Browse code

2.5.3. Vignette enhancements

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/OncoSimulR@125024 bc3139a8-67e5-0310-9ffc-ced21a209358

Ramon Diaz-Uriarte authored on 12/12/2016 12:06:14
Showing4 changed files

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@@ -1,8 +1,8 @@
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 Package: OncoSimulR
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 Type: Package
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 Title: Forward Genetic Simulation of Cancer Progression with Epistasis 
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-Version: 2.5.2
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-Date: 2016-12-10
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+Version: 2.5.3
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+Date: 2016-12-12
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 Authors@R: c(person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),
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 		     email = "rdiaz02@gmail.com"),
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 	      person("Mark", "Taylor", role = "ctb", email = "ningkiling@gmail.com"))
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@@ -30,7 +30,7 @@ URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cance
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 BugReports: https://github.com/rdiaz02/OncoSimul/issues
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 Depends: R (>= 3.3.0)
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 Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel
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-Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown
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+Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander
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 LinkingTo: Rcpp
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 VignetteBuilder: knitr
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@@ -1,3 +1,7 @@
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+Changes in version 2.5.2 (2016-12-12):
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+	- Vignette uses pander in tables.
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+	- Typos fixed and other enhancements in vignette.
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+	
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 Changes in version 2.5.2 (2016-12-10):
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         - Lots and lots of addition to vignette including benchmarks.
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         - Diversity of sampled genotypes.
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@@ -10,7 +10,9 @@ author: "
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 		 <rdiaz02@gmail.com>, <http://ligarto.org/rdiaz>
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 		 "
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-date: "`r paste0(Sys.Date(),'. OncoSimulR version ', packageVersion('OncoSimulR'), '. Revision: ', system('git rev-parse --short HEAD', intern = TRUE))`"
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+date: "`r paste0(Sys.Date(),'. OncoSimulR version ', packageVersion('OncoSimulR'), suppressWarnings(ifelse(length(try(system('git rev-parse --short HEAD', ignore.stderr = TRUE, intern = TRUE))), paste0('. Revision: ', system('git rev-parse --short HEAD', intern = TRUE)), '')))`"
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+header-includes:
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+    - \input{preamble.tex}
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 output: 
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   bookdown::html_document2:
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     css: custom4.css
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@@ -21,6 +23,7 @@ classoption: a4paper
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 geometry: margin=3cm
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 fontsize: 12pt
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 bibliography: OncoSimulR.bib
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+biblio-style: "apalike"
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 link-citations: true
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 vignette: >
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   %\VignetteIndexEntry{OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression.}
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@@ -129,6 +132,7 @@ MathJax.Hub.Config({
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 knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
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 options(width = 70)
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 require(BiocStyle)
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+require(pander)
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 ```
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 <!-- bookdown::pdf_document2 seems to produce the tex even if failures -->
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@@ -144,8 +148,9 @@ require(BiocStyle)
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 <!-- sed -i 's/\\Rfunction{\([^}]\+\)}/*`\1`*/g' p22.md -->
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+\clearpage
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-# Introduction {#intro}
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+# Introduction {#introdd}
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 OncoSimulR is an individual-based forward-time genetic simulator for
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 biallelic markers (wildtype vs. mutated) in asexually reproducing
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@@ -286,6 +291,7 @@ is a summary of some of the key features:
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   from the simulations.
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+
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 The table below, modified from the table at the
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 [Genetics Simulation Resources (GSR) page](https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed)
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 provides a summary of the key features of OncoSimulR. (An
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@@ -295,7 +301,7 @@ https://popmodels.cancercontrol.cancer.gov/gsr/search/ or from the
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 [Genetics Simulation Resources table itself](https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed),
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 by moving the mouse over each term).
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-
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+\clearpage
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 |Attribute Category     | Attribute                                     |
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 |-----------------------|-----------------------------------------------|
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@@ -322,7 +328,7 @@ by moving the mouse over each term).
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 |&nbsp; Mutation Models|	Two-allele Mutation Model (wildtype, mutant), without back mutation|
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 |&nbsp; Events Allowed|	Varying Genetic Features: change of individual mutation rates (mutator/antimutator genes)|
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 |&nbsp; Spatial Structure| No Spatial Structure (perfectly mixed and no migration)|
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-Table: (\#tab:osrfeatures) Key features of OncoSimulR. Modified from
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+Table:(\#tab:osrfeatures) Key features of OncoSimulR. Modified from
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 the original table from
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 https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed
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 .
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@@ -485,17 +491,19 @@ g1 <- simOGraph(4, out = "rT")
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 ## 2. Simulate two evolutionary trajectories
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 s1 <- oncoSimulPop(10, allFitnessEffects(g1, drvNames = 1:4),
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                    mc.cores = 2, ## adapt to your hardware
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-                   seed = NULL) ## for reproducibility in this vignette
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+                   seed = NULL) ## for reproducibility of vignette
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 ## 3. Sample those data uniformly, and add noise
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 d1 <- samplePop(s1, timeSample = "unif", propError = 0.1)
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-## 4. You would now run the appropriate inferential method and compare
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-## observed and true
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+## 4. You would now run the appropriate inferential method and
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+## compare observed and true
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+
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 require(Oncotree)
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 fit1 <- oncotree.fit(d1)
