05, 0.05 * 1.3,  
  1.01 * 1.02 * 0.1, 1.02 * 1.1, 0.1 * 0.05 * 1.3,
  1.03 * 0.05, 1.03 * 0.05, 1.03 * 1.04 * 1.2, 1.03 * 1.04 * 1.2, 
  1.02 * 1.1 * 1.03 * 1.04 * 1.2 * 1.3)


## ----ny0121-------------------------------------------------------
o3 <- allFitnessEffects(orderEffects = c(
                            "F > D > M" = -0.3,
                            "D > F > M" = 0.4,
                            "D > M > F" = 0.2,
                            "D > M"     = 0.1,
                            "M > D"     = 0.5),
                        geneToModule =
                            c("M" = "m",
                              "F" = "f",
                              "D" = "d") )


(ag <- evalAllGenotypes(o3, addwt = TRUE, order = TRUE))

## ----ny0123-------------------------------------------------------

ofe1 <- allFitnessEffects(
    orderEffects = c("F > D" = -0.3, "D > F" = 0.4),
    geneToModule =
        c("F" = "f1, f2",
          "D" = "d1, d2") )

ag <- evalAllGenotypes(ofe1, order = TRUE)


## ----ny0124-------------------------------------------------------
ag[5:16,]

## ----ny0125-------------------------------------------------------
ag[c(17, 39, 19, 29), ]

## ----ny0126-------------------------------------------------------
ag[c(17, 39, 19, 29), "Fitness"] == c(1.4, 0.7, 1.4, 0.7)

## ----ny0127-------------------------------------------------------
ag[c(43, 44),]
ag[c(43, 44), "Fitness"] == c(1.4, 1.4)

## ----ny0128-------------------------------------------------------
all(ag[41:52, "Fitness"] == 1.4)

## ----ny0129-------------------------------------------------------
all(ag[53:64, "Fitness"] == 0.7)

## ----ny0130-------------------------------------------------------

ofe2 <- allFitnessEffects(
    orderEffects = c("F > D" = -0.3, "D > F" = 0.4),
    geneToModule =
        c("F" = "f1, f2, f3",
          "D" = "d1, d2") )
ag2 <- evalAllGenotypes(ofe2, max = 325, order = TRUE)


## ----ny0131-------------------------------------------------------
all(ag2[grep("^d.*f.*", ag2[, 1]), "Fitness"] == 1.4)
all(ag2[grep("^f.*d.*", ag2[, 1]), "Fitness"] == 0.7)
oe <- c(grep("^f.*d.*", ag2[, 1]), grep("^d.*f.*", ag2[, 1]))
all(ag2[-oe, "Fitness"] == 1)

## ----ny0132-------------------------------------------------------

foi1 <- allFitnessEffects(
    orderEffects = c("D>B" = -0.2, "B > D" = 0.3),
    noIntGenes = c("A" = 0.05, "C" = -.2, "E" = .1))


## ----ny0133-------------------------------------------------------
foi1[c("geneModule", "long.geneNoInt")]

## ----ny0134-------------------------------------------------------
agoi1 <- evalAllGenotypes(foi1,  max = 325, order = TRUE)
head(agoi1)

## ----ny0135-------------------------------------------------------
rn <- 1:nrow(agoi1)
names(rn) <- agoi1[, 1]

agoi1[rn[LETTERS[1:5]], "Fitness"] == c(1.05, 1, 0.8, 1, 1.1)


## ----ny0136-------------------------------------------------------
agoi1[grep("^A > [BD]$", names(rn)), "Fitness"] == 1.05
agoi1[grep("^C > [BD]$", names(rn)), "Fitness"] == 0.8
agoi1[grep("^E > [BD]$", names(rn)), "Fitness"] == 1.1
agoi1[grep("^[BD] > A$", names(rn)), "Fitness"] == 1.05
agoi1[grep("^[BD] > C$", names(rn)), "Fitness"] == 0.8
agoi1[grep("^[BD] > E$", names(rn)), "Fitness"] == 1.1

## ----ny0137-------------------------------------------------------
all.equal(agoi1[230:253, "Fitness"] ,
          rep((1 - 0.2) * 1.05 * 0.8 * 1.1, 24))

## ----ny0138-------------------------------------------------------
sa <- 0.2
sb <- 0.3
sab <- 0.7

e2 <- allFitnessEffects(epistasis =
                            c("A: -B" = sa,
                              "-A:B" = sb,
                              "A : B" = sab))
evalAllGenotypes(e2, order = FALSE, addwt = TRUE)


## ----ny0139-------------------------------------------------------
s2 <- ((1 + sab)/((1 + sa) * (1 + sb))) - 1

e3 <- allFitnessEffects(epistasis =
                            c("A" = sa,
                              "B" = sb,
                              "A : B" = s2))
evalAllGenotypes(e3, order = FALSE, addwt = TRUE)


## ----ny0140-------------------------------------------------------
sa <- 0.1
sb <- 0.15
sc <- 0.2
sab <- 0.3
sbc <- -0.25
sabc <- 0.4

sac <- (1 + sa) * (1 + sc) - 1

E3A <- allFitnessEffects(epistasis =
                            c("A:-B:-C" = sa,
                              "-A:B:-C" = sb,
                              "-A:-B:C" = sc,
                              "A:B:-C" = sab,
                              "-A:B:C" = sbc,
                              "A:-B:C" = sac,
                              "A : B : C" = sabc)
                                                )

evalAllGenotypes(E3A, order = FALSE, addwt = FALSE)


## ----ny0141-------------------------------------------------------

sa <- 0.1
sb <- 0.15
sc <- 0.2
sab <- 0.3
Sab <- ( (1 + sab)/((1 + sa) * (1 + sb))) - 1
Sbc <- ( (1 + sbc)/((1 + sb) * (1 + sc))) - 1
Sabc <- ( (1 + sabc)/
          ( (1 + sa) * (1 + sb) * (1 + sc) *
            (1 + Sab) * (1 + Sbc) ) ) - 1

E3B <- allFitnessEffects(epistasis =
                             c("A" = sa,
                               "B" = sb,
                               "C" = sc,
                               "A:B" = Sab,
                               "B:C" = Sbc,
                               ## "A:C" = sac, ## not needed now
                               "A : B : C" = Sabc)
                                                )
evalAllGenotypes(E3B, order = FALSE, addwt = FALSE)


## ----ny0142-------------------------------------------------------
all(evalAllGenotypes(E3A, order = FALSE, addwt = FALSE) == 
    evalAllGenotypes(E3B, order = FALSE, addwt = FALSE))

## ----ny0143-------------------------------------------------------

sa <- 0.2
sb <- 0.3
sab <- 0.7

em <- allFitnessEffects(epistasis =
                            c("A: -B" = sa,
                              "-A:B" = sb,
                              "A : B" = sab),
                        geneToModule = c("A" = "a1, a2",
                                         "B" = "b1, b2"))
evalAllGenotypes(em, order = FALSE, addwt = TRUE)

## ----ny0144-------------------------------------------------------
s2 <- ((1 + sab)/((1 + sa) * (1 + sb))) - 1

em2 <- allFitnessEffects(epistasis =
                            c("A" = sa,
                              "B" = sb,
                              "A : B" = s2),
                         geneToModule = c("A" = "a1, a2",
                                         "B" = "b1, b2")
                         )
evalAllGenotypes(em2, order = FALSE, addwt = TRUE)


## ----ny0145-------------------------------------------------------

fnme <- allFitnessEffects(epistasis = c("A" = 0.1,
                                        "B" = 0.2),
                          geneToModule = c("A" = "a1, a2",
                                           "B" = "b1, b2, b3"))

evalAllGenotypes(fnme, order = FALSE, addwt = TRUE)


## ----ny0146-------------------------------------------------------
fnme <- allFitnessEffects(epistasis = c("A" = 0.1,
                                        "B" = 0.2,
                                        "A : B" = 0.0),
                          geneToModule = c("A" = "a1, a2",
                                           "B" = "b1, b2, b3"))

evalAllGenotypes(fnme, order = FALSE, addwt = TRUE)


## ----ny0147-------------------------------------------------------
s <- 0.2
sv <- allFitnessEffects(epistasis = c("-A : B" = -1,
                                      "A : -B" = -1,
                                      "A:B" = s))

## ----ny0148-------------------------------------------------------
(asv <- evalAllGenotypes(sv, order = FALSE, addwt = TRUE))

## ----ny01149------------------------------------------------------
evalAllGenotypes(sv, order = TRUE, addwt = TRUE)

## ----ny0150-------------------------------------------------------
sa <- -0.1
sb <- -0.2
sab <- 0.25
sv2 <- allFitnessEffects(epistasis = c("-A : B" = sb,
                             "A : -B" = sa,
                             "A:B" = sab),
                         geneToModule = c(
                             "A" = "a1, a2",
                             "B" = "b"))
evalAllGenotypes(sv2, order = FALSE, addwt = TRUE)

## ----ny0151-------------------------------------------------------
evalAllGenotypes(sv2, order = TRUE, addwt = TRUE)

## ----ny0152-------------------------------------------------------
sa <- 0.1
sb <- 0.2
sab <- -0.8
sm1 <- allFitnessEffects(epistasis = c("-A : B" = sb,
                             "A : -B" = sa,
                             "A:B" = sab))
evalAllGenotypes(sm1, order = FALSE, addwt = TRUE)


## ----ny0153-------------------------------------------------------
evalAllGenotypes(sm1, order = TRUE, addwt = TRUE)

## ----ny0154-------------------------------------------------------
evalAllGenotypes(sv, order = FALSE, addwt = TRUE, model = "Bozic")

## ----ny0155-------------------------------------------------------
s <- 0.2
svB <- allFitnessEffects(epistasis = c("-A : B" = -Inf,
                                      "A : -B" = -Inf,
                                      "A:B" = s))
evalAllGenotypes(svB, order = FALSE, addwt = TRUE, model = "Bozic")

## ----ny0156-------------------------------------------------------

s <- 1
svB1 <- allFitnessEffects(epistasis = c("-A : B" = -Inf,
                                       "A : -B" = -Inf,
                                       "A:B" = s))

evalAllGenotypes(svB1, order = FALSE, addwt = TRUE, model = "Bozic")


s <- 3
svB3 <- allFitnessEffects(epistasis = c("-A : B" = -Inf,
                                       "A : -B" = -Inf,
                                       "A:B" = s))

evalAllGenotypes(svB3, order = FALSE, addwt = TRUE, model = "Bozic")



## ----ny0157-------------------------------------------------------
i1 <- allFitnessEffects(noIntGenes = c(1, 0.5))
evalAllGenotypes(i1, order = FALSE, addwt = TRUE, 
                 model = "Bozic")
				 
i1_b <- oncoSimulIndiv(i1, model = "Bozic", onlyCancer = TRUE)


## ----ny0158-------------------------------------------------------
evalAllGenotypes(i1, order = FALSE, addwt = TRUE, 
                 model = "Exp")
i1_e <- oncoSimulIndiv(i1, model = "Exp", onlyCancer = TRUE)
summary(i1_e)

## ----ny0159-------------------------------------------------------
p4 <- data.frame(
    parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
    child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
    s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
    sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
    typeDep = c(rep("--", 4), 
                "XMPN", "XMPN", "MN", "MN", "SM", "SM"))

oe <- c("C > F" = -0.1, "H > I" = 0.12)
sm <- c("I:J"  = -1)
sv <- c("-K:M" = -.5, "K:-M" = -.5)
epist <- c(sm, sv)

modules <- c("Root" = "Root", "A" = "a1",
             "B" = "b1, b2", "C" = "c1",
             "D" = "d1, d2", "E" = "e1",
             "F" = "f1, f2", "G" = "g1",
             "H" = "h1, h2", "I" = "i1",
             "J" = "j1, j2", "K" = "k1, k2", "M" = "m1")

set.seed(1) ## for reproducibility
noint <- rexp(5, 10)
names(noint) <- paste0("n", 1:5)

fea <- allFitnessEffects(rT = p4, epistasis = epist,
                         orderEffects = oe,
                         noIntGenes = noint,
                         geneToModule = modules)

## ----ny0160, fig.height=5.5---------------------------------------
plot(fea)

## ----ny0161, fig.height=5.5---------------------------------------
plot(fea, "igraph")

## ----ny0162, fig.height=5.5---------------------------------------
plot(fea, expandModules = TRUE)

## ----ny0163, fig.height=6.5---------------------------------------
plot(fea, "igraph", expandModules = TRUE)

## ----nyv0100------------------------------------------------------

evalGenotype("k1 > i1 > h2", fea) ## 0.5
evalGenotype("k1 > h1 > i1", fea) ## 0.5 * 1.12

evalGenotype("k2 > m1 > h1 > i1", fea) ## 1.12

evalGenotype("k2 > m1 > h1 > i1 > c1 > n3 > f2", fea) 
## 1.12 * 0.1 * (1 + noint[3]) * 0.05 * 0.9


## ----nyv0101------------------------------------------------------

randomGenotype <- function(fe, ns = NULL) {
    gn <- setdiff(c(fe$geneModule$Gene,
                    fe$long.geneNoInt$Gene), "Root")
    if(is.null(ns)) ns <- sample(length(gn), 1)
    return(paste(sample(gn, ns), collapse = " > "))
}

set.seed(2) ## for reproducibility

evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  k2 > i1 > c1 > n1 > m1
##  Individual s terms are : 0.0755182 -0.9
##  Fitness:  0.107552 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  n2 > h1 > h2
##  Individual s terms are : 0.118164
##  Fitness:  1.11816 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  d2 > k2 > c1 > f2 > n4 > m1 > n3 > f1 > b1 > g1 > n5 > h1 > j2
##  Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 0.03 -0.95 0.3 -0.1
##  Fitness:  0.0725829 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  h2 > c1 > f1 > n2 > b2 > a1 > n1 > i1
##  Individual s terms are : 0.0755182 0.118164 0.01 0.02 -0.9 -0.95 -0.1 0.12
##  Fitness:  0.00624418 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  h2 > j1 > m1 > d2 > i1 > b2 > k2 > d1 > b1 > n3 > n1 > g1 > h1 > c1 > k1 > e1 > a1 > f1 > n5 > f2
##  Individual s terms are : 0.0755182 0.0145707 0.0436069 0.01 0.02 -0.9 0.03 0.04 0.2 0.3 -1 -0.1 0.12
##  Fitness:  0 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  n1 > m1 > n3 > i1 > j1 > n5 > k1
##  Individual s terms are : 0.0755182 0.0145707 0.0436069 -1
##  Fitness:  0 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  d2 > n1 > g1 > f1 > f2 > c1 > b1 > d1 > k1 > a1 > b2 > i1 > n4 > h2 > n2
##  Individual s terms are : 0.0755182 0.118164 0.0139795 0.01 0.02 -0.9 0.03 -0.95 0.3 -0.5
##  Fitness:  0.00420528 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  j1 > f1 > j2 > a1 > n4 > c1 > n3 > k1 > d1 > h1
##  Individual s terms are : 0.0145707 0.0139795 0.01 0.1 0.03 -0.95 -0.5
##  Fitness:  0.0294308 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  n5 > f2 > f1 > h2 > n4 > c1 > n3 > b1
##  Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 -0.95
##  Fitness:  0.0602298 
evalGenotype(randomGenotype(fea), fea, echo = TRUE, verbose = TRUE)
## Genotype:  h1 > d1 > f2
##  Individual s terms are : 0.03 -0.95
##  Fitness:  0.0515 



## ----nyv0102------------------------------------------------------

muvar2 <- c("U" = 1e-6, "z" = 5e-5, "e" = 5e-4, "m" = 5e-3,
            "D" = 1e-4)
ni1 <- rep(0, 5)
names(ni1) <- names(muvar2) ## We use the same names, of course
fe1 <- allFitnessEffects(noIntGenes = ni1)
bb <- oncoSimulIndiv(fe1, 
                     mu = muvar2, onlyCancer = FALSE,
                     initSize = 1e5,
                     finalTime = 25,
                     seed =NULL)


## ----nyv0103------------------------------------------------------
fe2 <- allFitnessEffects(noIntGenes =
                         c(a1 = 0.1, a2 = 0.2,
                           b1 = 0.01, b2 = 0.3, b3 = 0.2,
                           c1 = 0.3, c2 = -0.2))

fm2 <- allMutatorEffects(epistasis = c("A" = 5,
                                       "B" = 10,
                                       "C" = 3),
                         geneToModule = c("A" = "a1, a2",
                                          "B" = "b1, b2, b3",
                                          "C" = "c1, c2"))

## Show the fitness effect of a specific genotype
evalGenotype("a1, c2", fe2, verbose = TRUE)

## Show the mutator effect of a specific genotype
evalGenotypeMut("a1, c2", fm2, verbose = TRUE)

## Fitness and mutator of a specific genotype
evalGenotypeFitAndMut("a1, c2", fe2, fm2, verbose = TRUE)

## ----rzx033, eval=FALSE-------------------------------------------
#  ## Show only all the fitness effects
#  evalAllGenotypes(fe2, order = FALSE)
#  
#  ## Show only all mutator effects
#  evalAllGenotypesMut(fm2)
#  
#  ## Show all fitness and mutator
#  evalAllGenotypesFitAndMut(fe2, fm2, order = FALSE)

## ----nyv0105------------------------------------------------------

set.seed(1) ## for reproducibility
## 17 genes, 7 with no direct fitness effects
ni <- c(rep(0, 7), runif(10, min = -0.01, max = 0.1))
names(ni) <- c("a1", "a2", "b1", "b2", "b3", "c1", "c2",
               paste0("g", 1:10))

fe3 <- allFitnessEffects(noIntGenes = ni)

fm3 <- allMutatorEffects(epistasis = c("A" = 5,
                                       "B" = 10,
                                       "C" = 3,
                                       "A:C" = 70),
                         geneToModule = c("A" = "a1, a2",
                                          "B" = "b1, b2, b3",
                                          "C" = "c1, c2"))

## ----nyv0106------------------------------------------------------
## These only affect mutation, not fitness
evalGenotypeFitAndMut("a1, a2", fe3, fm3, verbose = TRUE)
evalGenotypeFitAndMut("a1, b3", fe3, fm3, verbose = TRUE)

## These only affect fitness: the mutator multiplier is 1
evalGenotypeFitAndMut("g1", fe3, fm3, verbose = TRUE)                      
evalGenotypeFitAndMut("g3, g9", fe3, fm3, verbose = TRUE)

## These affect both
evalGenotypeFitAndMut("g3, g9, a2, b3", fe3, fm3, verbose = TRUE)

## ----nyv0107------------------------------------------------------
set.seed(1) ## so that it is easy to reproduce
mue1 <- oncoSimulIndiv(fe3, muEF = fm3, 
                       mu = 1e-6,
                       initSize = 1e5,
                       model = "McFL",
                       detectionSize = 5e6,
                       finalTime = 500,
                       onlyCancer = FALSE)

## ----rzx0321, eval=FALSE------------------------------------------
#  ## We do not show this in the vignette to avoid cluttering it
#  ## with output
#  mue1

## ----rzx0111, eval=FALSE------------------------------------------
#  
#  set.seed(1) ## for reproducibility
#  ## 17 genes, 7 with no direct fitness effects
#  ni <- c(rep(0, 7), runif(10, min = -0.01, max = 0.1))
#  names(ni) <- c("a1", "a2", "b1", "b2", "b3", "c1", "c2",
#                 paste0("g", 1:10))
#  
#  ## Next is for nicer figure labeling.
#  ## Consider as drivers genes with s >0
#  gp <- which(ni > 0)
#  
#  fe3 <- allFitnessEffects(noIntGenes = ni,
#                           drvNames = names(ni)[gp])
#  
#  
#  set.seed(12)
#  mue1 <- oncoSimulIndiv(fe3, muEF = fm3,
#                         mu = 1e-6,
#                         initSize = 1e5,
#                         model = "McFL",
#                         detectionSize = 5e6,
#                         finalTime = 270,
#                         keepPhylog = TRUE,
#                         onlyCancer = FALSE)
#  mue1
#  ## If you decrease N even further it gets even more cluttered
#  op <- par(ask = TRUE)
#  plotClonePhylog(mue1, N = 10, timeEvents = TRUE)
#  plot(mue1, plotDrivers = TRUE, addtot = TRUE,
#       plotDiversity = TRUE)
#  	
#  ## The stacked plot is slow; be patient
#  ## Most clones have tiny population sizes, and their lines
#  ## are piled on top of each other
#  plot(mue1, addtot = TRUE,
#       plotDiversity = TRUE, type = "stacked")
#  par(op)