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-## Now, compare fitted and original. This is well beyond the scope of this
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-## document (and OncoSimulR itself).
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+
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+## Now, compare fitted and original. This is well beyond the
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+## scope of this document (and OncoSimulR itself).
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 ```
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@@ -532,8 +540,8 @@ RNGkind("L'Ecuyer-CMRG")
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 ```{r exochs}
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 ## Specify fitness effects. 
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-## Numeric values arbitrary, but set the intermediate genotype 
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-## en route to ui as mildly deleterious so there is a valley. 
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+## Numeric values arbitrary, but set the intermediate genotype en
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+## route to ui as mildly deleterious so there is a valley.
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 ## As in Ochs and Desai, the ui and uv genotypes
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 ## can never appear. 
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@@ -545,7 +553,9 @@ od <- allFitnessEffects(
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                   "u:v" = uv, "i" = i,
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                   "v:-i" = -Inf, "v:i" = vi))
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-## For the sake of extending this example, also turn i into a mutator gene
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+## For the sake of extending this example, also turn i into a
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+## mutator gene
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+
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 odm <- allMutatorEffects(noIntGenes = c("i" = 50))
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 ## How do mutation and fitness look like for each genotype?
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@@ -600,19 +610,23 @@ RNGkind("L'Ecuyer-CMRG")
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 ## reference genotype or evolutionary model, or stopping criterion, 
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 ## sampling procedure, or ...
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-## 1. Generate a random fitness landscape, from the Rough
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-##    Mount Fuji model, with g genes, and c ("slope" constant) and
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-##    reference chosen randomly. Ask for a minimal number of accessible
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-##    genotypes
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+## 1. Generate a random fitness landscape, from the Rough Mount
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+##    Fuji model, with g genes, and c ("slope" constant) and
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+##    reference chosen randomly. Ask for a minimal number of
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+##    accessible genotypes
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+
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 g <- 6
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 c <- runif(1, 1/5, 5)
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 rl <- rfitness(g, c = c, min_accessible_genotypes = g)
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-## Plot it if you want; commented here as it takes long for a vignette
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+## Plot it if you want; commented here as it takes long for a
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+## vignette
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+
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 ## plot(rl)
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 ## Obtain landscape measures from Magellan. Export to Magellan
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 to_Magellan(rl, file = "rl1.txt")
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+
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 ## (Getting the statistics requires you to install Magellan,
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 ## and that requires either calling the web app 
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 ## (http://wwwabi.snv.jussieu.fr/public/Magellan/) or
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@@ -1165,7 +1179,7 @@ version 2. Please note that **the functionality of version 1 will soon be remove
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-
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+\clearpage
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 # Running time and space consumption of OncoSimulR {#timings}
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@@ -1230,6 +1244,91 @@ some of the benchmarks is available from the
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 repository at https://github.com/rdiaz02/OncoSimul).
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+```{r colnames_benchmarks, echo = FALSE, eval = TRUE}
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+
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+data(benchmark_1)
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+data(benchmark_1_0.05)
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+data(benchmark_2)
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+data(benchmark_3)
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+
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+colnames(benchmark_1)[
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+    match(c(
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+	"time_per_simul",
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+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean",
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+	"TotalPopSize.Max.", "keepEvery",  "Attempts.Median",
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+	"Attempts.Mean", "Attempts.Max.",
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+	"PDBaseline", "n2", "onlyCancer"),
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+	 colnames(benchmark_1)
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+	)] <- c("Elapsed Time, average per simulation (s)",
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+	              "Object Size, average per simulation (MB)",
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+				  "Number of Clones, median",
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+				  "Number of Iterations, median",
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+				  "Final Time, median",
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+				  "Total Population Size, median",
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+				   "Total Population Size, mean",
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+				  "Total Population Size, max.",
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+				  "keepEvery",
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+				  "Attempts until Cancer, median",
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+				  "Attempts until Cancer, mean",
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+				  "Attempts until Cancer, max.",
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+				  "PDBaseline", "n2", "onlyCancer"
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+				  )
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+				  
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+	
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+colnames(benchmark_1_0.05)[
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+    match(c("time_per_simul",
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+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
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+	"TotalPopSize.Max.",
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+	"keepEvery",
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+	"PDBaseline", "n2", "onlyCancer", "Attempts.Median"),
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+	colnames(benchmark_1_0.05))] <- c("Elapsed Time, average per simulation (s)",
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+	              "Object Size, average per simulation (MB)",
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+				  "Number of Clones, median",
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+				  "Number of Iterations, median",
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+				  "Final Time, median",
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+				  "Total Population Size, median",
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+				  "Total Population Size, mean",
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+				  "Total Population Size, max.",
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+				  "keepEvery",
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+				  "PDBaseline", "n2", "onlyCancer",
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+				  "Attempts until Cancer, median"
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+				  )
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+
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+
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+colnames(benchmark_2)[match(c("Model", "fitness", "time_per_simul",
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+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
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+	"TotalPopSize.Max."), colnames(benchmark_2))] <-  c("Model",
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+				  "Fitness",
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+	"Elapsed Time, average per simulation (s)",
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+	              "Object Size, average per simulation (MB)",
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+				  "Number of Clones, median",
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+				  "Number of Iterations, median",
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+				  "Final Time, median",
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+				  "Total Population Size, median",
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+				  "Total Population Size, mean",
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+				  "Total Population Size, max."
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+				  )	
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+				  
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+colnames(benchmark_3)[match(c("Model", "fitness", "time_per_simul",
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+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
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+	"TotalPopSize.Max."), colnames(benchmark_3))] <-  c("Model",
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+				  "Fitness",
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+	"Elapsed Time, average per simulation (s)",
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+	              "Object Size, average per simulation (MB)",
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+				  "Number of Clones, median",
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+				  "Number of Iterations, median",
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+				  "Final Time, median",
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+				  "Total Population Size, median",
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+				  "Total Population Size, mean",
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+				  "Total Population Size, max."
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+				  )					  
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+```
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+
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+
1233 1332
 ## Exp and McFL with "detectionProb" and pancreas example {#bench1}
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 To get familiar with some of they factors that affect time and size,
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@@ -1246,9 +1345,6 @@ period are pruned and only the existing clones at the end of the
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 simulation are returned (see details in \@ref(prune)).
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-```{r loadbench1, echo = FALSE, eval = TRUE} 
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-data(benchmark_1) 
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-``` 
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 Will run `r unique(benchmark_1$Numindiv)` simulations.  The results
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 I show are for a laptop with an 8-core Intel Xeon E3-1505M CPU,
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@@ -1340,36 +1436,50 @@ summary(unlist(lapply(exp1, "[[", "TotalPopSize")))
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 The above runs yield the following:
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+\blandscape
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+
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+Table: (\#tab:bench1) Benchmarks of Exp and McFL models using the default `detectionProb` with two settings of `keepEvery`. 
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 ```{r bench1, eval=TRUE, echo = FALSE}
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-data(benchmark_1)
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-knitr::kable(benchmark_1[1:4, c("time_per_simul",
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-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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-	"FinalTime.Median", "TotalPopSize.Median",
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-	"TotalPopSize.Max.", "keepEvery")], 
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-    booktabs = TRUE,
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-	col.names = c("Elapsed Time, average per simulation (s)",
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-	              "Object Size, average per simulation (MB)",
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-				  "Number of Clones, median",
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-				  "Number of Iterations, median",
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-				  "Final Time, median",
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-				  "Total Population Size, median",
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-				  "Total Population Size, max.",
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-				  "keepEvery"
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-				  ),
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-    caption = "Benchmarks of Exp and McFL models using the default
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-	`detectionProb` with two settings of `keepEvery`.", 
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-	align = "c")
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+
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+panderOptions("table.split.table", 99999999)
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+panderOptions("table.split.cells", 900)  ## For HTML
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+## panderOptions("table.split.cells", 8) ## For PDF
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+
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+set.alignment('right')
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+panderOptions('round', 3)
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+				          
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+pander(benchmark_1[1:4, c("Elapsed Time, average per simulation (s)", 
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+ 	              "Object Size, average per simulation (MB)",
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+ 				  "Number of Clones, median",
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+ 				  "Number of Iterations, median",
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+ 				  "Final Time, median",
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+ 				  "Total Population Size, median",
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+ 				  "Total Population Size, max.",
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+ 				  "keepEvery")],
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+				  ## caption = "\\label{tab:bench1}Benchmarks of Exp and McFL  models using the default `detectionProb` with two settings of `keepEvery`."
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+				  )
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 ```
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-The above table shows that a naive comparison (looking simply at
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-execution time) might conclude that the McFL model is much, much
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-slower than the Exp model. But that is not the complete story: using
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-the `detectionProb` stopping mechanism (see \@ref(detectprob)) will
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-lead to stopping the simulations very quickly in the exponential
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-model because as soon as a clone with fitness $>1$ appears it starts
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-growing exponentially. In fact, we can see that the number of
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-iterations and the final time are much smaller in the Exp than in
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-the McFL model. 
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+\elandscape
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+
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+\clearpage
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+
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+
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+
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+The above table shows that a naive comparison (looking simply at execution
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+time) might conclude that the McFL model is much, much slower than the Exp
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+model. But that is not the complete story: using the `detectionProb`
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+stopping mechanism (see \@ref(detectprob)) will lead to stopping the
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+simulations very quickly in the exponential model because as soon as a
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+clone with fitness $>1$ appears it starts growing exponentially. In fact,
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+we can see that the number of iterations and the final time are much
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+smaller in the Exp than in the McFL model.  We will elaborate on this