## ----nyv0108------------------------------------------------------

d1 <- -0.05 ## single mutant fitness 0.95
d2 <- -0.08 ## double mutant fitness 0.92
d3 <- 0.2   ## triple mutant fitness 1.2
s2 <- ((1 + d2)/(1 + d1)^2) - 1
s3 <- ( (1 + d3)/((1 + d1)^3 * (1 + s2)^3) ) - 1

wb <- allFitnessEffects(
    epistasis = c(
        "A" = d1,
        "B" = d1,
        "C" = d1,
        "A:B" = s2,
        "A:C" = s2,
        "B:C" = s2,
        "A:B:C" = s3))

## ----rzx0112, fig.width=6.5, fig.height=5-------------------------
plotFitnessLandscape(wb, use_ggrepel = TRUE) 

## ----rzx0113, fig.width=6.5, fig.height=5-------------------------
(ewb <- evalAllGenotypes(wb, order = FALSE))
plot(ewb, use_ggrepel = TRUE) 


## ----wasthis111, fig.width=9.5, fig.height=9.5--------------------
par(cex = 0.7)
pancr <- allFitnessEffects(
    data.frame(parent = c("Root", rep("KRAS", 4), 
                   "SMAD4", "CDNK2A", 
                   "TP53", "TP53", "MLL3"),
               child = c("KRAS","SMAD4", "CDNK2A", 
                   "TP53", "MLL3",
                   rep("PXDN", 3), rep("TGFBR2", 2)),
               s = 0.1,
               sh = -0.9,
               typeDep = "MN"))
plot(evalAllGenotypes(pancr, order = FALSE), use_ggrepel = TRUE)


## ----nyv0109------------------------------------------------------
K <- 4
sp <- 1e-5
sdp <- 0.015
sdplus <- 0.05
sdminus <- 0.1
cnt <- (1 + sdplus)/(1 + sdminus)
prod_cnt <- cnt - 1
bauer <- data.frame(parent = c("Root", rep("D", K)),
                    child = c("D", paste0("s", 1:K)),
                    s = c(prod_cnt, rep(sdp, K)),
                    sh = c(0, rep(sp, K)),
                    typeDep = "MN")
fbauer <- allFitnessEffects(bauer)
(b1 <- evalAllGenotypes(fbauer, order = FALSE, addwt = TRUE))


## ----rzx0114, fig.height=3----------------------------------------
plot(fbauer)

## ----rzx0115, fig.width=6, fig.height=6---------------------------
plot(b1, use_ggrepel = TRUE)

## ----nyv0110------------------------------------------------------
m1 <- expand.grid(D = c(1, 0), s1 = c(1, 0), s2 = c(1, 0),
                  s3 = c(1, 0), s4 = c(1, 0))

fitness_bauer <- function(D, s1, s2, s3, s4, 
                          sp = 1e-5, sdp = 0.015, sdplus = 0.05,
                          sdminus = 0.1) {
    if(!D) {
        b <- 0.5 * ( (1 + sp)^(sum(c(s1, s2, s3, s4))))
    } else {
        b <- 0.5 * 
            (((1 + sdplus)/(1 + sdminus)  *
              (1 + sdp)^(sum(c(s1, s2, s3, s4)))))
    }
    fitness <- b - (1 - b)
    our_fitness <- 1 + fitness ## prevent negative fitness and
    ## make wt fitness = 1
    return(our_fitness)
}

m1$Fitness <- 
    apply(m1, 1, function(x) do.call(fitness_bauer, as.list(x)))

bauer2 <- allFitnessEffects(genotFitness = m1)

## ----nyv0111------------------------------------------------------
evalAllGenotypes(bauer2, order = FALSE, addwt = TRUE)

## ----rzx0116, echo=FALSE, fig.height=4, fig.width=4---------------

df1 <- data.frame(x = c(1, 1.2, 1.4), f = c(1, 1.2, 1.2),
                 names = c("wt", "A", "B"))
plot(df1[, 2] ~ df1[, 1], axes = TRUE, xlab= "", 
     ylab = "Fitness", xaxt = "n", yaxt = "n", ylim = c(1, 1.21))
segments(1, 1, 1.2, 1.2)
segments(1, 1, 1.4, 1.2)
text(1, 1, "wt", pos = 4)
text(1.2, 1.2, "A", pos = 2)
text(1.4, 1.2, "B", pos = 2)
## axis(1,  tick = FALSE, labels = FALSE)
## axis(2,  tick = FALSE, labels = FALSE)

## ----nyv0112------------------------------------------------------
s <- 0.1 ## or whatever number
m1a1 <- allFitnessEffects(data.frame(parent = c("Root", "Root"),
                                     child = c("A", "B"),
                                     s = s,
                                     sh = 0,
                                     typeDep = "MN"))
evalAllGenotypes(m1a1, order = FALSE, addwt = TRUE)

## ----nyv0113------------------------------------------------------
s <- 0.1
sab <- 0.3
m1a2 <- allFitnessEffects(epistasis = c("A:-B" = s,
                                        "-A:B" = s,
                                        "A:B" = sab))
evalAllGenotypes(m1a2, order = FALSE, addwt = TRUE)

## ----nyv0114------------------------------------------------------
sab3 <- ((1 + sab)/((1 + s)^2)) - 1
m1a3 <- allFitnessEffects(data.frame(parent = c("Root", "Root"),
                                     child = c("A", "B"),
                                     s = s,
                                     sh = 0,
                                     typeDep = "MN"),
                          epistasis = c("A:B" = sab3))
evalAllGenotypes(m1a3, order = FALSE, addwt = TRUE)

## ----nyv0115------------------------------------------------------
all.equal(evalAllGenotypes(m1a2, order = FALSE, addwt = TRUE),
          evalAllGenotypes(m1a3, order = FALSE, addwt = TRUE))

## ----rzx0117, echo=FALSE, fig.width=4, fig.height=4---------------

df1 <- data.frame(x = c(1, 1.2, 1.2, 1.4), f = c(1, 0.4, 0.3, 1.3),
                 names = c("wt", "A", "B", "AB"))
plot(df1[, 2] ~ df1[, 1], axes = TRUE, xlab= "", ylab = "Fitness",
     xaxt = "n", yaxt = "n", ylim = c(0.29, 1.32))
segments(1, 1, 1.2, 0.4)
segments(1, 1, 1.2, 0.3)
segments(1.2, 0.4, 1.4, 1.3)
segments(1.2, 0.3, 1.4, 1.3)
text(x = df1[, 1], y = df1[, 2], labels = df1[, "names"], pos = c(4, 2, 2, 2))
## text(1, 1, "wt", pos = 4)
## text(1.2, 1.2, "A", pos = 2)
## text(1.4, 1.2, "B", pos = 2)

## ----nyv0116------------------------------------------------------
sa <- -0.6
sb <- -0.7
sab <- 0.3
m1b1 <- allFitnessEffects(epistasis = c("A:-B" = sa,
                                        "-A:B" = sb,
                                        "A:B" = sab))
evalAllGenotypes(m1b1, order = FALSE, addwt = TRUE)

## ----rzx0118, echo=FALSE, fig.width=4, fig.height=4---------------

df1 <- data.frame(x = c(1, 1.2, 1.2, 1.4), f = c(1, 1.2, 0.7, 1.5),
                 names = c("wt", "A", "B", "AB"))
plot(df1[, 2] ~ df1[, 1], axes = TRUE, xlab = "", ylab = "Fitness",
     xaxt = "n", yaxt = "n", ylim = c(0.69, 1.53))
segments(1, 1, 1.2, 1.2)
segments(1, 1, 1.2, 0.7)
segments(1.2, 1.2, 1.4, 1.5)
segments(1.2, 0.7, 1.4, 1.5)
text(x = df1[, 1], y = df1[, 2], labels = df1[, "names"], pos = c(3, 3, 3, 2))
## text(1, 1, "wt", pos = 4)
## text(1.2, 1.2, "A", pos = 2)
## text(1.4, 1.2, "B", pos = 2)


## ----nyv0117------------------------------------------------------
sa <- 0.2
sb <- -0.3
sab <- 0.5
m1c1 <- allFitnessEffects(epistasis = c("A:-B" = sa,
                                        "-A:B" = sb,
                                        "A:B" = sab))
evalAllGenotypes(m1c1, order = FALSE, addwt = TRUE)

## ----rzx0119, echo=FALSE, fig.width=4.5, fig.height=3.5-----------

df1 <- data.frame(x = c(1, 2, 3, 4), f = c(1.1, 1, 0.95, 1.2),
                 names = c("u", "wt", "i", "v"))
plot(df1[, 2] ~ df1[, 1], axes = FALSE, xlab = "", ylab = "")
par(las = 1)
axis(2)
axis(1, at = c(1, 2, 3, 4), labels = df1[, "names"], ylab = "")
box()
arrows(c(2, 2, 3), c(1, 1, 0.95),
       c(1, 3, 4), c(1.1, 0.95, 1.2))
## text(1, 1, "wt", pos = 4)
## text(1.2, 1.2, "A", pos = 2)
## text(1.4, 1.2, "B", pos = 2)

## ----nyv0118------------------------------------------------------
su <- 0.1
si <- -0.05
fvi <- 1.2 ## the fitness of the vi mutant
sv <- (fvi/(1 + si)) - 1
sui <- suv <- -1
od <- allFitnessEffects(
    data.frame(parent = c("Root", "Root", "i"),
               child = c("u", "i", "v"),
               s = c(su, si, sv),
               sh = -1,
               typeDep = "MN"),
    epistasis = c(
        "u:i" = sui,
        "u:v" = suv))

## ----rzx0120, fig.width=3, fig.height=3---------------------------
plot(od)

## ----nyv0119------------------------------------------------------
evalAllGenotypes(od, order = FALSE, addwt = TRUE)

## ----nyv0120------------------------------------------------------
u <- 0.1; i <- -0.05; vi <- (1.2/0.95) - 1; ui <- uv <- -Inf
od2 <- allFitnessEffects(
    epistasis = c("u" = u,  "u:i" = ui,
                  "u:v" = uv, "i" = i,
                  "v:-i" = -Inf, "v:i" = vi))
evalAllGenotypes(od2, addwt = TRUE)


## ----rzx0121, echo=FALSE, fig.width=4, fig.height=3---------------

df1 <- data.frame(x = c(1, 2, 3), f = c(1, 0.95, 1.2),
                 names = c("wt", "1", "2"))
plot(df1[, 2] ~ df1[, 1], axes = FALSE, xlab = "", ylab = "")
par(las = 1)
axis(2)
axis(1, at = c(1, 2, 3), labels = df1[, "names"], ylab = "")
box()
segments(c(1, 2), c(1, 0.95),
       c(2, 3), c(0.95, 1.2))
## text(1, 1, "wt", pos = 4)
## text(1.2, 1.2, "A", pos = 2)
## text(1.4, 1.2, "B", pos = 2)



## ----rzx0122, echo=FALSE, fig.width=4, fig.height=3---------------

df1 <- data.frame(x = c(1, 2, 3, 4), f = c(1, 0.95, 0.92, 1.2),
                 names = c("wt", "1", "2", "3"))
plot(df1[, 2] ~ df1[, 1], axes = FALSE, xlab = "", ylab = "")
par(las = 1)
axis(2)
axis(1, at = c(1, 2, 3, 4), labels = df1[, "names"], ylab = "")
box()
segments(c(1, 2, 3), c(1, 0.95, 0.92),
       c(2, 3, 4), c(0.95, 0.92, 1.2))
## text(1, 1, "wt", pos = 4)
## text(1.2, 1.2, "A", pos = 2)
## text(1.4, 1.2, "B", pos = 2)

## ----nyv0122------------------------------------------------------

d1 <- -0.05 ## single mutant fitness 0.95
d2 <- -0.08 ## double mutant fitness 0.92
d3 <- 0.2   ## triple mutant fitness 1.2

s2 <- ((1 + d2)/(1 + d1)^2) - 1
s3 <- ( (1 + d3)/((1 + d1)^3 * (1 + s2)^3) ) - 1

w <- allFitnessEffects(
    data.frame(parent = c("Root", "Root", "Root"),
               child = c("A", "B", "C"),
               s = d1,
               sh = -1,
               typeDep = "MN"),
    epistasis = c(
        "A:B" = s2,
        "A:C" = s2,
        "B:C" = s2,
        "A:B:C" = s3))

## ----rzx0123, fig.width=4, fig.height=4---------------------------
plot(w)

## ----nyv0123------------------------------------------------------
evalAllGenotypes(w, order = FALSE, addwt = TRUE)

## ----nyv0124------------------------------------------------------
wb <- allFitnessEffects(
    epistasis = c(
        "A" = d1,
        "B" = d1,
        "C" = d1,
        "A:B" = s2,
        "A:C" = s2,
        "B:C" = s2,
        "A:B:C" = s3))

evalAllGenotypes(wb, order = FALSE, addwt = TRUE)

## ----rzx0124, , fig.width=3, fig.height=3-------------------------
plot(wb)

## ----nyv0125------------------------------------------------------
wc <- allFitnessEffects(
    epistasis = c(
        "A:-B:-C" = d1,
        "B:-C:-A" = d1,
        "C:-A:-B" = d1,
        "A:B:-C" = d2,
        "A:C:-B" = d2,
        "B:C:-A" = d2,
        "A:B:C" = d3))
evalAllGenotypes(wc, order = FALSE, addwt = TRUE)

## ----rzx0125, fig.width=4-----------------------------------------

pancr <- allFitnessEffects(
    data.frame(parent = c("Root", rep("KRAS", 4), 
                   "SMAD4", "CDNK2A", 
                   "TP53", "TP53", "MLL3"),
               child = c("KRAS","SMAD4", "CDNK2A", 
                   "TP53", "MLL3",
                   rep("PXDN", 3), rep("TGFBR2", 2)),
               s = 0.1,
               sh = -0.9,
               typeDep = "MN"))

plot(pancr)

## ----rzx0126, fig.height = 4--------------------------------------
rv1 <- allFitnessEffects(data.frame(parent = c("Root", "A", "KRAS"),
                                    child = c("A", "KRAS", "FBXW7"),
                                    s = 0.1,
                                    sh = -0.01,
                                    typeDep = "MN"),
                         geneToModule = c("Root" = "Root",
                             "A" = "EVC2, PIK3CA, TP53",
                             "KRAS" = "KRAS",
                             "FBXW7" = "FBXW7"))

plot(rv1, expandModules = TRUE, autofit = TRUE)


## ----rzx0127, fig.height=6----------------------------------------
rv2 <- allFitnessEffects(
    data.frame(parent = c("Root", "1", "2", "3", "4"),
               child = c("1", "2", "3", "4", "ELF3"),
               s = 0.1,
               sh = -0.01,
               typeDep = "MN"),
    geneToModule = c("Root" = "Root",
                     "1" = "APC, FBXW7",
                     "2" = "ATM, FAM123B, PIK3CA, TP53",
                     "3" = "BRAF, KRAS, NRAS",
                     "4" = "SMAD2, SMAD4, SOX9",
                     "ELF3" = "ELF3"))

plot(rv2, expandModules = TRUE,   autofit = TRUE)

## ----seedprbau003, echo=FALSE-------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## ----prbau003, fig.height=6---------------------------------------

o3init <- allFitnessEffects(orderEffects = c(
                            "M > D > F" = 0.99,
                            "D > M > F" = 0.2,
                            "D > M"     = 0.1,
                            "M > D"     = 0.9),
                        noIntGenes = c("u" = 0.01, 
                                       "v" = 0.01,
                                       "w" = 0.001,
                                       "x" = 0.0001,
                                       "y" = -0.0001,
                                       "z" = -0.001),
                        geneToModule =
                            c("M" = "m",
                              "F" = "f",
                              "D" = "d") )

oneI <- oncoSimulIndiv(o3init, model = "McFL",
                       mu = 5e-5, finalTime = 200,
                       detectionDrivers = 3,
                       onlyCancer = FALSE,
                       initSize = 1000,
                       keepPhylog = TRUE,
                       initMutant = c("m > u > d")
                       )
plotClonePhylog(oneI, N = 0)

## ----prbau003bb, fig.height=6-------------------------------------
## Note we also disable the stopping stochastically as a function of size
## to allow the population to grow large and generate may different
## clones.

## For speed, we set a small finalTime and we fix the seed
## for reproducilibity. Beware: since finalTime is short, sometimes
## we do not reach cancer 
set.seed(1)
RNGkind("L'Ecuyer-CMRG")

ospI <- oncoSimulPop(2, 
                    o3init, model = "Exp",
                    mu = 5e-5, finalTime = 200,
                    detectionDrivers = 3,
                    onlyCancer = TRUE,
                    initSize = 10,
                    keepPhylog = TRUE,
                    initMutant = c("d > m > z"),
                    mc.cores = 2,
                    seed = NULL
                    )

## Show just one example
## op <- par(mar = rep(0, 4), mfrow = c(1, 2))
plotClonePhylog(ospI[[1]])
## plotClonePhylog(ospI[[2]])
## par(op)

set.seed(1)
RNGkind("L'Ecuyer-CMRG")
ossI <- oncoSimulSample(2, 
                        o3init, model = "Exp",
                        mu = 5e-5, finalTime = 200,
                        detectionDrivers = 2,
                        onlyCancer = TRUE,
                        initSize = 10,
                        initMutant = c("z > d"),
                        ## check presence of initMutant:
                        thresholdWhole = 1,
                        seed = NULL
                    )

## No phylogeny is kept with oncoSimulSample, but look at the 
## OcurringDrivers and the sample
ossI$popSample
ossI$popSummary[, "OccurringDrivers", drop = FALSE]

## ----prbaux002, eval=FALSE----------------------------------------
#  gi2 <- rep(0, 5)
#  names(gi2) <- letters[1:5]
#  oi2 <- allFitnessEffects(noIntGenes = gi2)
#  s5 <- oncoSimulPop(200,
#                     oi2,
#                     model = "McFL",
#                     initSize = 1000,
#                     onlyCancer = TRUE,
#                     detectionProb = c(p2 = 0.1,
#                                       n2 = 2000,
#                                       PDBaseline = 1000,
#                                       checkSizePEvery = 2),
#                     detectionSize = NA,
#                     finalTime = NA,
#                     keepEvery = NA,
#                     detectionDrivers = NA)
#  s5
#  hist(unlist(lapply(s5, function(x) x$FinalTime)))

## ----nyv0126------------------------------------------------------
u <- 0.2; i <- -0.02; vi <- 0.6; ui <- uv <- -Inf
od2 <- allFitnessEffects(
    epistasis = c("u" = u,  "u:i" = ui,
                  "u:v" = uv, "i" = i,
                  "v:-i" = -Inf, "v:i" = vi))

## ----simul-ochs---------------------------------------------------
initS <- 20
## We use only a small number of repetitions for the sake
## of speed. Even fewer in Windows, since we run on a single
## core

if(.Platform$OS.type == "windows") {
    nruns <- 4
} else {
    nruns <- 10
}

od100 <- oncoSimulPop(nruns, od2,
                      fixation = c("u", "v, i"),
                      model = "McFL",
                      mu = 1e-4,
                      detectionDrivers = NA,
                      finalTime = NA,
                      detectionSize = NA,
                      detectionProb = NA,
                      onlyCancer = TRUE,
                      initSize = initS, 
                      mc.cores = 2)

## ----ochs-freq-genots---------------------------------------------
sampledGenotypes(samplePop(od100))                      

## ----sum-simul-ochs-----------------------------------------------
head(summary(od100)[, c(1:3, 8:9)])

## ----fixationG1---------------------------------------------------
## Create a simple fitness landscape
rl1 <- matrix(0, ncol = 6, nrow = 9)
colnames(rl1) <- c(LETTERS[1:5], "Fitness")
rl1[1, 6] <- 1
rl1[cbind((2:4), c(1:3))] <- 1
rl1[2, 6] <- 1.4
rl1[3, 6] <- 1.32
rl1[4, 6] <- 1.32
rl1[5, ] <- c(0, 1, 0, 0, 1, 1.5)
rl1[6, ] <- c(0, 0, 1, 1, 0, 1.54)
rl1[7, ] <- c(1, 0, 1, 1, 0, 1.65)
rl1[8, ] <- c(1, 1, 1, 1, 0, 1.75)
rl1[9, ] <- c(1, 1, 1, 1, 1, 1.85)
class(rl1) <- c("matrix", "genotype_fitness_matrix")
## plot(rl1) ## to see the fitness landscape

## Gene combinations
local_max_g <- c("A", "B, E", "A, B, C, D, E")
## Specify the genotypes
local_max <- paste0("_,", local_max_g)

fr1 <- allFitnessEffects(genotFitness = rl1, drvNames = LETTERS[1:5])
initS <- 2000


######## Stop on gene combinations   #####
r1 <- oncoSimulPop(10,
                       fp = fr1,
                       model = "McFL",
                       initSize = initS,
                       mu = 1e-4,
                       detectionSize = NA,
                       sampleEvery = .03,
                       keepEvery = 1, 
                       finalTime = 50000,
                       fixation = local_max_g, 
                       detectionDrivers = NA,
                       detectionProb = NA,
                       onlyCancer = TRUE,
                       max.num.tries = 500,
                       max.wall.time = 20, 
                       errorHitMaxTries = TRUE,
                       keepPhylog = FALSE,
                       mc.cores = 2)
sp1 <- samplePop(r1, "last", "singleCell")
sgsp1 <- sampledGenotypes(sp1)
## often you will stop on gene combinations that
## are not local maxima in the fitness landscape
sgsp1
sgsp1$Genotype %in% local_max_g

####### Stop on genotypes   ####

r2 <- oncoSimulPop(10,
                       fp = fr1,
                       model = "McFL",
                       initSize = initS,
                       mu = 1e-4,
                       detectionSize = NA,
                       sampleEvery = .03,
                       keepEvery = 1, 
                       finalTime = 50000,
                       fixation = local_max, 
                       detectionDrivers = NA,
                       detectionProb = NA,
                       onlyCancer = TRUE,
                       max.num.tries = 500,
                       max.wall.time = 20, 
                       errorHitMaxTries = TRUE,
                       keepPhylog = FALSE,
                       mc.cores = 2)
## All final genotypes should be local maxima                       
sp2 <- samplePop(r2, "last", "singleCell")
sgsp2 <- sampledGenotypes(sp2)
sgsp2$Genotype %in% local_max_g




## ----fixationG2---------------------------------------------------
## Create a simple fitness landscape
rl1 <- matrix(0, ncol = 6, nrow = 9)
colnames(rl1) <- c(LETTERS[1:5], "Fitness")
rl1[1, 6] <- 1
rl1[cbind((2:4), c(1:3))] <- 1
rl1[2, 6] <- 1.4
rl1[3, 6] <- 1.32
rl1[4, 6] <- 1.32
rl1[5, ] <- c(0, 1, 0, 0, 1, 1.5)
rl1[6, ] <- c(0, 0, 1, 1, 0, 1.54)
rl1[7, ] <- c(1, 0, 1, 1, 0, 1.65)
rl1[8, ] <- c(1, 1, 1, 1, 0, 1.75)
rl1[9, ] <- c(1, 1, 1, 1, 1, 1.85)
class(rl1) <- c("matrix", "genotype_fitness_matrix")
## plot(rl1) ## to see the fitness landscape

## The local fitness maxima are
## c("A", "B, E", "A, B, C, D, E")

fr1 <- allFitnessEffects(genotFitness = rl1, drvNames = LETTERS[1:5])
initS <- 2000

## Stop on genotypes

r3 <- oncoSimulPop(10,
                  fp = fr1,
                  model = "McFL",
                  initSize = initS,
                  mu = 1e-4,
                  detectionSize = NA,
                  sampleEvery = .03,
                  keepEvery = 1, 
                  finalTime = 50000,
                  fixation = c(paste0("_,",
                                   c("A", "B, E", "A, B, C, D, E")),
                               fixation_tolerance = 0.1,
                               min_successive_fixation = 200,
                               fixation_min_size = 3000),
                  detectionDrivers = NA,
                  detectionProb = NA,
                  onlyCancer = TRUE,
                  max.num.tries = 500,
                  max.wall.time = 20, 
                  errorHitMaxTries = TRUE,
                  keepPhylog = FALSE,
                  mc.cores = 2)


## ----prbaux001----------------------------------------------------
K <- 5
sd <- 0.1
sdp <- 0.15
sp <- 0.05
bauer <- data.frame(parent = c("Root", rep("p", K)),
                    child = c("p", paste0("s", 1:K)),
                    s = c(sd, rep(sdp, K)),
                    sh = c(0, rep(sp, K)),
                    typeDep = "MN")
fbauer <- allFitnessEffects(bauer, drvNames = "p")
set.seed(1)
## Use fairly large mutation rate
b1 <- oncoSimulIndiv(fbauer, mu = 5e-5, initSize = 1000,
                     finalTime = NA,
                     onlyCancer = TRUE,
                     detectionProb = "default")


## ----baux1,fig.width=6.5, fig.height=10, fig.cap="Three drivers' plots of a simulation of Bauer's model"----
par(mfrow = c(3, 1))
## First, drivers
plot(b1, type = "line", addtot = TRUE)
plot(b1, type = "stacked")
plot(b1, type = "stream")

## ----baux2,fig.width=6.5, fig.height=10, fig.cap="Three genotypes' plots of a simulation of Bauer's model"----
par(mfrow = c(3, 1))
## Next, genotypes
plot(b1, show = "genotypes", type = "line")
plot(b1, show = "genotypes", type = "stacked")
plot(b1, show = "genotypes", type = "stream")

## ----rzx0000, fig.width=6-----------------------------------------

set.seed(678)
nd <- 70  
np <- 5000 
s <- 0.1  
sp <- 1e-3 
spp <- -sp/(1 + sp)
mcf1 <- allFitnessEffects(noIntGenes = c(rep(s, nd), rep(spp, np)),
                          drvNames = seq.int(nd))
mcf1s <-  oncoSimulIndiv(mcf1,
                         model = "McFL", 
                         mu = 1e-7,
                         detectionProb = "default",
                         detectionSize = NA, 
                         detectionDrivers = NA,
                         sampleEvery = 0.025,
                         keepEvery = 8,
                         initSize = 2000,
                         finalTime = 4000,
                         onlyCancer = FALSE)
summary(mcf1s)


## ----mcf1sx1,fig.width=6.5, fig.height=10-------------------------
par(mfrow  = c(2, 1))
## I use thinData to make figures smaller and faster
plot(mcf1s, addtot = TRUE, lwdClone = 0.9, log = "", 
     thinData = TRUE, thinData.keep = 0.5)
plot(mcf1s, show = "drivers", type = "stacked",
     thinData = TRUE, thinData.keep = 0.3,
     legend.ncols = 2)

## ----rzx0300, eval=FALSE------------------------------------------
#  
#  set.seed(123)
#  nd <- 70
#  np <- 50000
#  s <- 0.1
#  sp <- 1e-4 ## as we have many more passengers
#  spp <- -sp/(1 + sp)
#  mcfL <- allFitnessEffects(noIntGenes = c(rep(s, nd), rep(spp, np)),
#                            drvNames = seq.int(nd))
#  mcfLs <-  oncoSimulIndiv(mcfL,
#                           model = "McFL",
#                           mu = 1e-7,
#                           detectionSize = 1e8,
#                           detectionDrivers = 100,
#                           sampleEvery = 0.02,
#                           keepEvery = 2,
#                           initSize = 1000,
#                           finalTime = 2000,
#                           onlyCancer = FALSE)

## ----rzx0301, mcflsx2,fig.width=6---------------------------------
data(mcfLs)
plot(mcfLs, addtot = TRUE, lwdClone = 0.9, log = "",
     thinData = TRUE, thinData.keep = 0.3,
     plotDiversity = TRUE)

## ----nyv0127------------------------------------------------------
summary(mcfLs)
## number of passengers per clone
summary(colSums(mcfLs$Genotypes[-(1:70), ]))

## ----mcflsx3------------------------------------------------------
plot(mcfLs, type = "stacked", thinData = TRUE, 
     thinData.keep = 0.1,
     plotDiversity = TRUE,
     xlim = c(0, 1000))

## ----nyv0128------------------------------------------------------
data(examplesFitnessEffects)
names(examplesFitnessEffects)

## ----seedsmmmcfls-------------------------------------------------
set.seed(1)

## ----smmcfls------------------------------------------------------
data(examplesFitnessEffects)
evalAllGenotypes(examplesFitnessEffects$cbn1, order = FALSE)[1:10, ]
sm <-  oncoSimulIndiv(examplesFitnessEffects$cbn1,
                      model = "McFL", 
                      mu = 5e-7,
                      detectionSize = 1e8, 
                      detectionDrivers = 2,
                      detectionProb = "default",
                      sampleEvery = 0.025,
                      keepEvery = 5,
                      initSize = 2000,
                      onlyCancer = TRUE)
summary(sm)

## ----smbosb1------------------------------------------------------
set.seed(1234)
evalAllGenotypes(examplesFitnessEffects$cbn1, order = FALSE, 
                 model = "Bozic")[1:10, ]
sb <-  oncoSimulIndiv(examplesFitnessEffects$cbn1,
                       model = "Bozic", 
                       mu = 5e-6,
                      detectionProb = "default",
                       detectionSize = 1e8, 
                       detectionDrivers = 4,
                       sampleEvery = 2,
                       initSize = 2000,
                       onlyCancer = TRUE)
summary(sb)

## ----sbx1,fig.width=6.5, fig.height=3.3---------------------------
## Show drivers, line plot
par(cex = 0.75, las = 1)
plot(sb,show = "drivers", type = "line", addtot = TRUE,
     plotDiversity = TRUE)

## ----sbx2,fig.width=6.5, fig.height=3.3---------------------------
## Drivers, stacked
par(cex = 0.75, las = 1)
plot(sb,show = "drivers", type = "stacked", plotDiversity = TRUE)

## ----sbx3,fig.width=6.5, fig.height=3.3---------------------------
## Drivers, stream
par(cex = 0.75, las = 1)
plot(sb,show = "drivers", type = "stream", plotDiversity = TRUE)

## ----sbx4,fig.width=6.5, fig.height=3.3---------------------------
## Genotypes, line plot
par(cex = 0.75, las = 1)
plot(sb,show = "genotypes", type = "line", plotDiversity = TRUE)

## ----sbx5,fig.width=6.5, fig.height=3.3---------------------------
## Genotypes, stacked
par(cex = 0.75, las = 1)
plot(sb,show = "genotypes", type = "stacked", plotDiversity = TRUE)

## ----sbx6,fig.width=6.5, fig.height=3.3---------------------------
## Genotypes, stream
par(cex = 0.75, las = 1)
plot(sb,show = "genotypes", type = "stream", plotDiversity = TRUE)

## ----rzx0302, fig.width=6-----------------------------------------

set.seed(4321)
tmp <-  oncoSimulIndiv(examplesFitnessEffects[["o3"]],
                       model = "McFL", 
                       mu = 5e-5,
                       detectionSize = 1e8, 
                       detectionDrivers = 3,
                       sampleEvery = 0.025,
                       max.num.tries = 10,
                       keepEvery = 5,
                       initSize = 2000,
                       finalTime = 6000,
                       onlyCancer = FALSE) 

## ----tmpmx1,fig.width=6.5, fig.height=4.1-------------------------
par(las = 1, cex = 0.85)
plot(tmp, addtot = TRUE, log = "", plotDiversity = TRUE,
     thinData = TRUE, thinData.keep = 0.2)

## ----tmpmx2,fig.width=6.5, fig.height=4.1-------------------------
par(las = 1, cex = 0.85)
plot(tmp, type = "stacked", plotDiversity = TRUE, 
     ylim = c(0, 5500), legend.ncols = 4,
     thinData = TRUE, thinData.keep = 0.2)

## ----nyv0129------------------------------------------------------
evalAllGenotypes(examplesFitnessEffects[["o3"]], addwt = TRUE,
                 order = TRUE)

## ----tmpmx3,fig.width=6.5, fig.height=5.5-------------------------
plot(tmp, show = "genotypes", ylim = c(0, 5500), legend.ncols = 3,
     thinData = TRUE, thinData.keep = 0.5)

## ----nyv0130------------------------------------------------------
set.seed(15)
tmp <-  oncoSimulIndiv(examplesFitnessEffects[["o3"]],
                       model = "McFL", 
                       mu = 5e-5,
                       detectionSize = 1e8, 
                       detectionDrivers = 3,
                       sampleEvery = 0.025,
                       max.num.tries = 10,
                       keepEvery = 5,
                       initSize = 2000,
                       finalTime = 20000,
                       onlyCancer = FALSE,
                       extraTime = 1500)
tmp

## ----tmpmdx5,fig.width=6.5, fig.height=4--------------------------
par(las = 1, cex = 0.85)
plot(tmp, addtot = TRUE, log = "", plotDiversity = TRUE,
     thinData = TRUE, thinData.keep = 0.5)

## ----tmpmdx6,fig.width=6.5, fig.height=4--------------------------
par(las = 1, cex = 0.85)
plot(tmp, type = "stacked", plotDiversity = TRUE,
     legend.ncols = 4, ylim = c(0, 5200), xlim = c(3400, 5000),
     thinData = TRUE, thinData.keep = 0.5)

## ----tmpmdx7,fig.width=6.5, fig.height=5.3------------------------
## Improve telling apart the most abundant 
## genotypes by sorting colors
## differently via breakSortColors
## Modify ncols of legend, so it is legible by not overlapping
## with plot
par(las = 1, cex = 0.85)
plot(tmp, show = "genotypes", breakSortColors = "distave",
     plotDiversity = TRUE, legend.ncols = 4,
     ylim = c(0, 5300), xlim = c(3400, 5000),
     thinData = TRUE, thinData.keep = 0.5)

## ----rzx0303, eval=FALSE------------------------------------------
#  ## Convert the data
#  lb1 <- OncoSimulWide2Long(b1)
#  
#  ## Install the streamgraph package from GitHub and load
#  library(devtools)
#  devtools::install_github("hrbrmstr/streamgraph")
#  library(streamgraph)
#  
#  ## Stream plot for Genotypes
#  sg_legend(streamgraph(lb1, Genotype, Y, Time, scale = "continuous"),
#                show=TRUE, label="Genotype: ")
#  
#  ## Staked area plot and we use the pipe
#  streamgraph(lb1, Genotype, Y, Time, scale = "continuous",
#              offset = "zero") %>%
#      sg_legend(show=TRUE, label="Genotype: ")

## ----minitm1------------------------------------------------------
r2 <- rfitness(6)
## Make sure these always viable for interesting stuff
r2[2, 7] <- 1 + runif(1) # A
r2[4, 7] <- 1 + runif(1) # C
r2[8, 7] <- 1 + runif(1) # A, B
o2 <- allFitnessEffects(genotFitness = r2)
ag <- evalAllGenotypes(o2)

out1 <- oncoSimulIndiv(o2, initMutant = c("A", "C"),
                       initSize = c(100, 200),
                       onlyCancer = FALSE,
                       finalTime = 200)



## ----minitm1fdf---------------------------------------------------
gffd0 <- data.frame(
    Genotype = c(
        "A", "A, B",
        "C", "C, D", "C, E"),
    Fitness = c(
        "1.3",
        "1.4",
        "1.4",
        "1.1 + 0.7*((f_A + f_A_B) > 0.3)",
        "1.2 + sqrt(f_A + f_C + f_C_D)"))

afd0 <- allFitnessEffects(genotFitness = gffd0,
                          frequencyDependentFitness = TRUE)

sp <- 1:5
names(sp) <- c("A", "C", "A, B", "C, D", "C, E")
eag0 <- evalAllGenotypes(afd0, spPopSizes = sp)

os0 <- oncoSimulIndiv(afd0,
                      initMutant = c("A", "C"),
                      finalTime = 20, initSize = c(1e4, 1e5),
                      onlyCancer = FALSE, model = "McFLD")

## ----mspeci1------------------------------------------------------

mspec <- data.frame(
    Genotype = c("A",
                 "A, a1", "A, a2", "A, a1, a2",
                 "B",
                 "B, b1", "B, b2", "B, b3",
                 "B, b1, b2", "B, b1, b3", "B, b1, b2, b3"),
    Fitness = 1 + runif(11)
)
fmspec <- allFitnessEffects(genotFitness = mspec)
afmspec <- evalAllGenotypes(fmspec)

## Show only viable ones
afmspec[afmspec$Fitness >= 1, ]

muv <- c(1e-10, rep(1e-5, 2), 1e-10, rep(1e-5, 3))
names(muv) <- c("A", paste0("a", 1:2), "B", paste0("b", 1:3))

out1 <- oncoSimulIndiv(fmspec, initMutant = c("A", "B"),
                       initSize = c(100, 200),
                       mu = muv,
                       onlyCancer = FALSE,
                       finalTime = 200)


## ----mspeci2------------------------------------------------------

mspecF <- data.frame(
    Genotype = c("A",
                 "A, a1", "A, a2", "A, a1, a2",
                 "B",
                 "B, b1", "B, b2", "B, b3",
                 "B, b1, b2", "B, b1, b3", "B, b1, b2, b3"),
    Fitness = c("1 + f_A_a1",
                "1 + f_A_a2",
                "1 + f_A_a1_a2",
                "1 + f_B",
                "1 + f_B_b1",
                "1 + f_B_b2",
                "1 + f_B_b3",
                "1 + f_B_b1_b2",
                "1 + f_B_b1_b3",
                "1 + f_B_b1_b2_b3",
                "1 + f_A")
)
fmspecF <- allFitnessEffects(genotFitness = mspecF,
                             frequencyDependentFitness = TRUE)
## Remeber, spPopSizes correspond to the genotypes
## shown in
fmspecF$full_FDF_spec
## in exactly that order if it is unnamed.

afmspecF <- evalAllGenotypes(fmspecF,
                             spPopSizes = 1:11)

## Alternatively, pass a named vector, which is the recommended approach

spp <- 1:11
names(spp) <- c("A","B",
                "A, a1", "A, a2", 
                "B, b1", "B, b2", "B, b3",
                "A, a1, a2",
                "B, b1, b2", "B, b1, b3", "B, b1, b2, b3")

afmspecF <- evalAllGenotypes(fmspecF,
                             spPopSizes = spp)


## Show only viable ones
afmspecF[afmspecF$Fitness >= 1, ]

## Expected values of fitness
exv <- 1 + c(3, 5, 4, 8, 6, 7, 9, 2, 10, 11, 1)/sum(1:11)
stopifnot(isTRUE(all.equal(exv, afmspecF[afmspecF$Fitness >= 1, ]$Fitness)))

muv <- c(1e-10, rep(1e-5, 2), 1e-10, rep(1e-5, 3))
names(muv) <- c("A", paste0("a", 1:2), "B", paste0("b", 1:3))

out1 <- oncoSimulIndiv(fmspecF, initMutant = c("A", "B"),
                       initSize = c(1e4, 1e5),
                       mu = muv,
                       finalTime = 20,
                       model = "McFLD",
                       onlyCancer = FALSE)


## ----pancrpopcreate-----------------------------------------------

pancrPop <- oncoSimulPop(4, pancr,
                         onlyCancer = TRUE,
                         detectionSize = 1e7,
                         keepEvery = 10,
                         mc.cores = 2)

summary(pancrPop)
samplePop(pancrPop)


## ----nyv0131------------------------------------------------------
library(parallel)

if(.Platform$OS.type == "windows") {
    mc.cores <- 1
} else {
    mc.cores <- 2
}

p2 <- mclapply(1:4, function(x) oncoSimulIndiv(pancr,
                                               onlyCancer = TRUE,
                                               detectionSize = 1e7,
                                               keepEvery = 10),
                                               mc.cores = mc.cores)
class(p2) <- "oncosimulpop"
samplePop(p2)

## ----nyv0132------------------------------------------------------
tail(pancrPop[[1]]$pops.by.time)

## ----nyv0133------------------------------------------------------
pancrPopNH <- oncoSimulPop(4, pancr,
                           onlyCancer = TRUE,
                           detectionSize = 1e7,
                           keepEvery = NA,
                           mc.cores = 2)

summary(pancrPopNH)
samplePop(pancrPopNH)

## ----nyv0134------------------------------------------------------
pancrPopNH[[1]]$pops.by.time

## ----nyv0135------------------------------------------------------
pancrSamp <- oncoSimulSample(4, pancr, onlyCancer = TRUE)
pancrSamp$popSamp


## ----nyv0136------------------------------------------------------

set.seed(15)
tmp <-  oncoSimulIndiv(examplesFitnessEffects[["o3"]],
                       model = "McFL", 
                       mu = 5e-5,
                       detectionSize = 1e8, 
                       detectionDrivers = 3,
                       sampleEvery = 0.025,
                       max.num.tries = 10,
                       keepEvery = 5,
                       initSize = 2000,
                       finalTime = 20000,
                       onlyCancer = FALSE,
                       extraTime = 1500,
                       keepPhylog = TRUE)
tmp

## ----nyv0137------------------------------------------------------
plotClonePhylog(tmp, N = 0)

## ----nyv0138------------------------------------------------------
plotClonePhylog(tmp, N = 1)

## ----pcpkeepx1----------------------------------------------------
plotClonePhylog(tmp, N = 1, keepEvents = TRUE)

## ----nyv0139------------------------------------------------------
plotClonePhylog(tmp, N = 1, timeEvents = TRUE)

## ----rzx0304, fig.keep="none"-------------------------------------
get.adjacency(plotClonePhylog(tmp, N = 1, returnGraph = TRUE))