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+point below (section \@ref(common1)), when we discuss the setting for
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+`checkSizePEvery` (here left at its default value of 20): checking the
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+exiting condition more often (smaller `checkSizePEvery`) would probably be
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+justified here (notice also the very large final times) and would lead to
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+a sharp decrease in number of iterations and, thus, running time.
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+
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 This table also shows that the `keepEvery = NA` setting, which was in effect
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@@ -1451,30 +1561,38 @@ t_exp6 <- system.time(
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 ```
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+\blandscape
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+Table: (\#tab:bench1b) Benchmarks of Exp models modifying the default `detectionProb` with two settings of `keepEvery`.
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 ```{r bench1b, eval=TRUE, echo = FALSE}
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-data(benchmark_1)
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-knitr::kable(benchmark_1[5:8, c("time_per_simul",
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-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
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-	"FinalTime.Median", "TotalPopSize.Median",
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-	"TotalPopSize.Max.", "keepEvery",
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-	"PDBaseline", "n2")], 
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-    booktabs = TRUE,
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-	col.names = c("Elapsed Time, average per simulation (s)",
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-	              "Object Size, average per simulation (MB)",
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-				  "Number of Clones, median",
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-				  "Number of Iterations, median",
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-				  "Final Time, median",
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-				  "Total Population Size, median",
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-				  "Total Population Size, max.",				  
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-				  "keepEvery",
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-				  "PDBaseline", "n2"
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-				  ),	
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-    caption = "Benchmarks of Exp models modifying the default
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-	`detectionProb` with two settings of `keepEvery`.", 
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-	align = "c")
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+panderOptions("table.split.table", 99999999)
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+panderOptions("table.split.cells", 900)  ## For HTML
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+## panderOptions("table.split.cells", 8) ## For PDF
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+set.alignment('right')
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+panderOptions('round', 2)
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+panderOptions('big.mark', ',')
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+panderOptions('digits', 2)
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+
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+pander(benchmark_1[5:8, c("Elapsed Time, average per simulation (s)",
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+ 	              "Object Size, average per simulation (MB)",
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+ 				  "Number of Clones, median",
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+ 				  "Number of Iterations, median",
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+ 				  "Final Time, median",
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+ 				  "Total Population Size, median",
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+ 				  "Total Population Size, max.",
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+ 				  "keepEvery",
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+				  "PDBaseline",
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+				  "n2")], 
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+## 				  round = c(rep(2, 3), rep(0, 7)),
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+## 				  digits = c(rep(2, 3), rep(1, 7)),
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+	  ## caption = "\\label{tab:bench1b}Benchmarks of Exp and McFL models modifying the default `detectionProb` with two settings of `keepEvery`."
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+    )
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+
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 ```
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+\elandscape
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+
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+\clearpage
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1479 1597
 As above,  `keepEvery = NA` (in `exp4` and `exp6`) leads to much
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 smaller object sizes and slightly smaller numbers of clones and
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@@ -1509,62 +1627,95 @@ initial population sizes) because of the dependency of death rate on total
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 population size (see section \@ref(mcfl)).
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-The number of attempts until cancer was reached in the above models is
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-shown in the following table (the values can be obtained from any of the
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-above runs doing, for instance, `median(unlist(lapply(exp1, function(x)
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-x$other$attemptsUsed)))` ).:
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-
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-
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+The number of attempts until cancer was reached in the above
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+models is shown in the Table \@ref(tab:bench1c) (the values can be obtained from
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+any of the above runs doing, for instance, `median(unlist(lapply(exp1,
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+function(x) x$other$attemptsUsed)))` ):
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+Table: (\#tab:bench1c) Number of attempts until cancer.
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 ```{r bench1c, eval=TRUE, echo = FALSE}
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-data(benchmark_1)
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-knitr::kable(benchmark_1[1:8, c("Attempts.Median"), drop = FALSE], 
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-    booktabs = TRUE,
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-	row.names = TRUE,
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-	col.names = "Attempts until cancer",
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-    caption = "Median Number of attempts until cancer.", 
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-	align = "r")
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+panderOptions("table.split.table", 99999999)
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+panderOptions("table.split.cells", 900)  ## For HTML
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+## panderOptions("table.split.cells", 8) ## For PDF
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+set.alignment('right')
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+panderOptions('round', 2)
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+panderOptions('big.mark', ',')
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+panderOptions('digits', 2)
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+
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+pander(benchmark_1[1:8, c(
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+"Attempts until Cancer, median", 
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+"Attempts until Cancer, mean", 
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+"Attempts until Cancer, max.", 
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+				  "PDBaseline",
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+				  "n2")], 
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+## 				  round = c(rep(2, 3), rep(0, 7)),
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+## 				  digits = c(rep(2, 3), rep(1, 7)),
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+	  ## caption = "\\label{tab:bench1c}Median number of attempts until cancer."
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+    )
1655
+## ## data(benchmark_1)
1656
+## knitr::kable(benchmark_1[1:8, c("Attempts.Median",
1657
+##                                 "PDBaseline", "n2"), drop = FALSE], 
1658
+##     booktabs = TRUE,
1659
+## 	row.names = TRUE,
1660
+## 	col.names = c("Attempts until cancer", "PDBaseline", "n2"),
1661
+##     caption = "Median number of attempts until cancer.", 
1662
+## 	align = "r")
1527 1663
 	