## ----nyv0140------------------------------------------------------

set.seed(456)
mcf1s <-  oncoSimulIndiv(mcf1,
                         model = "McFL", 
                         mu = 1e-7,
                         detectionSize = 1e8, 
                         detectionDrivers = 100,
                         sampleEvery = 0.025,
                         keepEvery = 2,
                         initSize = 2000,
                         finalTime = 1000,
                         onlyCancer = FALSE,
                         keepPhylog = TRUE)


## ----nyv0141------------------------------------------------------
plotClonePhylog(mcf1s, N = 1)

## ----nyv0142------------------------------------------------------
par(cex = 0.7)
plotClonePhylog(mcf1s, N = 1, timeEvents = TRUE)

## ----nyv0143------------------------------------------------------
par(cex = 0.7)
plotClonePhylog(mcf1s, N = 1, t = c(800, 1000))

## ----nyv0144------------------------------------------------------
par(cex = 0.7)
plotClonePhylog(mcf1s, N = 1, t = c(900, 1000), timeEvents = TRUE)

## ----fig.keep="none"----------------------------------------------
g1 <- plotClonePhylog(mcf1s, N = 1, t = c(900, 1000),
                      returnGraph = TRUE)

## ----nyv0145------------------------------------------------------
plot(g1)

## ----rzx0305, eval=FALSE------------------------------------------
#  op <- par(ask = TRUE)
#  while(TRUE) {
#      tmp <- oncoSimulIndiv(smn1, model = "McFL",
#                            mu = 5e-5, finalTime = 500,
#                            detectionDrivers = 3,
#                            onlyCancer = FALSE,
#                            initSize = 1000, keepPhylog = TRUE)
#      plotClonePhylog(tmp, N = 0)
#  }
#  par(op)

## ----nyv0146------------------------------------------------------

oi <- allFitnessEffects(orderEffects =
               c("F > D" = -0.3, "D > F" = 0.4),
               noIntGenes = rexp(5, 10),
                          geneToModule =
                              c("F" = "f1, f2, f3",
                                "D" = "d1, d2") )
oiI1 <- oncoSimulIndiv(oi, model = "Exp", onlyCancer = TRUE)
oiP1 <- oncoSimulPop(4, oi,
                     keepEvery = 10,
                     mc.cores = 2,
                     keepPhylog = TRUE, onlyCancer = TRUE)


## ----rzx0306, fig.height=9----------------------------------------

op <- par(mar = rep(0, 4), mfrow = c(2, 1))
plotClonePhylog(oiP1[[1]])
plotClonePhylog(oiP1[[2]])
par(op)


## ----nyv0147------------------------------------------------------
## A small example
rfitness(3)

## A 5-gene example, where the reference genotype is the
## one with all positions mutated, similar to Greene and Crona,
## 2014.  We will plot the landscape and use it for simulations
## We downplay the random component with a sd = 0.5

r1 <- rfitness(5, reference = rep(1, 5), sd = 0.6)
plot(r1)
oncoSimulIndiv(allFitnessEffects(genotFitness = r1),
               onlyCancer = TRUE)

## ----nkex1--------------------------------------------------------
rnk <- rfitness(5, K = 3, model = "NK")
plot(rnk)
oncoSimulIndiv(allFitnessEffects(genotFitness = rnk),
               onlyCancer = TRUE)

## ----addex1-------------------------------------------------------
radd <- rfitness(4, model = "Additive", mu = 0, sd = 0.5)
plot(radd)

## ----eggex1-------------------------------------------------------
regg <- rfitness(4, model = "Eggbox", e = 1, E = 0.5)
plot(regg)

## ----isingex1-----------------------------------------------------
ris <- rfitness(g = 4, model = "Ising", i = 0.002, I = 0.45)
plot(ris)

## ----fullex1------------------------------------------------------
rnk <- rfitness(4, model = "Full", i = 0.5, I = 0.1,
                e = 2, s = 0.3, S = 0.08)
plot(rnk)

## ----magstats1----------------------------------------------------
rnk1 <- rfitness(6, K = 1, model = "NK")
Magellan_stats(rnk1)

rnk2 <- rfitness(6, K = 4, model = "NK")
Magellan_stats(rnk2)

## ----fdf1---------------------------------------------------------
## Define fitness of the different genotypes
gffd <- data.frame(
    Genotype = c("WT", "A", "B", "C", "A, B"), 
    Fitness = c("1 + 1.5 * f_A_B",
                "1.3 + 1.5 * f_A_B",
                "1.4",
                "1.1 + 0.7*((f_A + f_B) > 0.3) + f_A_B",
                "1.2 + sqrt(f_1 + f_C + f_B) - 0.3 * (f_A_B > 0.5)"))


## ----fdf1named----------------------------------------------------
## Define fitness of the different genotypes
gffdn <- data.frame(
    Genotype = c("WT", "A", "B", "C", "A, B"), 
    Fitness = c("1 + 1.5 * f_1_2",
                "1.3 + 1.5 * f_1_2",
                "1.4",
                "1.1 + 0.7*((f_1 + f_2) > 0.3) + f_1_2",
                "1.2 + sqrt(f_1 + f_3 + f_2) - 0.3 * (f_1_2 > 0.5)"),
    stringsAsFactors = FALSE)

## ----fdf1b--------------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness = gffd, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel"),
                 spPopSizes = c(WT = 100, A = 20, B = 20, C = 30, "A, B" = 0))

## Notice the warning
evalAllGenotypes(allFitnessEffects(genotFitness = gffd, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel"),
                 spPopSizes = c(100, 30, 40, 0, 10))

evalAllGenotypes(allFitnessEffects(genotFitness = gffd, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel"),
                 spPopSizes = c(100, 30, 40, 0, 100))


## ----fdf1bnamed---------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness = gffdn, 
                         frequencyDependentFitness = TRUE),
                 spPopSizes = c(100, 20, 20, 30, 0))

evalAllGenotypes(allFitnessEffects(genotFitness = gffdn, 
                         frequencyDependentFitness = TRUE),
                 spPopSizes = c(100, 30, 40, 0, 10))

evalAllGenotypes(allFitnessEffects(genotFitness = gffdn, 
                         frequencyDependentFitness = TRUE),
                 spPopSizes = c(100, 30, 40, 0, 100))


## ----fdf1c--------------------------------------------------------
afd <- allFitnessEffects(genotFitness = gffd, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

set.seed(1) ## for reproducibility
sfd <- oncoSimulIndiv(afd, 
                     model = "McFL", 
                     onlyCancer = FALSE, 
                     finalTime = 55, ## short, for speed
                     mu = 1e-4,
                     initSize = 5000, 
                     keepPhylog = FALSE,
                     seed = NULL, 
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE)
plot(sfd, show = "genotypes")

## ----hurlbutpay, eval=TRUE,echo=FALSE, fig.cap="Payoff matrix from Table 2 of Hurlbut et al., 2018, 'Game Theoretical Model of Cancer Dynamics withFour Cell Phenotypes', *Games*, 9, 61; doi:10.3390/g9030061."----
knitr::include_graphics("hurlbut.png")

## ----hurlbutfit1--------------------------------------------------


create_fe <- function(a, b, c, d, e, f, g,
                        gt = c("WT", "A", "P", "C")) {
  data.frame(Genotype = gt,
             Fitness = c(
                 paste0("1 + ",
                       d, " * f_A ",
                       "- ", c, " * f_C"),
                 paste0("1 - ", a, 
				     " + ", d, " + ",
                       f, " * f_A ",
                      "- ", c, " * f_C"),
                 paste0("1 + ", g, " + ",
                       d, " * f_A ",
                       "- ", c, " * (1 + ",
                       g, ") * f_C"),
                 paste0("1 - ", b, " + ",
                       e, " * f_ + ",
                       "(", d, " + ", 
					   e, ") * f_A + ",
                       e , " * f_P")))
}

## ----hbf1check----------------------------------------------------
create_fe("a", "b", "c", "d", "e", "f", "g")

## ----hurlbutfit2--------------------------------------------------
## Different assumption about origins from mutation:
## WT -> P; P -> A,P; P -> C,P

create_fe2 <- function(a, b, c, d, e, f, g,
                        gt = c("WT", "A", "P", "C", "A, P", "A, C",
                                "C, P")) {
  data.frame(Genotype = gt,
             Fitness = c(
                 paste0("1 + ",
                       d, " * f_A_P ",
                       "- ", c, " * f_P_C"),
                 "0",
                 paste0("1 + ", g, " + ",
                       d, " * f_A_P ",
                       "- ", c, " * (1 + ",
                       g, ") * f_P_C"),
                 "0",
                 paste0("1 - ", a, " + ", 
				 d, " + ",
                       f, " * f_A_P ",
                       "- ", c, " * f_P_C"),
                 "0",
                 paste0("1 - ", b, " + ",
                       e, " * f_ + ",
                       "(", d, " + ", 
					   e, ") * f_A_P + ",
                       e , " * f_P")),
             stringsAsFactors = FALSE)
}

## And check:
create_fe2("a", "b", "c", "d", "e", "f", "g")

## ----hurl3a, message=FALSE----------------------------------------
## Figure 3a
afe_3_a <- allFitnessEffects(
		        genotFitness =
                       create_fe(0.02, 0.04, 0.08, 0.06,
                                 0.15, 0.1, 0.06),
                frequencyDependentFitness = TRUE)


## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(s_3_a)
if (FALSE) {
set.seed(2)
s_3_a <- oncoSimulIndiv(afe_3_a,
                     model = "McFL", 
                     onlyCancer = FALSE, 
                     finalTime = 160,
                     mu = 1e-4,
                     initSize = 5000, 
                     keepPhylog = FALSE,
                     seed = NULL, 
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE,
                     keepEvery = 1)
}
plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue"))

## ----hurl3b, message=FALSE----------------------------------------
## Figure 3b
afe_3_b <- allFitnessEffects(
                genotFitness =
                       create_fe(0.02, 0.04, 0.08, 0.1,
                                 0.15, 0.1, 0.05),
                frequencyDependentFitness = TRUE)
				
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below
data(s_3_b)

if (FALSE) {				
set.seed(2)
## Use a short finalTime, for speed of vignette execution
s_3_b <- oncoSimulIndiv(afe_3_b,
                     model = "McFL", 
                     onlyCancer = FALSE, 
                     finalTime = 100, 
                     mu = 1e-4,
                     initSize = 5000, 
                     keepPhylog = FALSE, 
                     seed = NULL, 
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE,
                     keepEvery = 1)
}
plot(s_3_b, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue"))
## we are using keepEvery. O.w. we could have used
## thinData = TRUE for faster plotting

## ----hurl3b2------------------------------------------------------
## Figure 3b. Now with WT -> P; P -> A,P; P -> C,P
## For speed, we set finalTime = 100
afe_3_b_2 <- allFitnessEffects(
                  genotFitness =
                      create_fe2(0.02, 0.04, 0.08, 0.1,
                                 0.15, 0.1, 0.05),
                  frequencyDependentFitness = TRUE,
                  frequencyType = "rel")
set.seed(2)
s_3_b_2 <- oncoSimulIndiv(afe_3_b_2,
                   model = "McFL", 
                   onlyCancer = FALSE, 
                   finalTime = 100, 
                   mu = 1e-4,
                   initSize = 5000, 
                   keepPhylog = FALSE,
                   seed = NULL, 
                   errorHitMaxTries = FALSE, 
                   errorHitWallTime = FALSE,
				   keepEvery = 1)
plot(s_3_b_2, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue"))


## ----fdf2, message=FALSE------------------------------------------
gffd3 <- data.frame(Genotype = c("WT", "A", "B"), 
                   Fitness = c("1",
                               "1 + 0.2 * (n_B > 10)",
                               ".9 + 0.4 * (n_A > 10)"
                               ))
afd3 <- allFitnessEffects(genotFitness = gffd3, 
                         frequencyDependentFitness = TRUE)

## ----fdf2b, message=FALSE-----------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness = gffd3, 
                         frequencyDependentFitness = TRUE),
                 spPopSizes = c(WT = 100, A = 1, B = 11))

evalAllGenotypes(allFitnessEffects(genotFitness = gffd3, 
                         frequencyDependentFitness = TRUE),
                 spPopSizes = c(WT = 100, A = 11, B = 1))

## ----fdf2c--------------------------------------------------------
set.seed(1)
sfd3 <- oncoSimulIndiv(afd3,
                       model = "McFLD", 
                       onlyCancer = FALSE, 
                       finalTime = 200,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE)

## ----echo=FALSE---------------------------------------------------
op <- par(mfrow = c(1, 2))

## -----------------------------------------------------------------
plot(sfd3, show = "genotypes", type = "line")
plot(sfd3, show = "genotypes")
sfd3

## ----echo=FALSE---------------------------------------------------
par(op)


## ----fdf2d--------------------------------------------------------
set.seed(1)
sfd4 <- oncoSimulIndiv(afd3,
                       model = "McFL", 
                       onlyCancer = FALSE, 
                       finalTime = 145,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE)

## ----rzx0307, echo=FALSE------------------------------------------
op <- par(mfrow = c(1, 2))

## ----polotfdfv6---------------------------------------------------
plot(sfd4, show = "genotypes", type = "line")
plot(sfd4, show = "genotypes")
sfd4

## ----rzx0308, echo=FALSE------------------------------------------
par(op)

## ----fdfpopfinal--------------------------------------------------
## Check final pop size corresponds to birth = death 
K <- 5000/(exp(1) - 1) 
K
log1p(4290/K)

## ----lotka1-------------------------------------------------------
G_fe_LV <- function(r1, r2, K1, K2, a_12, a_21, awt = 1e-4,
                                 gt = c("WT", "S1", "S2")) {
    data.frame(Genotype = gt,
               Fitness = c(
                  paste0("max(0.1, 1 - ", awt, " * (n_2 + n_1))"),
                  paste0("1 + ", r1,
                         " * ( 1 - (n_1 + ", a_12, " * n_2)/", K1,
                         ")"),
                  paste0("1 + ", r2,
                         " * ( 1 - (n_2 + ", a_21, " * n_1)/", K2,
                         ")")
                  ))
}

## Show expressions for birth rates
G_fe_LV("r1", "r2", "K1", "K2", "a_12", "a_21", "awt")


## ----rzx0309, echo = FALSE----------------------------------------
set.seed(1)

## ----complv1, message=FALSE---------------------------------------
fe_competition <-
    allFitnessEffects(
        genotFitness =
            G_fe_LV(1.5, 1.4, 10000, 4000, 0.6, 0.2,
                  gt = c("WT","S1", "S2")),
        frequencyDependentFitness = TRUE,
        frequencyType = "abs")

competition <- oncoSimulIndiv(fe_competition,
                            model = "Exp",
                            onlyCancer = FALSE, 
                            finalTime = 100,
                            mu = 1e-4,
                            initSize = 40000, 
                            keepPhylog = FALSE,
                            seed = NULL, 
                            errorHitMaxTries = FALSE, 
                            errorHitWallTime = FALSE)

## ----pp1wt--------------------------------------------------------
plot(competition, show = "genotypes")

## ----rzx0310, echo=FALSE------------------------------------------
op <- par(mfrow = c(1, 2))

## ----pp1nowt------------------------------------------------------
plot(competition, show = "genotypes",
     xlim = c(80, 100))
plot(competition, show = "genotypes", type = "line",
     xlim = c(80, 100), ylim = c(1500, 12000))

## ----rzx0311, echo=FALSE------------------------------------------
par(op)	 

## ----lotka1multi--------------------------------------------------
G_fe_LVm <- function(r1, r2, K1, K2, a_12, a_21, awt = 1e-4,
                                 gt = c("S1", "S2")) {
    data.frame(Genotype = gt,
               Fitness = c(
                  paste0("1 + ", r1,
                         " * ( 1 - (n_1 + ", a_12, " * n_2)/", K1,
                         ")"),
                  paste0("1 + ", r2,
                         " * ( 1 - (n_2 + ", a_21, " * n_1)/", K2,
                         ")")
                  ))
}

## Show expressions for birth rates
G_fe_LVm("r1", "r2", "K1", "K2", "a_12", "a_21", "awt")


## ----rzx0312, echo = FALSE----------------------------------------
set.seed(1)

## ----complv1multi, message=FALSE----------------------------------
fe_competitionm <-
    allFitnessEffects(
        genotFitness =
            G_fe_LVm(1.5, 1.4, 10000, 4000, 0.6, 0.2,
                  gt = c("S1", "S2")),
        frequencyDependentFitness = TRUE)

fe_competitionm$full_FDF_spec

competitionm <- oncoSimulIndiv(fe_competitionm,
                              model = "Exp",
                              initMutant = c("S1", "S2"),
                              initSize = c(5000, 2000), 
                              onlyCancer = FALSE, 
                              finalTime = 100,
                              mu = 1e-4,
                              keepPhylog = FALSE,
                              seed = NULL, 
                              errorHitMaxTries = FALSE, 
                              errorHitWallTime = FALSE)

## ----pp1wtmulti---------------------------------------------------
plot(competitionm, show = "genotypes")

## ----ppreydefvm2--------------------------------------------------
G_fe_LVm2 <- function(r1, r2, K1, K2, a_12, a_21, awt = 1e-4,
                                 gt = c("S1", "S2")) {
    data.frame(Genotype = gt,
               Fitness = c(
                   paste0("1 + ", r1,
                         " * ( 1 - (n_", gt[1], " + ", a_12, " * n_", gt[2], ")/", K1,
                         ")"),
                  paste0("1 + ", r2,
                         " * ( 1 - (n_", gt[2], " + ", a_21, " * n_", gt[1], ")/", K2,
                         ")")
                  ))
}


## But notice that, because of ordering, "prey" ends up being n_2
## but that is not a problem.

fe_pred_preym2 <-
    allFitnessEffects(
        genotFitness =
            G_fe_LVm2(1.5, 1.4, 10000, 4000, 1.1, -0.5, awt = 1,
                  gt = c("prey", "Predator")),
        frequencyDependentFitness = TRUE)
fe_pred_preym2$full_FDF_spec

## Change order and note how these are, of course, equivalent

fe_pred_preym3 <-
    allFitnessEffects(
        genotFitness =
            G_fe_LVm2(1.4, 1.5, 4000, 10000, -0.5, 1.1, awt = 1,
                  gt = c("Predator", "prey")),
        frequencyDependentFitness = TRUE)
fe_pred_preym3$full_FDF_spec

evalAllGenotypes(fe_pred_preym2, spPopSizes = c(1000, 300))
evalAllGenotypes(fe_pred_preym3, spPopSizes = c(300, 1000))

## ----rzx0313, echo=FALSE------------------------------------------
set.seed(1)

## ----pprey3-------------------------------------------------------
s_pred_preym2 <- oncoSimulIndiv(fe_pred_preym2,
                                model = "Exp",
                                initMutant = c("prey", "Predator"),
                                initSize = c(1000, 1000), 
                                onlyCancer = FALSE, 
                                finalTime = 200,
                                mu = 1e-3,
                                keepPhylog = FALSE,
                                seed = NULL, 
                                errorHitMaxTries = FALSE, 
                                errorHitWallTime = FALSE)

## ----prepreylv1237------------------------------------------------
plot(s_pred_preym2, show = "genotypes")

## ----prepreylv1238------------------------------------------------
evalAllGenotypes(fe_pred_preym2, spPopSizes = c(0, 300))

evalAllGenotypes(allFitnessEffects(
    genotFitness =
        G_fe_LVm2(1.5, 1.4, 100, 40,
                  0.6, -0.5, awt = 0.1,
                  gt = c("prey", "Predator")),
    frequencyDependentFitness = TRUE),
    spPopSizes = c(0, 40))


## ----predprey2a---------------------------------------------------
C_fe_pred_prey2 <- function(r, a, c, e, d,
                           gt = c("s1", "s2")) {
    data.frame(Genotype = gt,
               Fitness = c(
                   paste0("1 + ", r, " - ", a,
                          " * ", c, " * n_2"),
                   paste0("1 + ", e, " * ", a,
                          " * ", c, " * n_1 - ", d)
               ))
}

C_fe_pred_prey2("r", "a", "c", "e", "d")


## ----echo=FALSE---------------------------------------------------
set.seed(2)

## ----predprey2b---------------------------------------------------
fe_pred_prey2 <-
    allFitnessEffects(
        genotFitness =
            C_fe_pred_prey2(r = .7, a = 1, c = 0.005,
                            e = 0.02, d = 0.4,
                            gt = c("Fly", "Lizard")),
        frequencyDependentFitness = TRUE)


fe_pred_prey2$full_FDF_spec

## You want to make sure you start the simulation from
## a viable condition

evalAllGenotypes(fe_pred_prey2,
                 spPopSizes = c(5000, 100))

set.seed(2)
pred_prey2 <- oncoSimulIndiv(fe_pred_prey2,
                             model = "Exp",
                             initMutant = c("Fly", "Lizard"),
                             initSize = c(500, 100),
                             sampleEvery = 0.1,
                             mu = 1e-3,
                             onlyCancer = FALSE, 
                             finalTime = 100,
                             keepPhylog = FALSE,
                             seed = NULL, 
                             errorHitMaxTries = FALSE, 
                             errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
## Nicer colors
plot(pred_prey2, show = "genotypes")
## But this shows better what is going on
plot(pred_prey2, show = "genotypes", type = "line")
par(op)