1528 1664
 ```
1529 1665
 
1530 1666
 
1531
-
1532
-
1667
+The McFL models finish in a single attempt. The exponential model
1668
+simulations where we can exit with small population sizes (`exp1`, `exp2`)
1669
+need many fewer attempts to reach cancer than those where large population
1670
+sizes are required (`exp3` to `exp6`). There is no relevant different
1671
+among those four, which is what we would expect: a population that has
1672
+already reached a size of 50,000 cells from an initial population size of
1673
+500 is obviously a growing population where there is at least one mutant
1674
+with positive fitness; thus, it unlikely to go extinct and therefore
1675
+having to grow up to at least 500,000 will not significantly increase the
1676
+risk of extinction.
1533 1677
 
1534 1678
 
1535 1679
 We will now rerun all of the above models with argument `onlyCancer =
1536
-FALSE`.  The results are shown below (note that the differences between
1537
-this table and table \@ref(tab:bench1) for the McFL models are due only to
1538
-sampling variation).
1680
+FALSE`.  The results are shown in Table \@ref(tab:timing3) (note that the
1681
+differences between this table and table \@ref(tab:bench1) for the McFL
1682
+models are due only to sampling variation).
1683
+
1684
+\bslandscape
1539 1685
 
1540 1686
 
1541
-Table: (\#tab:timing3) Benchmarks of models in Table \@ref(tab:bench1) and
1542
-\@ref(tab:bench1b) when run with `onlyCancer = FALSE`
1687
+Table: (\#tab:timing3) Benchmarks of models in Table \@ref(tab:bench1) and \@ref(tab:bench1b) when run with `onlyCancer = FALSE`
1543 1688
 ```{r bench1d, eval=TRUE, echo = FALSE}
1544
-data(benchmark_1)
1545
-knitr::kable(benchmark_1[9:16, c("time_per_simul",
1546
-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
1547
-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
1548
-	"TotalPopSize.Max.",
1549
-	"keepEvery",
1550
-	"PDBaseline", "n2")], 
1551
-    booktabs = TRUE,
1552
-	col.names = c("Elapsed Time, average per simulation (s)",
1553
-	              "Object Size, average per simulation (MB)",
1554
-				  "Number of Clones, median",
1555
-				  "Number of Iterations, median",
1556
-				  "Final Time, median",
1557
-				  "Total Population Size, median",
1689
+panderOptions("table.split.table", 99999999)
1690
+panderOptions("table.split.cells", 900)  ## For HTML
1691
+## panderOptions("table.split.cells", 8) ## For PDF
1692
+panderOptions("table.split.cells", 15) ## does not fit otherwise
1693
+set.alignment('right')
1694
+panderOptions('round', 3)
1695
+
1696
+pander(benchmark_1[9:16, 
1697
+    c("Elapsed Time, average per simulation (s)",
1698
+ 	              "Object Size, average per simulation (MB)",
1699
+ 				  "Number of Clones, median",
1700
+ 				  "Number of Iterations, median",
1701
+ 				  "Final Time, median",
1702
+ 				  "Total Population Size, median",
1558 1703
 				  "Total Population Size, mean",
1559
-				  "Total Population Size, max.",
1560
-				  "keepEvery",
1561
-				  "PDBaseline", "n2"
1562
-				  ),
1563
-##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and
1564
-##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 
1565
-	align = "c")
1704
+ 				  "Total Population Size, max.",
1705
+ 				  "keepEvery",
1706
+				  "PDBaseline",
1707
+				  "n2")],
1708
+## caption = "\\label{tab:timing3} Benchmarks of models in Table \\@ref(tab:bench1) and \\@ref(tab:bench1b) when run with `onlyCancer = FALSE`."
1709
+				  )	
1710
+	
1566 1711
 ```
1567 1712
 
1713
+\eslandscape
1714
+
1715
+\clearpage
1716
+
1717
+
1718
+
1568 1719
 
1569 1720
 Now most simulations under the exponential model end up in extinction, as
1570 1721
 seen by the median population size of 0 (but not all, as the mean and
... ...
@@ -1576,47 +1727,101 @@ on the question being asked (see, for example, section \@ref(exbauer) for
1576 1727
 a question where we will naturally want to use `onlyCancer = FALSE`).
1577 1728
 
1578 1729
 
1730
+To make it easier to compare results with those of the next section, Table
1731
+\@ref(tab:allr1bck) shows all the runs so far.
1732
+
1733
+
1734
+\bslandscape
1735
+
1736
+Table: (\#tab:allr1bck) Benchmarks of all models in Tables \@ref(tab:bench1), \@ref(tab:bench1b) and \@ref(tab:timing3).  
1737
+```{r bench1dx0, eval=TRUE, echo = FALSE}
1738
+panderOptions("table.split.table", 99999999)
1739
+## panderOptions("table.split.cells", 900)  ## For HTML
1740
+panderOptions("table.split.cells", 19)
1741
+
1742
+set.alignment('right') 
1743
+panderOptions('round', 3)
1744
+	
1745
+pander(benchmark_1[ , c("Elapsed Time, average per simulation (s)",
1746
+ 	              "Object Size, average per simulation (MB)", 
1747
+				  "Number of Clones, median", 
1748
+				  "Number of Iterations, median", 
1749
+				  "Final Time, median", "Total Population Size, median", 
1750
+				  "Total Population Size, mean", "Total Population Size, max.",
1751
+ 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 
1752
+				  ## caption = "\\label{tab:allr1bck}Benchmarks of all models in Tables \\@ref(tab:bench1), \\@ref(tab:bench1b)  and \\@ref(tab:timing3)."  
1753
+				  )  
1754
+```
1755
+
1756
+\eslandscape
1757
+
1758
+\clearpage
1759
+
1760
+
1761
+
1579 1762
 ### Changing fitness: $s=0.1$ and $s=0.05$ {#bench1xf}
1580 1763
 
1581 1764
 In the above fitness specification the fitness effect of each gene (when
1582 1765
 its restrictions are satisfied) is $s = 0.1$ (see section \@ref(numfit)
1583 1766
 for details). Here we rerun all the above benchmarks using $s= 0.05$ and
1584
-results are shown below in Table \@(tab:timing3xf); the results from these
1767
+results are shown below in Table \@ref(tab:timing3xf); the results from these
1585 1768
 benchmarks are available as `data(benchmark_1_0.05)`:
1586 1769
 
1770
+\bslandscape
1587 1771
 
1588
-Table: (\#tab:timing3xf) Benchmarks of all models in Tables \@ref(tab:bench1),
1589
-\@ref(tab:bench1b) and \@ref(tab:timing3) using $s=0.05$ (instead of
1590
-$s=0.1$).
1772
+Table: (\#tab:timing3xf) Benchmarks of all models in Table \@ref(tab:allr1bck) using $s=0.05$ (instead of $s=0.1$).
1591 1773
 ```{r bench1dx, eval=TRUE, echo = FALSE}
1592
-data(benchmark_1_0.05)
1593
-knitr::kable(benchmark_1_0.05[, c("time_per_simul",
1594
-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
1595
-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