## ----commens, eval=FALSE------------------------------------------
#  fe_commens <-
#      allFitnessEffects(
#          genotFitness =
#              G_fe_LV(1.2, 1.3, 5000, 20000,
#                                   0, -0.2,
#                                   gt = c("WT","A", "Commensal")),
#          frequencyDependentFitness = TRUE,
#          frequencyType = "abs")
#  
#  commens <- oncoSimulIndiv(fe_commens,
#                              model = "Exp",
#                              onlyCancer = FALSE,
#                              finalTime = 100,
#                              mu = 1e-4,
#                              initSize = 40000,
#                            keepPhylog = FALSE,
#                            seed = NULL,
#                              errorHitMaxTries = FALSE,
#                              errorHitWallTime = FALSE)
#  
#  plot(commens, show = "genotypes")
#  
#  plot(commens, show = "genotypes",
#       xlim = c(80, 100))
#  
#  plot(commens, show = "genotypes", type = "line",
#       xlim = c(80, 100), ylim = c(2000, 22000))
#  

## ----fdfar, message=FALSE-----------------------------------------
rar <- data.frame(Genotype = c("WT", "A", "B", "C"), 
                 Fitness = c("1",
                             "1.1 + .3*f_2",
                             "1.2 + .4*f_1",
                             "1.0 + .5 * (f_1 + f_2)"))
afear <- allFitnessEffects(genotFitness = rar, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

evalAllGenotypes(afear, spPopSizes = c(100, 200, 300, 400))


rar2 <- data.frame(Genotype = c("WT", "A", "B", "C"), 
                 Fitness = c("1",
                             "1.1 + .3*(n_2/N)",
                             "1.2 + .4*(n_1/N)",
                             "1.0 + .5 * ((n_1 + n_2)/N)"))
afear2 <- allFitnessEffects(genotFitness = rar2, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "abs")
                         
evalAllGenotypes(afear2, spPopSizes = c(100, 200, 300, 400))

## ----relarres, message=FALSE--------------------------------------
set.seed(1)
tmp1 <- oncoSimulIndiv(afear, 
                       model = "McFL", 
                       onlyCancer = FALSE, 
                       finalTime = 30,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE)

set.seed(1)
tmp2 <- oncoSimulIndiv(afear2, 
                       model = "McFL", 
                       onlyCancer = FALSE, 
                       finalTime = 30,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE)
stopifnot(identical(print(tmp1), print(tmp2)))

## ----relar3a, message=FALSE---------------------------------------
rar3 <- data.frame(Genotype = c("WT", "A", "B", "C"), 
                 Fitness = c("1",
                             "1.1 + .3*(n_2/N)",
                             "1.2 + .4*(n_1/N)",
                             "1.0 + .5 * ( n_1 > 20)"))
afear3 <- allFitnessEffects(genotFitness = rar3, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "abs")
                         
evalAllGenotypes(afear3, spPopSizes = c(100, 200, 300, 400))


set.seed(1)
tmp3 <- oncoSimulIndiv(afear3, 
                       model = "McFL", 
                       onlyCancer = FALSE, 
                       finalTime = 60,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE)
plot(tmp3, show = "genotypes")

## ----fdfmutex-----------------------------------------------------
## Relative
r1fd <- data.frame(Genotype = c("WT", "A", "B", "A, B"), 
                 Fitness = c("1",
                             "1.4 + 1*(f_2)",
                             "1.4 + 1*(f_1)",
                             "1.6 + f_1 + f_2"))
afe4 <- allFitnessEffects(genotFitness = r1fd, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

mtfd <- allMutatorEffects(epistasis = c("A" = 0.1,
                                        "B" = 10))

## ----fdfmutex1----------------------------------------------------
set.seed(1)
s1fd <- oncoSimulIndiv(afe4, 
                     model = "McFL", 
                     onlyCancer = FALSE, 
                     finalTime = 40,
                     mu = 1e-4,
                     initSize = 5000, 
                     keepPhylog = TRUE,
                     seed = NULL, 
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE)
plot(s1fd, show = "genotypes")

## ----fdfmutex2----------------------------------------------------
set.seed(1)
s2fd <- oncoSimulIndiv(afe4,
                     muEF = mtfd,
                     model = "McFL", 
                     onlyCancer = FALSE, 
                     finalTime = 40,
                     mu = 1e-4,
                     initSize = 5000, 
                     keepPhylog = TRUE,
                     seed = NULL, 
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE)

## In the Mac ARM64 architecture, the above
## run leads to an exception, which is really odd.
## While that is debugged, use try to prevent
## failure of the plot to abort vignette building.

try(plot(s2fd, show = "genotypes"))

## ----echo=FALSE---------------------------------------------------
op <- par(mfrow = c(1, 2))

## ----figmutfdf----------------------------------------------------
plotClonePhylog(s1fd, keepEvents = TRUE)
plotClonePhylog(s2fd, keepEvents = TRUE)

## ----echo=FALSE---------------------------------------------------
par(op)

## ----exmutfdf2, eval=FALSE----------------------------------------
#  
#  ## Absolute
#  r5 <- data.frame(Genotype = c("WT", "A", "B", "A, B"),
#                   Fitness = c("1",
#                               "1.25 - .0025*(n_2)",
#                               "1.25 - .0025*(n_1)",
#                               "1.4"),
#                   stringsAsFactors = FALSE)
#  
#  afe5 <- allFitnessEffects(genotFitness = r5,
#                           frequencyDependentFitness = TRUE,
#                           frequencyType = "abs")
#  set.seed(8)
#  s5 <- oncoSimulIndiv(afe5,
#                       model = "McFL",
#                       onlyCancer = FALSE,
#                       finalTime = 100,
#                       mu = 1e-4,
#                       initSize = 5000,
#                       keepPhylog = TRUE,
#                       seed = NULL,
#                       errorHitMaxTries = FALSE,
#                       errorHitWallTime = FALSE)
#  plot(s5, show = "genotypes")
#  plot(s5, show = "genotypes", log = "y", type = "line")
#  
#  mt <- allMutatorEffects(epistasis = c("A" = 0.1,
#                                        "B" = 10))
#  set.seed(8)
#  s6 <- oncoSimulIndiv(afe5,
#                       muEF = mt,
#                       model = "McFL",
#                       onlyCancer = FALSE,
#                       finalTime = 100,
#                       mu = 1e-4,
#                       initSize = 5000,
#                       keepPhylog = TRUE,
#                       seed = NULL,
#                       errorHitMaxTries = FALSE,
#                       errorHitWallTime = FALSE)
#  plot(s6, show = "genotypes")
#  plot(s6, show = "genotypes", log = "y", type = "line")
#  
#  plotClonePhylog(s5, keepEvents = TRUE)
#  plotClonePhylog(s6, keepEvents = TRUE)
#  

## ----noworkeval---------------------------------------------------

evalAllGenotypes(allFitnessEffects(genotFitness = r1fd, 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(10, 20, 30, 40))


evalAllGenotypesFitAndMut(allFitnessEffects(genotFitness = r1fd, 
                                   frequencyDependentFitness = TRUE,
                                            frequencyType = "rel"),
                          mtfd,
                          spPopSizes = c(10, 20, 30, 40))

## ----rps1,  message=F---------------------------------------------
crs <- function (a, b, c){
  data.frame(Genotype = c("WT", "C", "R"),
             Fitness = c(paste0("1 + ", a, " * f_R - ", b, " * f_C"),
                         paste0("1 + ", b, " * f_ - ", c, " * f_R"),
                         paste0("1 + ", c, " * f_C - ", a, " * f_")
             ))
}

## ----rps2, message=F----------------------------------------------
crs("a", "b", "c")

## ----sps3b, message=F---------------------------------------------
afcrs1 <- allFitnessEffects(genotFitness = crs(1, 1, 1), 
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")

resultscrs1 <- oncoSimulIndiv(afcrs1,
                             model = "McFL", 
                             onlyCancer = FALSE,
                             finalTime = 100, 
                             mu = 1e-2,
                             initSize = 4000, 
                             keepPhylog = FALSE,
                             seed = NULL,
                             errorHitMaxTries = FALSE,
                             errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
plot(resultscrs1, show = "genotypes", type = "line", cex.lab=1.1,
     las = 1)
plot(resultscrs1, show = "genotypes", type = "stacked")
par(op)	 

## ----rps4, message=F----------------------------------------------
afcrs2 <- allFitnessEffects(genotFitness = crs(10, 1, 1), 
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")



## ----rps4b, eval=FALSE--------------------------------------------
#  resultscrs2 <- oncoSimulPop(10,
#                             afcrs2,
#                               model = "McFL",
#                               onlyCancer = FALSE,
#                               finalTime = 100,
#                               mu = 1e-2,
#                               initSize = 4000,
#                               keepPhylog = FALSE,
#                               seed = NULL,
#                               errorHitMaxTries = FALSE,
#                               errorHitWallTime = FALSE)

## ----rps5, message=F----------------------------------------------
set.seed(1)

resultscrs2a <- oncoSimulIndiv(afcrs2,
                             model = "McFL", 
                             onlyCancer = FALSE,
                             finalTime = 100, 
                             mu = 1e-2,
                             initSize = 4000, 
                             keepPhylog = FALSE,
                             seed = NULL,
                             errorHitMaxTries = FALSE,
                             errorHitWallTime = FALSE)

plot(resultscrs2a, show = "genotypes", type = "line")

## ----rps6, message=F----------------------------------------------
set.seed(3)

resultscrs2b <- oncoSimulIndiv(afcrs2,
                             model = "McFL", 
                             onlyCancer = FALSE,
                             finalTime = 60, 
                             mu = 1e-2,
                             initSize = 4000, 
                             keepPhylog = FALSE,
                             seed = NULL,
                             errorHitMaxTries = FALSE,
                             errorHitWallTime = FALSE)

plot(resultscrs2b, show = "genotypes", type = "line", cex.lab=1.1,
     las = 1)

## ----rps7, message=F----------------------------------------------
afcrs3 <- allFitnessEffects(genotFitness = crs(1, 5, 5), 
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")

resultscrs3 <- oncoSimulIndiv(afcrs3,
                             model = "McFL", 
                             onlyCancer = FALSE,
                             finalTime = 60, 
                             mu = 1e-2,
                             initSize = 4000, 
                             keepPhylog = FALSE,
                             seed = NULL,
                             errorHitMaxTries = FALSE,
                             errorHitWallTime = FALSE)

plot(resultscrs3, show = "genotypes", type = "line", cex.lab=1.1,
     las = 1)

## ----hkdv, message=F----------------------------------------------
## Stablish Genotype-Fitnees mapping. D = Dove, H = Hawk

## With newer OncoSimulR functionality, using WT to start the simulation
## would no longer be needed.
H_D_fitness <- function(c, v,
                    gt = c("WT", "H", "D")) {
  data.frame(Genotype = gt,
             Fitness = c(
               paste0("1"),
               paste0("1 + f_H *", (v-c)/2, "+ f_D *", v),
               paste0("1 + f_D *", v/2)))
}

## Fitness Effects specification
HD_competition <-allFitnessEffects(
  genotFitness = H_D_fitness(10, 2, 
                         gt = c("WT", "H", "D")),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

## Plot fitness landscape of genotype "H, D" evaluation
data.frame("Doves_fitness" = evalGenotype(genotype = "D",
                                          fitnessEffects = HD_competition,
                                          spPopSizes = c(5000, 5000, 5000)), 
           "Hawks_fitness" = evalGenotype(genotype = "H", 
                                          fitnessEffects = HD_competition,
                                          spPopSizes = c(5000, 5000, 5000))
           )

## ----hkdv2, message=F---------------------------------------------
## Simulated trajectories
## run only a few for the sake of speed
simulation <- oncoSimulPop(2,
                           mc.cores = 2,
                           HD_competition,
                           model = "McFL", # There is no collapse
                           onlyCancer = FALSE,
                           finalTime = 50,
                           mu = 1e-2, # Quick emergence of D and H
                           initSize = 4000,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)



## Plot first trajectory as an example
plot(simulation[[1]], show = "genotypes", type = "line", 
    xlim = c(40, 50),
     lwdClone = 2, ylab = "Number of individuals", 
     main = "Hawk and Dove trajectory",  
     col = c("#a37acc", "#f8776d", "#7daf00"),
     font.main=2, font.lab=2,
     cex.main=1.4, cex.lab=1.1,
     las = 1)


## ----hkdvx45------------------------------------------------------
## Recover the final result from first simulation
result <- tail(simulation[[1]][[1]], 1)

## Get the number of organisms from each species
n_WT <- result[2]
n_D <- result[3]
n_H <- result[4]
total <- n_WT + n_D + n_H

## Dove percentage
data.frame("Doves" = round(n_D/total, 2)*100, 
           "Hawks" = round(n_H/total, 2)*100  )

## ----glvop1, message=FALSE----------------------------------------

# Definition of the function for creating the corresponding dataframe.
avc <- function (a, v, c) {
  data.frame(Genotype = c("WT", "GLY", "VOP", "DEF"),
             Fitness = c("1",
                         paste0("1 + ",a," * (f_GLY + 1)"),
                         paste0("1 + ",a," * f_GLY + ",v," * (f_VOP + 1) - ",c),
                         paste0("1 + ",a," * f_GLY + ",v," * f_VOP")
                         ))
                          }

# Specification of the different effects on fitness.
afavc <- allFitnessEffects(genotFitness = avc(2.5, 2, 1),
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")

## For real, you would probably want to run
## this multiple times with oncoSimulPop
simulation <- oncoSimulIndiv(afavc,
                           model = "McFL",
                           onlyCancer = FALSE,
                           finalTime = 15,
                           mu = 1e-3,
                           initSize = 4000,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)


## ----glvop2, message=FALSE----------------------------------------

# Representation of the plot of one simulation as an example (the others are
# highly similar).
plot(simulation, show = "genotypes", type = "line",
     ylab = "Number of individuals", main = "Fully glycolytic tumours",
     font.main=2, font.lab=2, cex.main=1.4, cex.lab=1.1, las = 1)


## ----glvop3, message=FALSE----------------------------------------

# Definition of the function for creating the corresponding dataframe.
avc <- function (a, v, c) {
  data.frame(Genotype = c("WT", "GLY", "VOP", "DEF"),
             Fitness = c("1",
                         paste0("1 + ",a," * (f_GLY + 1)"),
                         paste0("1 + ",a," * f_GLY + ",v, " * (f_VOP + 1) - ",c),
                         paste0("1 + ",a," * f_GLY + ",v, " * f_VOP")
                         ))
                          }

# Specification of the different effects on fitness.
afavc <- allFitnessEffects(genotFitness = avc(2.5, 7, 1),
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")

simulation <- oncoSimulIndiv(afavc,
                           model = "McFL",
                           onlyCancer = FALSE,
                           finalTime = 15,
                           mu = 1e-4,
                           initSize = 4000,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)


## ----glvop4, message=FALSE----------------------------------------
## We get a huge number of VOP very quickly
## (too quickly?)
plot(simulation, show = "genotypes", type = "line",
     ylab = "Number of individuals", main = "Fully angiogenic tumours",
     font.main=2, font.lab=2, cex.main=1.4, cex.lab=1.1, las = 1)


## ----glvop5, message=FALSE----------------------------------------

# Definition of the function for creating the corresponding dataframe.
avc <- function (a, v, c) {
  data.frame(Genotype = c("WT", "GLY", "VOP", "DEF"),
             Fitness = c("1",
                         paste0("1 + ",a," * (f_GLY + 1)"),
                         paste0("1 + ",a," * f_GLY + ",v," * (f_VOP + 1) - ",c),
                         paste0("1 + ",a," * f_GLY + ",v," * f_VOP")
                         ))
                          }

# Specification of the different effects on fitness.
afavc <- allFitnessEffects(genotFitness = avc(7.5, 2, 1),
                           frequencyDependentFitness = TRUE,
                           frequencyType = "rel")

# Launching of the simulation (20 times).
simulation <- oncoSimulIndiv(afavc,
                           model = "McFL",
                           onlyCancer = FALSE,
                           finalTime = 25,
                           mu = 1e-4,
                           initSize = 4000,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)


## ----glvop6, message=FALSE----------------------------------------

# Representation of the plot of one simulation as an example (the others are
# highly similar).
plot(simulation, show = "genotypes", type = "line",
     ylab = "Number of individuals", main = "Heterogeneous tumours",
     font.main=2, font.lab=2, cex.main=1.4, cex.lab=1.1, las = 1)


## ----example5_fitness, message=FALSE------------------------------
fitness_rel <- function(a, b, r, g, gt = c("WT", "S", "I", "D")) {
    data.frame(
      Genotype = gt,
      Fitness = c("1",
                  paste0("1 + ", a, " * f_D"),
                  paste0("1 + 1 - ", g),
                  paste0("1 + (1 - f_I - f_D) * (1 - ", b, " + ", 
                         a, ") + f_I * (1 - ", b, " + ", r,
                         ") + f_D * (1 - 2 * ", b, ") + 1 - ", b, 
                         " + ", a, " + f_I * (", r, " - ",
                         a, ") - f_D * (", b, " + ", a, ")"))
                  )
}

## ----example5scen1, message=FALSE---------------------------------
scen1 <- allFitnessEffects(genotFitness = fitness_rel(a = 0.5, b = 0.7,
                                                     r = 0.1, g = 0.8),
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")

set.seed(1)
simulScen1 <- oncoSimulIndiv(scen1,
                              model = "McFL",
                              onlyCancer = FALSE,
                              finalTime = 70,
                              mu = 1e-4,
                              initSize = 5000,
                              keepPhylog = FALSE,
                              seed = NULL,
                              errorHitMaxTries = FALSE,
                              errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
plot(simulScen1, show = "genotypes", type = "line",
     main = "First scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
plot(simulScen1, show = "genotypes",
     main = "First scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
par(op)

## ----example5scen2, message=FALSE---------------------------------
scen2 <- allFitnessEffects(genotFitness = fitness_rel(a = 0.3, b = 0.7,
                                                     r = 0.1, g = 0.7),
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")

set.seed(1)

simulScen2 <- oncoSimulIndiv(scen2,
                              model = "McFL",
                              onlyCancer = FALSE,
                              finalTime = 70,
                              mu = 1e-4,
                              initSize = 4000,
                              keepPhylog = FALSE,
                              seed = NULL,
                              errorHitMaxTries = FALSE,
                              errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
plot(simulScen2, show = "genotypes", type = "line",
     main = "Second scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
plot(simulScen2, show = "genotypes",
     main = "Second scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
par(op)

## ----example5scen3, message=FALSE---------------------------------
scen3 <- allFitnessEffects(genotFitness = fitness_rel(a = 0.2, b = 0.3,
                                                     r = 0.1, g = 0.3),
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")

set.seed(1)
simulScen3 <- oncoSimulIndiv(scen3,
                              model = "McFL",
                              onlyCancer = FALSE,
                              finalTime = 50,
                              mu = 1e-4,
                              initSize = 4000,
                             keepPhylog = FALSE,
                             seed = NULL,
                              errorHitMaxTries = FALSE,
                              errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
plot(simulScen3, show = "genotypes", type = "line",
     main = "Third scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
plot(simulScen3, show = "genotypes",
     main = "Third scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
par(op)

## ----example5scen4, message=FALSE---------------------------------
scen4 <- allFitnessEffects(genotFitness = fitness_rel(a = 0.2, b = 0.4,
                                                     r = 0.1, g = 0.3),
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")

## Set a different seed to show the results better since
## with set.seed(1) the progression of I cells was not shown
set.seed(2)

simulScen4 <- oncoSimulIndiv(scen4,
                              model = "McFL",
                              onlyCancer = FALSE,
                              finalTime = 40,
                              mu = 1e-4,
                              initSize = 4000,
                             keepPhylog = FALSE,
                             seed = NULL,
                              errorHitMaxTries = FALSE,
                              errorHitWallTime = FALSE)
op <- par(mfrow = c(1, 2))
plot(simulScen4, show = "genotypes", type = "line",
     main = "Fourth scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
plot(simulScen4, show = "genotypes",
     main = "Fourth scenario",
     cex.main = 1.4, cex.lab = 1.1,
     las = 1)
par(op)