1596
-	"TotalPopSize.Max.",
1597
-	"keepEvery",
1598
-	"PDBaseline", "n2", "onlyCancer")], 
1599
-    booktabs = TRUE,
1600
-	col.names = c("Elapsed Time, average per simulation (s)",
1601
-	              "Object Size, average per simulation (MB)",
1602
-				  "Number of Clones, median",
1603
-				  "Number of Iterations, median",
1604
-				  "Final Time, median",
1605
-				  "Total Population Size, median",
1606
-				  "Total Population Size, mean",
1607
-				  "Total Population Size, max.",				  
1608
-				  "keepEvery",
1609
-				  "PDBaseline", "n2", "onlyCancer"
1610
-				  ),
1611
-##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and
1612
-##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 
1613
-	align = "c")
1774
+## data(benchmark_1_0.05)
1775
+## knitr::kable(benchmark_1_0.05[, c("time_per_simul",
1776
+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
1777
+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
1778
+## 	"TotalPopSize.Max.",
1779
+## 	"keepEvery",
1780
+## 	"PDBaseline", "n2", "onlyCancer")], 
1781
+##     booktabs = TRUE,
1782
+## 	col.names = c("Elapsed Time, average per simulation (s)",
1783
+## 	              "Object Size, average per simulation (MB)",
1784
+## 				  "Number of Clones, median",
1785
+## 				  "Number of Iterations, median",
1786
+## 				  "Final Time, median",
1787
+## 				  "Total Population Size, median",
1788
+## 				  "Total Population Size, mean",
1789
+## 				  "Total Population Size, max.",				  
1790
+## 				  "keepEvery",
1791
+## 				  "PDBaseline", "n2", "onlyCancer"
1792
+## 				  ),
1793
+## ##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and
1794
+## ##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 
1795
+## 	align = "c")
1796
+	
1797
+panderOptions("table.split.table", 99999999)
1798
+## panderOptions("table.split.cells", 900)  ## For HTML
1799
+panderOptions("table.split.cells", 19)
1800
+
1801
+set.alignment('right') 
1802
+panderOptions('round', 3)
1803
+	
1804
+pander(benchmark_1_0.05[ , c("Elapsed Time, average per simulation (s)",
1805
+ 	              "Object Size, average per simulation (MB)", 
1806
+				  "Number of Clones, median", 
1807
+				  "Number of Iterations, median", 
1808
+				  "Final Time, median", 
1809
+				  "Total Population Size, median", 
1810
+				  "Total Population Size, mean", "Total Population Size, max.",
1811
+ 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 
1812
+ 	              ## caption = "\\label{tab:timing3xf}Benchmarks of all models in Table \\@ref(tab:allr1bck) using $s=0.05$ (instead of $s=0.1$)."  
1813
+)  
1814
+				  
1614 1815
 ```
1615 1816
 
1817
+\eslandscape
1818
+
1819
+\clearpage
1820
+
1616 1821
 As expected, having a smaller $s$ leads to slower processes in most cases,
1617 1822
 since it takes longer to reach the exiting conditions sooner. Particularly
1618 1823
 noticeable are the runs for the McFL models (notice the increases in
1619
-population size and number of iterations). 
1824
+population size and number of iterations ---see also below). 
1620 1825
 
1621 1826
 
1622 1827
 That is not the case, however, for `exp5` and `exp6` (and `exp5_noc` and
... ...
@@ -1651,6 +1856,15 @@ additional iterations. They exit sooner in terms of time periods,
1651 1856
 but they do much more work before arriving there.
1652 1857
 
1653 1858
 
1859
+The setting of `checkSizePEvery` is also having a huge effect on the McFL
1860
+model simulations (the number of iterations is $>10^6$). Even more than in
1861
+the previous section, checking the exiting condition more often (smaller
1862
+`checkSizePEvery`) would probably be justified here (notice also the very
1863
+large final times) and would lead to a sharp decrease in number of
1864
+iterations and, thus, running time.
1865
+
1866
+
1867
+
1654 1868
 The moral here is that in complex simulations like this, the effects
1655 1869
 of some parameters ($s$ in this case) might look counter-intuitive
1656 1870
 at first. Thus the need to "experiment before launching a large
... ...
@@ -1680,34 +1894,41 @@ pancr <- allFitnessEffects(
1680 1894
 	             "MLL3", "TGFBR2", "PXDN"))
1681 1895
 
1682 1896
 
1683
-## Random fitness landscape with 6 genes 
1897
+## Random fitness landscape with 6 genes
1898
+## At least 50 accessible genotypes
1684 1899
 rfl6 <- rfitness(6, min_accessible_genotypes = 50)
1685
-attributes(rfl6)$accessible_genotypes ## How many actually accessible
1900
+attributes(rfl6)$accessible_genotypes ## How many accessible
1686 1901
 rf6 <- allFitnessEffects(genotFitness = rfl6)
1687 1902
 
1688 1903
 
1689 1904
 ## Random fitness landscape with 12 genes
1905
+## At least 200 accessible genotypes
1690 1906
 rfl12 <- rfitness(12, min_accessible_genotypes = 200)
1691
-attributes(rfl12)$accessible_genotypes ## How many actually accessible
1907
+attributes(rfl12)$accessible_genotypes ## How many accessible
1692 1908
 rf12 <- allFitnessEffects(genotFitness = rfl12)
1693 1909
 
1694 1910
 
1695 1911
 
1696 1912
 
1697 1913
 ## Independent genes; positive fitness from exponential distribution
1698
-## mean around 0.1, and negative from exponential with mean around 0.02.
1699
-## Half positive, half negative
1914
+## mean around 0.1, and negative from exponential with mean around 
1915
+## 0.02. Half of genes positive fitness effects, half negative.
1916
+
1700 1917
 ng <- 200
1701
-re_200 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))
1918
+re_200 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 
1919
+                                           -rexp(ng/2, 50)))
1702 1920
 
1703 1921
 ng <- 500
1704
-re_500 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))
1922
+re_500 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 
1923
+                                           -rexp(ng/2, 50)))
1705 1924
 
1706 1925
 ng <- 2000
1707
-re_2000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))
1926
+re_2000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 
1927
+                                            -rexp(ng/2, 50)))
1708 1928
 
1709 1929
 ng <- 4000
1710
-re_4000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))
1930
+re_4000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 
1931
+                                            -rexp(ng/2, 50)))
1711 1932
 
1712 1933
 ```
1713 1934
 