## ----smyeloma, eval=TRUE,echo=FALSE, fig.cap="Multiplication factor in myeloma interaction. Table 1 in Sartakhti et al., 2016, 'Evolutionary Dynamics of Tumor-Stroma Interactions in Multiple Myeloma' https://doi.org/10.1371/journal.pone.0168856 ."----
knitr::include_graphics("Myeloma_interaction.png")

## ----smyelo56j----------------------------------------------------
f_cells <- function(c1, c2, c3, r11, r12, r13, 
                    r21, r22, r23, r31, r32, r33, M, awt = 1e-4,
                                 gt = c("WT", "OC", "OB", "MM")) {
    data.frame(Genotype = gt,
               Fitness = c(
                  paste0("max(0.1, 1 - ", awt, " * (f_OC + f_OB+f_MM)*N)"),
                  paste0("1", "+(((f_OC * (", M, "-1)+1)*", c1, ")/", M, ")*",r11,
                          "+((((1-f_MM) * (", M, "-1)-f_OC*(", M, "-1)-1)*", c2, ")/", M, ")*", r12,
                          "+(((", M, "-(1-f_MM)*(", M, "-1))*", c3, ")/", M, ")*", r13,
                          "-", c1
                          ),
                  paste0("1", "+(((f_OB*(", M, "-1)+1)*", c2, ")/", M, ")*", r22,
                          "+((((1-f_OC)*(", M, "-1)-f_OB*(", M, "-1)-1)*", c3, ")/", M, ")*", r23,
                          "+(((", M, "-(1-f_OC)*(", M, "-1))*", c1, ")/", M, ")*", r21,
                          "-", c2
                          ),
                  paste0("1", "+(((f_MM*(", M, "-1)+1)*", c3, ")/", M, ")*", r33,
                          "+((((1-f_OB)*(", M, "-1)-f_MM*(", M, "-1)-1)*", c1, ")/", M, ")*", r31,
                          "+(((", M, "-(1-f_OB)*(", M, "-1))*", c2, ")/", M, ")*", r32,
                          "-", c3
                          )
                  )
               ,stringsAsFactors = FALSE
               )
}


## ----smyelo3v57---------------------------------------------------
N <- 40000
M <- 10
c1 <- 1
c2 <- 1.2
c3 <- 1.4
r11 <- 0
r12 <- 1
r13 <- 2.5
r21 <- 1
r22 <- 0
r23 <- -0.3
r31 <- 2.5
r32 <- 0
r33 <- 0

fe_cells <-
    allFitnessEffects(
        genotFitness =
            f_cells(c1, c2, c3, r11, r12, r13, 
                    r21, r22, r23, r31, r32, r33, M,
                    gt = c("WT", "OC", "OB", "MM")),
        frequencyDependentFitness = TRUE,
        frequencyType = "rel")

## Simulated trajectories

## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below
data(smyelo3v57)

if (FALSE) {
set.seed(2)
smyelo3v57 <- oncoSimulIndiv(fe_cells,
                           model = "McFL",
                           onlyCancer = FALSE,
                           finalTime = 20, 
                           mu = c("OC"=1e-1, "OB"=1e-1, "MM"=1e-4),
                           initSize = N,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE,
						   keepEvery = 0.1)
}
## Plot trajectories
plot(smyelo3v57, show = "genotypes")



## ----smyeloc1v23--------------------------------------------------
N <- 40000
M <- 10
c1 <- 1
c2 <- 1
c3 <- 1
r11 <- 0
r12 <- 1
r13 <- 0.5
r21 <- 1
r22 <- 0
r23 <- -0.3
r31 <- 0.5
r32 <- 0
r33 <- 0

fe_cells <-
    allFitnessEffects(
        genotFitness =
            f_cells(c1, c2, c3, r11, r12, r13, 
                    r21, r22, r23, r31, r32, r33, M,
                    gt = c("WT", "OC", "OB", "MM")),
        frequencyDependentFitness = TRUE,
        frequencyType = "rel")

## Simulated trajectories
set.seed(1)
simulation <- oncoSimulIndiv(fe_cells,
                           model = "McFL",
                           onlyCancer = FALSE,
                           finalTime = 15, ## 25
                           mu = c("OC"=1e-1, "OB"=1e-1, "MM"=1e-4),
                           initSize = N,
                           keepPhylog = FALSE,
                           seed = NULL,
                           errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)

#Plot trajectorie
plot(simulation, show = "genotypes", thinData = TRUE)


## ----parka , message=FALSE----------------------------------------
park1<- data.frame(Genotype = c("WT", "A", "B", "A,B"), 
                 Fitness = c("1",
			     "1 + 3*(f_1 + f_2 + f_1_2)",
                 "1 + 2*(f_1 + f_2 + f_1_2)", ## We establish
                 ## the fitness of B smaller than the one of A because
                 ## it is an indirect cause of the disease and not a direct one. 
                 "1.5 + 4.5*(f_1 + f_2 + f_1_2)")) ## The baseline
                  ## of the fitness is higher in the
                  ## AB population (their growth is favored).

parkgen1<- allFitnessEffects(genotFitness = park1, 
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel") 

## ----parkb--------------------------------------------------------
set.seed(1)
fpark1 <- oncoSimulIndiv(parkgen1, 
                     model = "McFL",
                     onlyCancer = FALSE,
                     finalTime = 100,
                     mu = 1e-4,
                     initSize = 5000,
                     keepPhylog = TRUE,
                     seed = NULL,
                     errorHitMaxTries = FALSE, 
                     errorHitWallTime = FALSE)



## ----parkplot1, message=FALSE-------------------------------------
plot(fpark1, show = "genotypes",  type = "line",
     col = c("black", "green", "red", "blue"))

## ----parkplot2, message=FALSE-------------------------------------
plotClonePhylog(fpark1, N = 0, keepEvents=TRUE, timeEvents=TRUE)

## ----wuAMicrobesfit1----------------------------------------------
create_fe <- function(bG, cG, iPA, cI, cS, bPA, ab,
                      gt = c("WT", "CT", "PA")) {
  data.frame(Genotype = gt,
             Fitness = c(
               paste0("1 + ", bG, " * (f_ + f_CT) - ", cS, 
                      " * (f_ +  f_CT + f_PA) - ", cI, "(f_PA > 0.2) - ", cG, 
                      " - ", ab),
               paste0("1 + ", bG, " * (f_ + f_CT) - ", cS, 
                      " * (f_ +  f_CT + f_PA) - ", cG),
               paste0("1 +", bPA, " - ", cS, " * (f_ +  f_CT + f_PA) - ", iPA,
                      " *(f_(f_PA > 0.2))")),
             stringsAsFactors = FALSE)
}


## ----wuAMicrobescheck---------------------------------------------
create_fe("bG", "cG", "iPA", "cI", "cS", "bPA", "ab")

## ----wuAMicrobes2a------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(7, 1, 9, 0.5, 2, 5, 0), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(1000, 1000, 1000))


## ----wuAMicrobes2b------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(7, 1, 9, 0.5, 2, 5, 2), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(1000, 1000, 1000))


## ----wuAMicrobes2c------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(7, 1, 9, 0.5, 2, 5, 2), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(0, 0, 1000))


## ----wuAMicrobes2d------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(7, 1, 9, 0.5, 2, 5, 2), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(100, 0, 1000))


## ----wuAMicrobes2e------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(7, 1, 9, 0.5, 2, 5, 5), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(1000, 0, 0))


## ----wuAMicrobes2f------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
				   create_fe(7, 1, 9, 0.5, 2, 5, 5), 
				   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(1000, 0, 1000))


## ----woAntib1, eval = FALSE---------------------------------------
#  woAntib <- allFitnessEffects(
#    genotFitness = create_fe(7, 1, 9, 0.5, 2, 5, 0),
#    frequencyDependentFitness = TRUE,
#    frequencyType = "rel")
#  
#  ## We do not run this for speed but load it below
#  set.seed(2)
#  woAntibS <- oncoSimulIndiv(woAntib,
#                          model = "McFL",
#                          onlyCancer = FALSE,
#                          finalTime = 2000,
#                          mu = 1e-4,
#                          initSize = 1000,
#                          keepPhylog = FALSE,
#                          seed = NULL,
#                          errorHitMaxTries = FALSE,
#                          errorHitWallTime = FALSE,
#                          keepEvery = 2 ## store a smaller object
#                          )
#  

## ----woAntib1data-------------------------------------------------
## Load stored results
data(woAntibS)

## ----woAntib2-----------------------------------------------------

plot(woAntibS, show = "genotypes", type = "line",
     col = c("black", "green", "red"))


## ----wiAntib1-----------------------------------------------------
wiAntib <- allFitnessEffects(
  genotFitness = create_fe(7, 1, 9, 0.5, 2, 5, 2),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")


set.seed(2)
wiAntibS <- oncoSimulIndiv(wiAntib,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 100,
                        mu = 1e-4,
                        initSize = 1000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)

plot(wiAntibS, show = "genotypes", type = "line",
     col = c("black", "green", "red"))


## ----breastCfit1--------------------------------------------------
create_fe <- function(cG, bG, cS, cMMph, cMMTC, bR, cD,
                      gt = c("WT", "Mph", "BTC", "MTC")) {
  data.frame(Genotype = gt,
             Fitness = c(
               paste0("1 + ", bG, "*(f_ + f_Mph) - ", cG, " - ", cS, "*(f_ + f_BTC)"),
               paste0("1 + ", bG, "*(f_ + f_Mph) - ", cG, " - ", cMMph),
               paste0("1 + ", bR, " + ", bG, "*(f_ + f_Mph) - ", cS, "* (f_ + f_BTC) -",
                      cD , " * f_Mph"),
               paste0("1 + ", bR, " + ", bG, " *(f_ + f_Mph) -", cMMTC, " - ",
                      cD , " * f_Mph")
               ),
             stringsAsFactors = FALSE)
}


## ----breastCcheck-------------------------------------------------
create_fe("cG", "bG","cS", "cMMph", "cMMTC", "bR", "cD")

## ----breastC2a----------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(2, 5, 1, 0.8, 1, 1, 9), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(WT = 1000, Mph = 0, BTC = 0, MTC = 0))


## ----breastC2b----------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(2, 5, 1, 0.8, 1, 1, 9), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(WT = 1000, Mph = 1000, BTC = 0, MTC = 0))


## ----breastC2c----------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(2, 5, 1, 0.8, 1, 1, 9), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(WT = 1000, Mph = 1000, BTC = 100, MTC = 100))


## ----cancercontrol1-----------------------------------------------
afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(0.5, 4, 1, 0.2, 1, 0.5, 4),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

set.seed(2)

s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 50,
                        mu = 1e-4,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)

plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "yellow"))


## ----cancerNM1----------------------------------------------------
afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(1, 4, 0.5, 1, 1.5, 1, 4),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

set.seed(2)

s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 50,
                        mu = 1e-4,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)


plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "yellow"))


## ----cancerM1-----------------------------------------------------

afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(0.5, 4, 2, 0.5, 0.5, 1, 4),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

set.seed(2)

s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 50,
                        mu = 1e-4,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)


plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "yellow"))



## ----breastCQfit1-------------------------------------------------
create_fe <- function(cG, bG, cS, cMMph, cMMTC, bR, cD, Q,
                      gt = c("WT", "BTC", "R", "MTC", "Mph", "BTC,R", "MTC,R")) {
  data.frame(Genotype = gt,
             Fitness = c(
               
               paste0("1 + ", bG, "(f_ + f_R + f_Mph) - ", cG, " - ", cS, "(f_ + f_BTC + f_R + f_BTC_R) -",
                      "0.01*", Q),
               paste0("1 + ", bR, " + ", bG, "(f_ + f_R + f_Mph) - ", cS, " (f_ + f_BTC + f_R + f_BTC_R) -",
                      cD , " * f_Mph -", Q),
               paste0("1 + ", bG, "(f_ + f_R + f_Mph) - ", cG, " - ", cS, "(f_ + f_BTC + f_R + f_BTC_R)"),
               paste0("1 + ", bR, " + ", bG, " *(f_ + f_R + f_Mph) -", cMMTC, " - ",
                      cD , " * f_Mph -", Q),
               paste0("1 + ", bG, "(f_ + f_R + f_Mph) - ", cG, " - ", cMMph, "- 0.01*",Q),
               paste0("1 + ", bR, " + ", bG, "(f_ + f_R + f_Mph) - ", cS, " (f_ + f_BTC + f_R + f_BTC_R) -",
                      cD , " * f_Mph"),
               paste0("1 + ", bR, " + ", bG, " *(f_ + f_R + f_Mph) -", cMMTC, " - ",
                      cD , " * f_Mph")
             ),
             stringsAsFactors = FALSE)
}


## ----breastCQcheck------------------------------------------------
create_fe("cG", "bG","cS", "cMMph", "cMMTC", "bR", "cD", "Q")

## ----breastCQ2a---------------------------------------------------
evalAllGenotypes(allFitnessEffects(genotFitness =  
                                   create_fe(2,5,1,0.8,1,1,9,5), 
                                   frequencyDependentFitness = TRUE,
                                   frequencyType = "rel"),
                 spPopSizes = c(WT = 1000, BTC = 100, R = 0,
                                MTC = 100, Mph = 1000,
                                "BTC, R" = 0, "MTC, R" = 0))


## ----cancerwoQ1---------------------------------------------------

afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(2, 5, 1, 0.8, 1, 1, 9, 0),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

#Set mutation rates
muvar2 <- c("Mph" = 1e-2, "BTC" = 1e-3, "MTC"=1e-3, "R" = 1e-7)

set.seed(2)
s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 20,
                        mu = muvar2,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)


plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "pink", "orange", "brown"))


## ----cancerwQlRm1-------------------------------------------------

afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(2, 5, 1, 0.8, 1, 1, 9, 2),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

muvar2 <- c("Mph" = 1e-2, "BTC" = 1e-3, "MTC"=1e-3, "R" = 1e-7)

set.seed(2)

s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 20,
                        mu = muvar2,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)


plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "pink", "orange", "brown"))


## ----cancerwQHRm1-------------------------------------------------

afe_3_a <- allFitnessEffects(
  genotFitness =
    create_fe(2, 5, 1, 0.8, 1, 1, 9, 2),
  frequencyDependentFitness = TRUE,
  frequencyType = "rel")

muvar2 <- c("Mph" = 1e-2, "BTC" = 1e-3, "MTC"=1e-3, "R" = 1e-5)

set.seed(2)

s_3_a <- oncoSimulIndiv(afe_3_a,
                        model = "McFL", 
                        onlyCancer = FALSE, 
                        finalTime = 20, ## short for speed; increase for "real"
                        mu = muvar2,
                        initSize = 10000, 
                        keepPhylog = FALSE,
                        seed = NULL, 
                        errorHitMaxTries = FALSE, 
                        errorHitWallTime = FALSE)


plot(s_3_a, show = "genotypes", type = "line",
     col = c("black", "green", "red", "blue", "pink", "orange", "brown"))


## ----exampleBirthNom----------------------------------------------

m4 <- data.frame(Genotype = c("WT", "A", "B", "A, B"), Birth = c(1, 2, 3, 4))

fem4 <- allFitnessEffects(genotFitness = m4, frequencyDependentBirth = FALSE)

evalAllGenotypes(fem4)


## ----exampleDeath-------------------------------------------------
m4 <- data.frame(Genotype = c("WT", "A", "B", "A, B"),
                 Birth = c(1, 2, 3, 4),
                 Death = c(1, 2, 3, 4))

fem4 <- allFitnessEffects(genotFitness = m4,
                          frequencyDependentBirth = FALSE,
                          frequencyDependentDeath = FALSE,
                          deathSpec = TRUE)

evalAllGenotypes(fem4)


## ----exampleSimulDeath--------------------------------------------
G_fe_LVm <- function(r1, r2, K1, K2, a_12, a_21, gt = c("S1", "S2")) {
    data.frame(Genotype = gt,
               Birth = c(paste0(r1, "-", r1, "*(", a_12, "*n_", gt[2], ")/", K1), r2),
               Death = c(paste0(r1, "*(n_", gt[1], ")/", K1),
                         paste0(r2, "*(n_", gt[2], "+", a_21, "*n_", gt[1], ")/", K2)))
}
fe_pred_prey <- allFitnessEffects(
		genotFitness = G_fe_LVm(1.4, 1.5, 4000, 10000, -0.5, 1.1, gt = c("Predator", "Prey")),
		frequencyDependentBirth = TRUE, 
		frequencyDependentDeath = TRUE,
		deathSpec = TRUE)

s_pred_preym <- oncoSimulIndiv(fe_pred_prey, model = "Arb",
                                initMutant = c("Predator", "Prey"),
                                initSize = c(1000, 1000), 
                                onlyCancer = FALSE, 
                                finalTime = 75, mu = 1e-3,
                               keepPhylog = FALSE, seed = NULL,
                               errorHitMaxTries = FALSE,
                                errorHitWallTime = FALSE,
								keepEvery = 1)

plot(s_pred_preym, show="genotypes", type="line", log = "")


## ----exampleConst-------------------------------------------------
H_D_fitness <- function(c, v, gt = c("H", "D")) {
                  data.frame(Genotype = gt,
                             Birth = c(
                    			    paste0("max(1e-5, f_H *", (v-c)/2, "+ f_D *", v, ")"),
                              paste0("f_D *", v/2)))
}

HD_eq <- allFitnessEffects(
	             genotFitness = H_D_fitness(10, 4, gt = c("H", "D")),
	             frequencyDependentBirth = TRUE,
	              frequencyType = "rel")
	
osi_eq <- oncoSimulIndiv(HD_eq, model = "Const",
                           onlyCancer = FALSE, finalTime = 50,
                           mu = 1e-6, initSize = c(2000, 2000),
                         initMutant = c("H", "D"), keepPhylog = FALSE,
                         seed = NULL, errorHitMaxTries = FALSE,
                           errorHitWallTime = FALSE)
                           
plot(osi_eq, show="genotypes", ylim=c(1, 5000))

## ----interventionListExample--------------------------------------
interventions <- list(
    list(ID           = "i2",
        Trigger       = "(N > 1e6) & (T > 100)",
        WhatHappens   = "N = 0.001 * N",
        Repetitions   = 7,  
        Periodicity    = Inf
    ),
    list(ID           = "i1",
        Trigger       = "(T > 10)",
        WhatHappens   = "N = 0.3 * N",
        Periodicity   = 10,
        Repetitions   = 0
    ),
    list(ID           = "i3", 
        Trigger       = "(T > 1) & (T < 200)",
        WhatHappens   = "n_A = n_A * 0,3 / n_C",
        Repetitions   = Inf,  
        Periodicity    = 10
    ),
    list(ID           = "i5",
        Trigger       = "(N > 1e8) & (T> 1.2)",
        WhatHappens   = "n_A_B = n_B * 0,3 / n_SRL",
        Repetitions   = 0,   
        Periodicity    = Inf
    )
)


## ----interventionEscenario1Example--------------------------------
fa1 <- data.frame(Genotype = c("WT", "A", "B"),
                    Fitness = c("n_*0",
                                "1.5",
                                "1"))

afd3 <- allFitnessEffects(genotFitness = fa1,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "abs")

## ----interventionsCreateInterventionExample-----------------------
interventions <- createInterventions(interventions, afd3)

## ----interventionsOncoSimulIndivExample, eval=FALSE---------------
#  ep2 <- oncoSimulIndiv(
#      afd3,
#      model = "Exp",
#      mu = 1e-4,
#      sampleEvery = 0.001,
#      initSize = c(20000, 20000),
#      initMutant = c("A", "B"),
#      finalTime = 5.2,
#      onlyCancer = FALSE,
#      interventions = interventions
#  )

## ----interventionsSimpleExampleNoInts-----------------------------
df3x <- data.frame(Genotype = c("WT", "B", "A", "B, A", "C, A"),
                       Fitness = c("0*n_",
                                    "1.5",
                                    "1.002",
                                    "1.003",
                                    "1.004"))

afd3 <- allFitnessEffects(genotFitness = df3x,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "abs")

ex1 <- oncoSimulIndiv(
                    afd3, 
                    model = "McFLD",
                    mu = 1e-4,
                    sampleEvery = 0.01,
                    initSize = c(20000, 20000),
                    initMutant = c("A", "B"),
                    finalTime = 10,
                    onlyCancer = FALSE
                    )
    
plot(ex1, show="genotypes", type = "line")    

## ----create_interventionsSimpleExample----------------------------
interventions <- list(
    list(ID           = "intOverB",
        Trigger       = "(T >= 5)",
        WhatHappens   = "n_B = n_B * 0.88",
        Repetitions   = Inf,
        Periodicity   = 0.07
    ))

interventions <- createInterventions(interventions, afd3)

## ----interventionsSimpleExampleWithInts---------------------------

ex1_with_ints <- oncoSimulIndiv(
                    afd3, 
                    model = "McFLD",
                    mu = 1e-4,
                    sampleEvery = 0.01,
                    initSize = c(20000, 20000),
                    initMutant = c("A", "B"),
                    finalTime = 10,
                    onlyCancer = FALSE,
                    interventions = interventions)

plot(ex1_with_ints, show="genotypes", type = "line")  