... ...
@@ -1769,12 +1990,12 @@ oncoSimulPop(Nindiv,
1769 1990
 
1770 1991
 
1771 1992
 For the exponential model we will stop simulations when the populations
1772
-gets $>10^6$ cells (simulations start from 500 cells). For the McFarland
1993
+have $>10^6$ cells (simulations start from 500 cells). For the McFarland
1773 1994
 model we will use the `detectionProb` mechanism (see section
1774 1995
 \@ref(detectprob) for details); we could have used as stopping mechanism
1775 1996
 `detectionSize = 2 * initSize` (which would be basically equivalent to
1776 1997
 reaching cancer, as argued in [@McFarland2013]) but we want to provide
1777
-further runs under the `detectionProb` mechanism. We will start from 1000
1998
+further examples under the `detectionProb` mechanism. We will start from 1000
1778 1999
 cells, not 500 (starting from 1000 we almost always reach cancer in a
1779 2000
 single execution).
1780 2001
 
... ...
@@ -1818,42 +2039,69 @@ met.
1818 2039
 
1819 2040
 
1820 2041
 
1821
-```{r loadbench2usual, echo = FALSE, eval = TRUE} 
1822
-data(benchmark_2) 
1823
-``` 
2042
+<!-- ```{r loadbench2usual, echo = FALSE, eval = TRUE}  -->
2043
+<!-- data(benchmark_2)  -->
2044
+<!-- ```  -->
1824 2045
 
1825 2046
 The results of the benchmarks, using `r unique(benchmark_2$Numindiv)`
1826 2047
 individual simulations, are shown in Table \@ref(tab:timingusual).
1827 2048
 