## ----intex2-------------------------------------------------------
gffd3 <- data.frame(Genotype = c("WT", "A", "B"),
                    Fitness = c("1",
                    "1 + 0.25 * (n_B > 0)",
                    ".9 + 0.4 * (n_A > 0)"
                    ))
afd3 <- allFitnessEffects(genotFitness = gffd3,
                            frequencyDependentFitness = TRUE,
                            frequencyType = "abs")


## ----intex2load---------------------------------------------------
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below
data(osi)

if (FALSE) {
osi <- oncoSimulIndiv( afd3,
                        model = "McFLD",
                        onlyCancer = FALSE,
                        finalTime = 200,
                        mu = 1e-4,
                        initSize = 5000,
                        sampleEvery = 0.001,
						keepEvery = 1)
}
						

## ----plotintex2---------------------------------------------------
plot(osi, show = "genotypes", type = "line")

## ----intforintex2-------------------------------------------------
intervention_tot_pop = list(
        list(
            ID            = "intOverTotPop",
            Trigger       = "T > 40",
            WhatHappens   = "N = N * 0.2",
            Repetitions   = 2,   
            Periodicity   = 20
        )
    )

    intervention_tot_pop <- createInterventions(intervention_tot_pop, afd3)

## ----simulationwithinterventionsintex2----------------------------
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below
data(osi_with_ints)

if(FALSE) {
osi_with_ints <- oncoSimulIndiv( afd3,
                        model = "McFLD",
                        onlyCancer = FALSE,
                        finalTime = 200,
                        mu = 1e-4,
                        initSize = 5000,
                        sampleEvery = 0.001,
                        interventions = intervention_tot_pop)
}

plot(osi_with_ints, show = "genotypes", type = "line")

## ----popsbytimeintex2---------------------------------------------
osi_with_ints$pops.by.time[39:42, ]

## ----popsbytimeintex2_proof---------------------------------------
pre_int_tot_pop = osi_with_ints$PerSampleStats[40, 1]
post_int_tot_pop = osi_with_ints$PerSampleStats[41, 1]

## If you did not remember about PerSampleStats
## you could add all except the first column of pops.by.time
## to get the total population sizes.
percentage_eliminated = (post_int_tot_pop/pre_int_tot_pop)*100

paste0("The percentage of population has decreased by ", percentage_eliminated, "%")

## ----idif1, eval = FALSE------------------------------------------
#  
#  N = 0.2 * N
#  

## ----idif2, eval = FALSE------------------------------------------
#  
#  n_A = 0.2 * n_A
#  

## ----rpswithnoints------------------------------------------------
crs <- function (a, b, c){
        data.frame(Genotype = c("WT", "C", "R"),
        Fitness = c(paste0("1 + ", a, " * n_R/N - ", b, " * n_C/N"),
        paste0("1 + ", b, " * n_/N - ", c, " * n_R/N"),
        paste0("1 + ", c, " * n_C/N - ", a, " * n_/N")
        ))
    }

afcrs1 <- allFitnessEffects(genotFitness = crs(1, 1, 1),
frequencyDependentFitness = TRUE,
frequencyType = "abs")
    
resultscrs1_noints <- oncoSimulIndiv(afcrs1,
                            model = "McFL",
                            finalTime = 25,
                            mu = 1e-2,
                            initSize = 4000,
                            onlyCancer = FALSE,
                            keepPhylog = FALSE,
                            seed = NULL)

plot(resultscrs1_noints, show="genotypes", type = "line")


## ----interventionforrps-------------------------------------------
int_over_C <- list(
    list(
        ID = "Bothering R strain, by reducing C",
        Trigger = "n_C >= 500",
        WhatHappens = "n_C = n_C * 0.1",
        Periodicity = 3,
        Repetitions = Inf
    )
    )

final_int_over_C <-
    createInterventions(interventions = int_over_C,
                        genotFitness = afcrs1)

## ----rpswithints--------------------------------------------------
resultscrs1_noints <- oncoSimulIndiv(afcrs1,
                            model = "McFL",
                            finalTime = 25,
                            mu = 1e-2,
                            initSize = 4000,
                            onlyCancer = FALSE,
                            keepPhylog = FALSE,
                            seed = NULL,
                            interventions = final_int_over_C)

plot(resultscrs1_noints, show="genotypes", type = "line")


## ----userVarsListExample------------------------------------------
userVars <- list(
    list(Name           = "user_var1",
        Value       = 0
    ),
    list(Name           = "user_var2",
        Value       = 3
    ),
    list(Name           = "user_var3",
        Value       = 2.5
    )
)


## ----ruleListExample----------------------------------------------
rules <- list(
        list(ID = "rule_1",
            Condition = "T > 20",
            Action = "user_var_1 = 1"
        ),list(ID = "rule_2",
            Condition = "T > 30",
            Action = "user_var_2 = 2; user_var3 = 2*N"
        ),list(ID = "rule_3",
            Condition = "T > 40",
            Action = "user_var_3 = 3;user_var_2 = n_A*n_B"
        )
    )


## ----userVarsEscenario1Example------------------------------------
dfuv <- data.frame(Genotype = c("WT", "A", "B"),
                    Fitness = c("1",
                    "1 + 0.2 * (n_B > 0)",
                    ".9 + 0.4 * (n_A > 0)"
                    ))
afuv <- allFitnessEffects(genotFitness = dfuv,
                        frequencyDependentFitness = TRUE,
                        frequencyType = "abs")

## ----userVarsCreateUserVarsAndRulesExample------------------------
userVars <- createUserVars(userVars)
rules <- createRules(rules, afuv)

## ----userVarsOncoSimulIndivExample, eval=FALSE--------------------
#  
#  uvex <- oncoSimulIndiv(
#      afuv,
#      model = "McFLD",
#      mu = 1e-4,
#      sampleEvery = 0.001,
#      initSize = c(20000, 20000),
#      initMutant = c("A", "B"),
#      finalTime = 5.2,
#      onlyCancer = FALSE,
#      userVars = userVars,
#      rules = rules
#  )
#  

## ----userVarsBasicExample-----------------------------------------
dfuv2 <- data.frame(Genotype = c("WT", "B", "A", "B, A", "C, A"),
                       Fitness = c("0*n_",
                                    "1.5",
                                    "1.002",
                                    "1.003",
                                    "1.004"))

afuv2 <- allFitnessEffects(genotFitness = dfuv2,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "abs")

userVars <- list(
    list(Name           = "genAProp",
        Value       = 0.5
    ),
	list(Name           = "genBProp",
        Value       = 0.5
    ),
	list(Name           = "genABProp",
        Value       = 0.0
    ),
	list(Name           = "genACProp",
        Value       = 0.0
    )
)

userVars <- createUserVars(userVars)

rules <- list(
        list(ID = "rule_1",
            Condition = "TRUE",
            Action = "genBProp = n_B/N"
        ),
		list(ID = "rule_2",
            Condition = "TRUE",
            Action = "genAProp = n_A/N"
        ),
		list(ID = "rule_3",
            Condition = "TRUE",
            Action = "genABProp = n_A_B/N"
        ),
		list(ID = "rule_4",
            Condition = "TRUE",
            Action = "genACProp = n_A_C/N"
        )
    )

rules <- createRules(rules, afuv2)

## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below
data(uvex2)

if(FALSE) {
set.seed(1)
uvex2 <- oncoSimulIndiv(
                    afuv2, 
                    model = "McFLD",
                    mu = 1e-4,
                    sampleEvery = 0.01,
                    initSize = c(20000, 20000),
                    initMutant = c("A", "B"),
                    finalTime = 10,
                    onlyCancer = FALSE,
                    userVars = userVars,
                    rules = rules,
                    keepEvery = 0.1
                    )
}
                    
plot(
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex2$other$userVarValues) [c(TRUE, FALSE, FALSE, FALSE, FALSE)],
    xlab="Time", ylab="Proportion", ylim=c(0,1), type="l", col="purple")  

lines(
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex2$other$userVarValues) [c(FALSE, TRUE, FALSE, FALSE, FALSE)], type="l", col="#E6AB02") 

lines(
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, TRUE, FALSE, FALSE)], type="l", col="#1B9E77") 

lines(
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex2$other$userVarValues) [c(FALSE, FALSE, FALSE, TRUE, FALSE)], type="l", col="#666666") 

legend(0,1,
       legend=c("genABProp", "genACProp", "genAProp", "genBProp"), col=c("purple", "#E6AB02", "#1B9E77", "#666666"), lty= 1:2) 

## ----userVarsBasicExample2----------------------------------------
dfuv3 <- data.frame(Genotype = c("WT", "A", "B"), 
                   Fitness = c("1",
                               "1 + 0.2 * (n_B > 10)",
                               ".9 + 0.4 * (n_A > 10)"
                               ))
afuv3 <- allFitnessEffects(genotFitness = dfuv3, 
                         frequencyDependentFitness = TRUE)

userVars <- list(
    list(Name           = "genWTRateDiff",
        Value       = 0.5
    ),list(Name           = "genARateDiff",
        Value       = 0.5
    ),list(Name           = "genBRateDiff",
        Value       = 0.0
    )
)

userVars <- createUserVars(userVars)

rules <- list(
        list(ID = "rule_1",
            Condition = "TRUE",
            Action = "genWTRateDiff = b_-d_"
        ),list(ID = "rule_2",
            Condition = "TRUE",
            Action = "genARateDiff = b_1-d_1"
        ),list(ID = "rule_3",
            Condition = "TRUE",
            Action = "genBRateDiff = b_2-d_2"
        )
    )

rules <- createRules(rules, afuv3)


## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(uvex3)

if(FALSE) {
set.seed(1)

uvex3 <- oncoSimulIndiv(afuv3,
                       model = "McFLD", 
                       onlyCancer = FALSE, 
                       finalTime = 105,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE,
                       userVars = userVars,
                       rules = rules,
                       keepEvery = 1)
}

plot(uvex3, show = "genotypes", type = "line")    

## ----userVarsBasicExamplePlotVars---------------------------------
plot(
    unlist(uvex3$other$userVarValues) [c(FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex3$other$userVarValues) [c(FALSE, FALSE, TRUE, FALSE)], xlab="Time", ylab="Rate Diff",
    xlim=c(0, 105),
    ylim=c(-0.75,0.75), type="l", col="#1B9E77")  

lines(
    unlist(uvex3$other$userVarValues) [c(FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex3$other$userVarValues) [c(TRUE, FALSE, FALSE, FALSE)], type="l", col="#A6761D") 

lines(
    unlist(uvex3$other$userVarValues) [c(FALSE, FALSE, FALSE, TRUE)],
    unlist(uvex3$other$userVarValues) [c(FALSE, TRUE, FALSE, FALSE)], type="l", col="#666666")

legend(0, 0.75,
       legend=c("genWTRateDiff", "genARateDiff", "genBRateDiff"), col=c("#1B9E77", "#A6761D", "#666666"), lty= 1:2) 

## ----adaptiveTherapy1---------------------------------------------

dfat <- data.frame(Genotype = c("WT", "B", "A", "B, A", "C, A"),
                       Fitness = c("0*n_",
                                    "1.5",
                                    "1.002",
                                    "1.003",
                                    "1.004"))

adat <- allFitnessEffects(genotFitness = dfat,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "abs")

userVars <- list(
    list(Name           = "user_var1",
        Value       = 0
    )
)

userVars <- createUserVars(userVars)

rules <- list(
        list(ID = "rule_1",
            Condition = "n_B > n_A",
            Action = "user_var1 = n_A-n_B"
        ),list(ID = "rule_2",
            Condition = "n_B > n_A",
            Action = "user_var1 = n_B-n_A"
        )
    )

rules <- createRules(rules, adat)


## ----AdaptiveTherapy2---------------------------------------------
interventions <- list(
    list(ID           = "i1",
        Trigger       = "N > 1000",
        WhatHappens   = "N = user_var1*0.8",
        Periodicity   = 1,
        Repetitions   = 5
    )
)

interventions <- createInterventions(interventions, adat)


## ----AdaptiveTherapy3---------------------------------------------
atex <- oncoSimulIndiv(
                    adat, 
                    model = "McFLD",
                    mu = 1e-4,
                    sampleEvery = 0.01,
                    initSize = c(20000, 20000),
                    initMutant = c("A", "B"),
                    finalTime = 10,
                    onlyCancer = FALSE,
                    userVars = userVars,
                    rules = rules,
                    interventions = interventions
                    )

plot(atex, show = "genotypes", type = "line")    

## ----adaptiveTherapyComplexExample--------------------------------
dfat3 <- data.frame(Genotype = c("WT", "A", "B"), 
                   Fitness = c("1",
                               "0.8 + 0.2 * (n_B > 10) + 0.1 (n_A > 10)",
                               "0.8 + 0.25 * (n_B > 10)"
                               ))
afat3 <- allFitnessEffects(genotFitness = dfat3, 
                         frequencyDependentFitness = TRUE)
    

userVars <- list(
    list(Name           = "lastMeasuredA",
        Value       = 0
    ),
	list(Name           = "lastMeasuredB",
        Value       = 0
    ),
	list(Name           = "previousA",
        Value       = 0
    ),
	list(Name           = "previousB",
        Value       = 0
    ),
	list(Name           = "lastTime",
        Value       = 0
    ),
	list(Name           = "measure",
        Value       = 0
    ),
	list(Name           = "treatment",
        Value       = 0
    )
)

userVars <- createUserVars(userVars)

rules <- list(
       list(ID = "rule_1",
            Condition = "T - lastTime < 10",
            Action = "measure = 0"
        ),
		list(ID = "rule_2",
            Condition = "T - lastTime >= 10",
            Action = "measure = 1;lastTime = T"
        ),
		list(ID = "rule_3",
            Condition = "measure == 1",
            Action = "previousA = lastMeasuredA;previousB = lastMeasuredB;lastMeasuredA = n_A;lastMeasuredB = n_B"
        ), 
		list(ID = "rule_4",
            Condition = "TRUE",
            Action = "treatment = 0"
        ), 
		list(ID = "rule_5",
            Condition = "lastMeasuredA + lastMeasuredB > 100",
            Action = "treatment = 1"
        ), 
		list(ID = "rule_6",
            Condition = "lastMeasuredA - PreviousA > 500",
            Action = "treatment = 2"
        ),
		list(ID = "rule_7",
            Condition = "lastMeasuredB - PreviousB > 500",
            Action = "treatment = 3"
        ), 
		list(ID = "rule_8",
            Condition = "lastMeasuredA - PreviousA > 500 and lastMeasuredB - PreviousB > 500",
            Action = "treatment = 4"
        )
    )

rules <- createRules(rules, afat3)


interventions <- list(
    list(ID           = "basicTreatment",
        Trigger       = "treatment == 1",
        WhatHappens   = "N = 0.8*N",
        Periodicity   = 10,
        Repetitions   = Inf
    ),
	list(ID           = "treatmentOverA",
        Trigger       = "treatment == 2 or treatment == 4",
        WhatHappens   = "n_B = n_B*0.3",
        Periodicity   = 20,
        Repetitions   = Inf
    ),
	list(ID           = "treatmentOverB",
        Trigger       = "treatment == 3 or treatment == 4",
        WhatHappens   = "n_B = n_B*0.3",
        Periodicity   = 20,
        Repetitions   = Inf
    ),
	list(ID           = "intervention",
        Trigger       = "lastMeasuredA+lastMeasuredB > 5000",
        WhatHappens   = "N = 0.1*N",
        Periodicity   = 70,
        Repetitions   = Inf
    )
)

interventions <- createInterventions(interventions, afat3)


## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(atex2b)

if (FALSE) {

set.seed(1) ## for reproducibility
atex2b <- oncoSimulIndiv(afat3,
                         model = "McFLD", 
                       onlyCancer = FALSE, 
                       finalTime = 200,
                       mu = 1e-4,
                       initSize = 5000, 
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE,
                       userVars = userVars,
                       rules = rules,
                       interventions = interventions,
					   keepEvery = 1)
}

plot(atex2b, show = "genotypes", type = "line")

## ----HansenExample1-----------------------------------------------
dfat4 <- data.frame(Genotype = c("WT", "A", "B"), 
                   Fitness = c("n_/n_",
                               "1.005",
                               "1.1"
                               ))
afat4 <- allFitnessEffects(genotFitness = dfat4, 
                         frequencyDependentFitness = TRUE)

## ----HansenExample2-----------------------------------------------
interventions <- list(
    list(ID           = "i1",
        Trigger       = "T > 10",
        WhatHappens   = "n_B = n_B*0.8",
        Periodicity   = 1,
        Repetitions   = Inf
    )
)

interventions <- createInterventions(interventions, afat4)

## ----HansenExample3-----------------------------------------------
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(atex4)

if(FALSE) {
set.seed(1) ## for reproducibility
atex4 <- oncoSimulIndiv(afat4,
                       model = "McFLD", 
                       onlyCancer = FALSE, 
                       finalTime = 2000,
                       mu = 1e-4,
                       initSize = c(10000, 50, 1000), 
                       initMutant = c("WT", "A", "B"),
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE,
                       interventions = interventions,
                       keepEvery = 1)
}

plot(atex4, show = "genotypes", type = "line")

## ----HansenExampleAT1---------------------------------------------
dfat5 <- data.frame(Genotype = c("WT", "A", "B"), 
                   Fitness = c("n_/n_",
                               "1.005",
                               "1.1"
                               ))
afat5 <- allFitnessEffects(genotFitness = dfat5, 
                         frequencyDependentFitness = TRUE)

## ----HansenExampleAT2---------------------------------------------
userVars <- list(
    list(Name           = "measure",
        Value       = 0
    ),list(Name           = "lastTime",
        Value       = 0
    ),list(Name           = "treatment",
        Value       = 0
    ),list(Name           = "totalPopMeasured",
        Value       = 0
    )
)

userVars <- createUserVars(userVars)

rules <- list(
        list(ID = "rule_1",
            Condition = "T - lastTime < 10",
            Action = "measure = 0"
        ),list(ID = "rule_2",
            Condition = "T - lastTime >= 10",
            Action = "measure = 1;lastTime = T"
        ),list(ID = "rule_3",
            Condition = "measure == 1",
            Action = "totalPopMeasured = n_A + n_B"
        ),list(ID = "rule_4",
            Condition = "totalPopMeasured < 2000",
            Action = "treatment = 0"
        ),list(ID = "rule_5",
            Condition = "totalPopMeasured >= 2000",
            Action = "treatment = 1"
        )
    )

rules <- createRules(rules, afat5)

## ----HansenExampleAT3---------------------------------------------
interventions <- list(
    list(ID           = "i1",
        Trigger       = "treatment == 1",
        WhatHappens   = "n_B = n_B*0.8",
        Periodicity   = 1,
        Repetitions   = Inf
    )
)

interventions <- createInterventions(interventions, afat5)

## ----HansenExampleAT4---------------------------------------------
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(atex5)

if(FALSE) {
set.seed(1) ## for reproducibility
atex5 <- oncoSimulIndiv(afat5,
                       model = "McFLD", 
                       onlyCancer = FALSE, 
                       finalTime = 1500,
                       mu = 1e-4,
                       initSize = c(10000, 50, 1000), 
                       initMutant = c("WT", "A", "B"),
                       keepPhylog = FALSE,
                       seed = NULL, 
                       errorHitMaxTries = FALSE, 
                       errorHitWallTime = FALSE,
                       userVars = userVars,
                       rules = rules,
                       interventions = interventions)
}

plot(atex5, show = "genotypes", type = "line")

## ----timefdf1-----------------------------------------------------
## Fitness definition
fl <- data.frame(
  Genotype = c("WT", "A", "B"),
  Fitness = c("1",                       #WT
              "if (T>50) 1.5; else 0;",  #A
              "0*f_") ,                  #B
  stringsAsFactors = FALSE
)

fe <- allFitnessEffects(genotFitness = fl, 
                        frequencyDependentFitness = TRUE,
                        frequencyType = "rel")

## Evaluate the fitness before and after the specified currentTime
evalAllGenotypes(fe, spPopSizes = c(100, 100, 100))
evalAllGenotypes(fe, spPopSizes = c(100, 100, 100), currentTime = 80)

## Simulation                        
sim <- oncoSimulIndiv(fe,
                      model = "McFL", 
                      onlyCancer = FALSE, 
                      finalTime = 100,
                      mu = 0.01,
                      initSize = 5000, 
                      keepPhylog = FALSE,
                      seed = NULL, 
                      errorHitMaxTries = FALSE, 
                      errorHitWallTime = FALSE)

## Plot the results                      
plot(sim, show = "genotypes")                        


## ----adapChemo1---------------------------------------------------
a <- 1;     b <- 0.5;  c <- 0.5    ## a b c
d <- 1;     e <- 1.25; f <- 0.7    ## d e f
g <- 0.975; h <- -0.5; i <- 0.75   ## g h i

payoff_m <- matrix(c(a,b,c,d,e,f,g,h,i), ncol=3, byrow=TRUE)
colnames(payoff_m) <- c("Healthy", "Chemo-sensitive", "Chemo-resistant")
rownames(payoff_m) <- c("Healthy", "Chemo-sensitive", "Chemo-resistant")
print(payoff_m <- as.table(payoff_m))
  

## ----adapChemo2---------------------------------------------------
print( df <- data.frame(
  CellType = c("H", "S", "R"),
  Fitness = c("F(H) = ax(H) + bx(S) + cx(R)",    #Healthy
              "F(S) = dx(H) + ex(S) + fx(R)",    #Sensitive
              "F(R) = gx(H) + hx(S) + ix(R)")),  #Resistant
  row.names = FALSE )

## ----scenChemo1---------------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")


## Coefficients
# Healthy  Sensitive  Resistant
a=3;       b=1.5;     c=1.5     # Healthy
d=4;       e=5;       f=2.8     # Sensitive
g=3.9;     h=-2;      i=2.2     # Resistant


# Here we divide coefficients to reduce the amount of cells obtained in the simulation. 
# We have divided a, b and c by 3, and d, e and i by 4.

# Healthy     Sensitive   Resistant
a <- 1;       b <- 0.5;   c <- 0.5    # Healthy
d <- 1;       e <- 1.25;  f <- 0.7    # Sensitive
g <- 0.975;   h <- -0.5;  i <- 0.75   # Resistant


## Fitness definition
players <- data.frame(Genotype = c("WT","S","R","S,R"),
                      Fitness = c(paste0(a, "*f_+", b, "*f_S+", c, "*f_S_R"), #WT
                                  paste0(d,"*f_+",e,"*f_S+",f,"*f_S_R"),      #S
                                  "0",                                        #R
                                  paste0(g,"*f_+",h,"*f_S+",i,"*f_S_R")),     #S,R
                      stringsAsFactors = FALSE)


game <- allFitnessEffects(genotFitness = players,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")

## Plot the first scenario
eag <- evalAllGenotypes(game, spPopSizes = c(10,1,0,10))[c(1, 3, 4),]
  
plot(eag)

## Simulation
gamesimul <- oncoSimulIndiv(game,
                            model = "McFL",
                            onlyCancer = FALSE,
                            finalTime = 40,
                            mu = 0.01,
                            initSize = 5000,
                            keepPhylog = FALSE,
                            seed = NULL)
## Plot 2
plot(gamesimul, show = "genotypes", type = "line",
     col = c("black", "green", "red"), ylim = c(20, 50000))

plot(gamesimul, show = "genotypes")

## ----fixedChemo1--------------------------------------------------
# Effect of drug on fitness sensible tumor cells
drug_eff <- 0.01

wt_fitness <- paste0(a, "*f_+", b, "*f_S+", c, "*f_S_R")
sens_fitness <- paste0(d, "*f_+", e, "*f_S+", f, "*f_S_R")
res_fitness <- paste0(g, "*f_+", h, "*f_S+", i, "*f_S_R")

players_1 <- data.frame(Genotype = c("WT", "S", "R", "S, R"),
                        Fitness = c(wt_fitness,                                               #WT
                                    paste0("if (T>50) ", drug_eff, "*(",sens_fitness, ")",";  
                                           else ", sens_fitness, ";"),                        #S
                                    "0",                                                      #R
                                    res_fitness),                                             #S,R
                        stringsAsFactors = FALSE)

period_1 <- allFitnessEffects(genotFitness = players_1,
                              frequencyDependentFitness = TRUE,
                              frequencyType = "rel")


## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(simul_period_1)

if (FALSE) {
set.seed(2)

final_time <- 170 ## for speed
simul_period_1 <- oncoSimulIndiv(period_1,
                                 model = "McFL",
                                 onlyCancer = FALSE,
                                 finalTime = final_time,
                                 mu = 0.01,
                                 initSize = 5000,
                                 keepPhylog = FALSE,
                                 seed = NULL)
}
# ylim has been adapted to number of cells
plot(simul_period_1, show = "genotypes", type = "line",
     col = c("black", "green", "red"), ylim = c(20, 300000),
     thinData = TRUE)

## plot(simul_period_1, show = "genotypes", ylim = c(20, 12000))

## ----switchChemo1-------------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

# Healthy     Sensitive   Resistant
a <- 1;       b <- 0.5;   c <- 0.5    # Healthy
d <- 1;       e <- 1.25;  f <- 0.7    # Sensitive
g <- 0.975;   h <- -0.5;  i <- 0.75   # Resistant

wt_fitness   <- paste0(a, "*f_+", b, "*f_S+", c, "*f_S_R")
sens_fitness <- paste0(d, "*f_+", e, "*f_S+", f, "*f_S_R")
res_fitness  <- paste0(g, "*f_+", h, "*f_S+", i, "*f_S_R")

fitness_df <-data.frame(Genotype = c("WT", "S", "R", "S, R"),
                        Fitness = c(wt_fitness,                                              #WT
                                    paste0("if (T>50) (sin(T+2)/10) * (", sens_fitness,")",
                                           "; else ", sens_fitness, ";"),                    #S
                                    "0",                                                     #R
                                    res_fitness),                                            #S,R
                        stringsAsFactors = FALSE)

afe <- allFitnessEffects(genotFitness = fitness_df,
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

switching_sim  <- oncoSimulIndiv(afe,
                               model = "McFL",
                               onlyCancer = FALSE,
                               finalTime = 100,
                               mu = 0.01,
                               initSize = 5000,
                               keepPhylog = FALSE,
                               seed = NULL)

plot(switching_sim, show = "genotypes", type = "line",
   col = c("black", "green", "red"), ylim = c(20, 200000))

## plot(switching_sim, show = "genotypes", ylim = c(20, 1000))


## ----gfChemo1-----------------------------------------------------
# WT      Cooperators Defectors Overproducers  
a <- 1;   b <- 0.5;   c <- 0.5; m <- 0.75      ## a b c m
d <- 1;   e <- 1.25;  f <- 0.7; o <- 0.185     ## d e f o
g <- 1;   h <- 1.5;   i <- 0.5; p <- 2.5       ## g h i p 
j <- 0.8; k <- 1;     l <- 0.5; q <- 1.5       ## j k l q

payoff_m <- matrix(c(a,b,c,m,d,e,f,o,g,h,i,p,j,k,l,q), ncol=4, byrow=TRUE)
colnames(payoff_m) <- c("WT", "Cooperators", "Defectors", "Overproducers")
rownames(payoff_m) <- c("WT", "Cooperators", "Defectors", "Overproducers")
print(payoff_m <- as.table(payoff_m))


## ----gfChemo2-----------------------------------------------------
print( df <- data.frame(
  CellType = c("WT", "C", "D", "P"),
  Fitness = c("F(WT) = ax(WT) + bx(C) + cx(D) + mx(P)",    
              "F(C) = dx(WT) + ex(C) + fx(D) + ox(P)",    
              "F(D) = gx(WT) + hx(C) + ix(D) + px(P)",
              "F(P) = jx(WT) + kc(C) + lx(D) + qx(P)")), 
  row.names = FALSE )

## ----scenGF1------------------------------------------------------
set.seed(2)
RNGkind("L'Ecuyer-CMRG")

## Coefficients
## New coefficients for the interaction with overproducing sensitive:

# WT      COOPERATOR  DEFECTOR    OVERPRODUCER
a <- 1;   b <- 0.5;   c <- 0.5;   m <- 0.75   # WT
wt_fitness <- paste0(a, "*f_+", b, "*f_C+", c, "*f_C_D+", m, "*f_C_P")

d <- 1;   e <- 1.25;  f <- 0.7;   o <- 1.875  # Cooperator
coop_fitness <- paste0(d, "*f_+", e, "*f_C+", f, "*f_C_D+", o, "*f_C_P")

g <- 1;   h <- 1.5;   i <- 0.5;   p <- 2.5    # Defector
def_fitness <- paste0(g, "*f_+", h, "*f_C+", i, "*f_C_D+", p, "*f_C_P")

j <- 0.8; k <- 1;     l <- 0.5;   q <- 1.5    # Cooperator overproducing 
over_fitness <- paste0(j, "*f_+", k, "*f_C+", l, "*f_C_D+", q, "*f_C_P")


## No-chemotherapy
## Fitness definition
coop_no <- data.frame(Genotype = c("WT", "C", "D", "P", "C,D", "C,P", "D,P", "C,D,P"),
                      Fitness = c(
                        wt_fitness, #WT
                        coop_fitness, #S
                        "0", #D
                        "0", #P
                        def_fitness, #S,D
                        over_fitness, #S,P
                        "0", #D,P
                        "0"  #C,D,P
                        ), 
                        stringsAsFactors = FALSE)

game_no <- allFitnessEffects(genotFitness = coop_no,
                          frequencyDependentFitness = TRUE,
                          frequencyType = "rel")
                          

## First plot
eag <- evalAllGenotypes(game_no,
                        spPopSizes = c(WT = 10, C = 10, D = 0, P = 0,
                                       "C, D" = 10, "C, P" = 1,
                                       "D, P" = 0, "C, D, P" = 0))[c(1, 2, 5, 6),]
plot(eag)

## Simulation
gamesimul_no <- oncoSimulIndiv(game_no,
                            model = "McFL",
                            onlyCancer = FALSE,
                            finalTime = 35,
                            mu = 0.01,
                            initSize = 5000,
                            keepPhylog = FALSE,
                            seed = NULL)

## Second plot  
plot(gamesimul_no, show = "genotypes", type = "line",
     col = c("blue", "red", "green", "purple"), ylim = c(20, 50000),
     thinData = TRUE)

# Third plot  
## plot(gamesimul_no, show = "genotypes")

## ----scenGFChemo1-------------------------------------------------
## Chemotherapy - GF Impairing

# Effect of drug on GF availability
# This term is multiplied by the fitness, and reduces the GF available
drug_eff <- 0.25 

coop_fix <- data.frame(Genotype = c("WT", "C", "D", "P", "C,D", "C,P", "D,P", "C,D,P"),
                      Fitness = c(
                        wt_fitness,                                                   #WT
                        paste0("if (T>50) ", drug_eff, "* 1.2 *(", coop_fitness, ")", 
                               "; else ", coop_fitness, ";"),                         #C
                        "0",                                                          #D
                        "0",                                                          #P
                        paste0("if (T>50) ", drug_eff, "*(", def_fitness, ")", 
                               "; else ", def_fitness, ";"),                          #C,D
                        paste0("if (T>50) ", drug_eff, "* 1.5 * (", over_fitness, ")", 
                               "; else ", over_fitness, ";"),                         #C,P **
                        "0",                                                          #D,P
                        "0"                                                           #C,D,P
                      ), 
                      stringsAsFactors = FALSE)

## ** The drug effect is 1.5 times the original because of the overproduction of GF and the 
##    full availability of this molecule inside the producing subclone.
  
period_fix <- allFitnessEffects(genotFitness = coop_fix,
                              frequencyDependentFitness = TRUE,
                              frequencyType = "rel")

set.seed(2)
final_time <- 30 ## you'd want this longer; short for speed of vignette
simul_period_fix <- oncoSimulIndiv(period_fix,
                                 model = "McFL",
                                 onlyCancer = FALSE,
                                 finalTime = final_time,
                                 mu = 0.01,
                                 initSize = 5000,
                                 keepPhylog = FALSE,
                                 seed = NULL)

# First plot
plot(simul_period_fix, show = "genotypes", type = "line",
     col = c("blue", "red", "green", "purple"), ylim = c(20, 50000),
     thinData = TRUE)

# Second plot
## plot(simul_period_fix, show = "genotypes", ylim = c(20, 8000))

## ----exTimeInc----------------------------------------------------
dfT1 <- data.frame(Genotype = c("WT", "A", "B"),
                 Fitness = c("1",
                             "if (T>50) 1 + 2.35*f_; else 0.50;",
                             "if (T>200) 1 + 0.45*(f_ + f_1); else 0.50;"),
                 stringsAsFactors = FALSE)

afeT1 <- allFitnessEffects(genotFitness = dfT1,
                         frequencyDependentFitness = TRUE,
                         frequencyType = "rel")

set.seed(1)
simT1 <- oncoSimulIndiv(afeT1,
                      model = "Exp",
                      mu = 1e-5,
                      initSize = 1000,
                      finalTime = 500,
                      onlyCancer = FALSE,
                      seed = NULL)

plot(simT1, show = "genotypes", type = "line")

## ----exTimeInc1---------------------------------------------------
evalAllGenotypes(afeT1, spPopSizes = c(10,10,10), currentTime = 49)[c(2,3), ]
evalAllGenotypes(afeT1, spPopSizes = c(10,10,10), currentTime = 51)[c(2,3), ]
evalAllGenotypes(afeT1, spPopSizes = c(10,10,10), currentTime = 201)[c(2,3), ]

## ----exTimeDec----------------------------------------------------
dfT2 <- data.frame(Genotype = c("WT", "A", "B"),
                     Fitness = c(
                       "1",
                       "if (T>0 and T<50) 0; else if (T>100 and T<150) 0.05; else 1.2 + 0.35*f_;",
                       "0.8 + 0.45*(f_)"
                     ),
                     stringsAsFactors = FALSE)
  
afeT2 <- allFitnessEffects(genotFitness = dfT2,
                             frequencyDependentFitness = TRUE,
                             frequencyType = "rel")
  
  
## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(simT2)

if (FALSE) {
set.seed(1)
simT2 <- oncoSimulIndiv(afeT2,
                        model = "McFL",
                        mu = 1e-5,
                        initSize = 10000,
                        finalTime = 225,
                        onlyCancer = FALSE,
                        seed = NULL,
						keepEvery = 1)
}
## Had we not used keepEvery, we'd probably have used
## plot(simT2, show = "genotypes", thinData = TRUE)
plot(simT2, show = "genotypes")


## ----examplesTDec1------------------------------------------------
evalAllGenotypes(afeT2, spPopSizes = c(100,10,10), currentTime = 49)[c(2,3), ]
evalAllGenotypes(afeT2, spPopSizes = c(100,10,10), currentTime = 51)[c(2,3), ]
evalAllGenotypes(afeT2, spPopSizes = c(100,10,10), currentTime = 101)[c(2,3), ]
evalAllGenotypes(afeT2, spPopSizes = c(100,10,10), currentTime = 201)[c(2,3), ]

## ----examplesTCol-------------------------------------------------
dfT3 <- data.frame(Genotype = c("WT", "A", "B"),
                 Fitness = c(
                   "1",
                   "1 + 0.2 * (n_2 > 10)",
                   "if (T>50 and T<80) 0.80; else 0.9 + 0.4 * (n_1 > 10)"),
                 stringsAsFactors = FALSE)

afeT3 <- allFitnessEffects(genotFitness = dfT3,
                           frequencyDependentFitness = TRUE,
                           frequencyType = "abs")


## For speed creating the vignette, we load
## precomputed simulation data. Otherwise, run code below

data(simT3)

if (FALSE) {
set.seed(2)
simT3 <- oncoSimulIndiv(afeT3,
                      model = "McFLD",
                      mu = 1e-4,
                      initSize = 5000,
                      finalTime = 500,
                      onlyCancer = FALSE,
                      seed = NULL,
                      errorHitWallTime = FALSE,
                      errorHitMaxTries = FALSE,
					  keepEvery = 1)
}

plot(simT3, show = "genotypes", type = "line")

## ----examplesTCol1------------------------------------------------
evalAllGenotypes(afeT3, spPopSizes = c(10,10,10), currentTime = 30)[c(2,3), ]
evalAllGenotypes(afeT3, spPopSizes = c(11,11,11), currentTime = 79)[c(2,3), ]
evalAllGenotypes(afeT3, spPopSizes = c(11,11,11), currentTime = 81)[c(2,3), ]

## ----lod_pom_ex---------------------------------------------------
pancr <- allFitnessEffects(
    data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A", 
                          "TP53", "TP53", "MLL3"),
               child = c("KRAS","SMAD4", "CDNK2A", 
                         "TP53", "MLL3",
                         rep("PXDN", 3), rep("TGFBR2", 2)),
               s = 0.05, sh = -0.3, typeDep = "MN"))

pancr16 <- oncoSimulPop(16, pancr,
                        model = "Exp",                          onlyCancer = TRUE,
                        mc.cores = 2)

## Look a the first POM 
str(POM(pancr16)[1:3])

LOD(pancr16)[1:2]

## The diversity of LOD (lod_single) and POM might or might not
## be identical
diversityPOM(POM(pancr16))
diversityLOD(LOD(pancr16))

## Show the genotypes and their diversity (which might, or might
## not, differ from the diversity of LOD and POM)
sampledGenotypes(samplePop(pancr16))


## ----nyv0150------------------------------------------------------
## No seed fixed, so reruns will give different DAGs.
(a1 <- simOGraph(10))
library(graph) ## for simple plotting
plot(as(a1, "graphNEL"))

## ----simographindirect, eval=FALSE,echo=TRUE----------------------
#  g2 <- simOGraph(4, out = "rT", removeDirectIndirect = FALSE)
#  
#  fe_from_d <- allFitnessEffects(g2)
#  fitness_d <- evalAllGenotypes(fe_from_d)
#  
#  fe_from_t <- allFitnessEffects(genotFitness =
#                            OncoSimulR:::allGenotypes_to_matrix(fitness_d))
#  						
#  ## Compare
#  fitness_d
#  (fitness_t <- evalAllGenotypes(fe_from_t))
#  
#  identical(fitness_d, fitness_t)
#  						
#  
#  ## ... but to be safe use fe_from_t as the fitnessEffects object for simulations
#  

## ----nyv0161------------------------------------------------------
## This code will only be evaluated under Windows
if(.Platform$OS.type == "windows")
    try(pancrError <- oncoSimulPop(10, pancr,
                                   initSize = 1e-5,
                                   onlyCancer = TRUE,
                                   detectionSize = 1e7,
                                   keepEvery = 10,
                               mc.cores = 2))

## ----nyv0162------------------------------------------------------
## Do not run under Windows
if(.Platform$OS.type != "windows")
    pancrError <- oncoSimulPop(10, pancr,
                               initSize = 1e-5,
                               onlyCancer = TRUE,
                               detectionSize = 1e7,
                               keepEvery = 10,
                               mc.cores = 2)

## ----rzx0316, eval=FALSE------------------------------------------
#  pancrError[[1]]

## ----rzx0317, eval=FALSE------------------------------------------
#  pancrError[[1]][1]

## ----ex-tomlin1exc------------------------------------------------
sd <- 0.1 ## fitness effect of drivers
sm <- 0 ## fitness effect of mutator
nd <- 20 ## number of drivers
nm <- 5  ## number of mutators
mut <- 50 ## mutator effect  THIS IS THE DIFFERENCE

fitnessGenesVector <- c(rep(sd, nd), rep(sm, nm))
names(fitnessGenesVector) <- 1:(nd + nm)
mutatorGenesVector <- rep(mut, nm)
names(mutatorGenesVector) <- (nd + 1):(nd + nm)

ft <- allFitnessEffects(noIntGenes = fitnessGenesVector,
                        drvNames = 1:nd)
mt <- allMutatorEffects(noIntGenes = mutatorGenesVector)


## ----ex-tomlinexc2------------------------------------------------
ddr <- 4
set.seed(2)
RNGkind("L'Ecuyer-CMRG")
st <- oncoSimulPop(4, ft, muEF = mt,
                   detectionDrivers = ddr,
                   finalTime = NA,
                   detectionSize = NA,
                   detectionProb = NA,
                   onlyCancer = TRUE,
                   keepEvery = NA, 
                   mc.cores = 2, ## adapt to your hardware
                   seed = NULL) ## for reproducibility
## set.seed(NULL) ## return things to their "usual state"


## ----nyv01880-----------------------------------------------------
sessionInfo()

## ----last, echo=FALSE---------------------------------------------
paste(difftime(Sys.time(), time0, units = "mins"), "minutes")

## ----timeit-------------------------------------------------------
## The 15 most time consuming chunks
sort(unlist(all_times), decreasing = TRUE)[1:15]

## ----sumtimeit----------------------------------------------------
paste("Sum times of chunks = ", sum(unlist(all_times))/60, " minutes")

## ----fundinglogo, eval=TRUE,echo=FALSE----------------------------
knitr::include_graphics("logo-micin-aei-uefeder.png", dpi = 150)

## ----rzx0323, echo=FALSE, eval=TRUE-------------------------------
## reinitialize the seed
set.seed(NULL)