1828 2049
 
2050
+\blandscape
1829 2051
 
1830
-Table: (\#tab:timingusual) Benchmarks under some common use cases,
1831
-set 1.
2052
+Table: (\#tab:timingusual) Benchmarks under some common use cases, set 1.
1832 2053
 ```{r benchustable, eval=TRUE, echo = FALSE}
1833
-data(benchmark_2)
1834
-
1835
-knitr::kable(benchmark_2[, c("Model", "fitness", "time_per_simul",
1836
-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
1837
-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
1838
-	"TotalPopSize.Max.")], 
1839
-    booktabs = TRUE,
1840
-	col.names = c("Model",
1841
-				  "Fitness",
1842
-	"Elapsed Time, average per simulation (s)",
1843
-	              "Object Size, average per simulation (MB)",
1844
-				  "Number of Clones, median",
1845
-				  "Number of Iterations, median",
1846
-				  "Final Time, median",
1847
-				  "Total Population Size, median",
1848
-				  "Total Population Size, mean",
1849
-				  "Total Population Size, max."
1850
-				  ),
1851
-	align = "c")
2054
+## data(benchmark_2)
2055
+
2056
+## knitr::kable(benchmark_2[, c("Model", "fitness", "time_per_simul",
2057
+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
2058
+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
2059
+## 	"TotalPopSize.Max.")], 
2060
+##     booktabs = TRUE,
2061
+## 	col.names = c("Model",
2062
+## 				  "Fitness",
2063
+## 	"Elapsed Time, average per simulation (s)",
2064
+## 	              "Object Size, average per simulation (MB)",
2065
+## 				  "Number of Clones, median",
2066
+## 				  "Number of Iterations, median",
2067
+## 				  "Final Time, median",
2068
+## 				  "Total Population Size, median",
2069
+## 				  "Total Population Size, mean",
2070
+## 				  "Total Population Size, max."
2071
+## 				  ),
2072
+## 	align = "c")
2073
+
2074
+panderOptions("table.split.table", 99999999)
2075
+panderOptions("table.split.cells", 900)  ## For HTML
2076
+## panderOptions("table.split.cells", 8) ## For PDF
2077
+
2078
+## set.alignment('right', row.names = 'center')
2079
+panderOptions('table.alignment.default', 'right')
2080
+
2081
+panderOptions('round', 3)
2082
+
2083
+pander(benchmark_2[ , c(
2084
+    "Model", "Fitness",
2085
+    "Elapsed Time, average per simulation (s)",
2086
+ 	              "Object Size, average per simulation (MB)",
2087
+ 				  "Number of Clones, median",
2088
+ 				  "Number of Iterations, median",
2089
+ 				  "Final Time, median",
2090
+ 				  "Total Population Size, median",
2091
+ 				  "Total Population Size, mean",				  
2092
+ 				  "Total Population Size, max.")], 
2093
+				  justify = c('left', 'left', rep('right', 8)),
2094
+				  ## caption = "\\label{tab:timingusual}Benchmarks under some common use cases, set 1." 
2095
+				  )	
2096
+	
1852 2097
 ```
1853 2098
 
2099
+\elandscape
2100
+
2101
+\clearpage
1854 2102
 
1855 2103
 In most cases, simulations run reasonably fast (under 0.1 seconds per
1856
-individual simulation) and the return objects are small. In will only
2104
+individual simulation) and the return objects are small. I will only
1857 2105
 focus on a few cases.
1858 2106
 
1859 2107
 The McFL model with random fitness landscape `rf12` and with `pancr` does
... ...
@@ -1927,29 +2175,56 @@ output in the 'miscell-files/vignette_bench_Rout' directory of the
1927 2175
 main OncoSimul repository at https://github.com/rdiaz02/OncoSimul.
1928 2176
 The data are available as `data(benchmark_3)`.
1929 2177
 	
1930
-Table: (\#tab:timingusual2) Benchmarks under some common use cases,
1931
-set 2.	
2178
+\blandscape
2179
+
2180
+Table: (\#tab:timingusual2) Benchmarks under some common use cases, set 2.	
1932 2181
 ```{r benchustable2, eval=TRUE, echo = FALSE}
1933
-data(benchmark_3)
2182
+## data(benchmark_3)
2183
+
2184
+## knitr::kable(benchmark_3[, c("Model", "fitness", "time_per_simul",
2185
+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
2186
+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
2187
+## 	"TotalPopSize.Max.")], 
2188
+##     booktabs = TRUE,
2189
+## 	col.names = c("Model",
2190
+## 				  "Fitness", "Elapsed Time, average per simulation (s)",
2191
+## 	              "Object Size, average per simulation (MB)",
2192
+## 				  "Number of Clones, median",
2193
+## 				  "Number of Iterations, median",
2194
+## 				  "Final Time, median",
2195
+## 				  "Total Population Size, median",
2196
+## 				  "Total Population Size, mean",
2197
+## 				  "Total Population Size, max."
2198
+## 				  ),
2199
+## 	align = "c")
2200
+
2201
+panderOptions("table.split.table", 99999999)
2202
+panderOptions("table.split.cells", 900)  ## For HTML
2203
+## panderOptions("table.split.cells", 8) ## For PDF
2204
+
2205
+
2206
+panderOptions('round', 3)
2207
+panderOptions('table.alignment.default', 'right')
2208
+
2209
+pander(benchmark_3[ , c(
2210
+    "Model", "Fitness",
2211
+    "Elapsed Time, average per simulation (s)",
2212
+ 	              "Object Size, average per simulation (MB)",
2213
+ 				  "Number of Clones, median",
2214
+ 				  "Number of Iterations, median",
2215
+ 				  "Final Time, median",
2216
+ 				  "Total Population Size, median",
2217
+ 				  "Total Population Size, mean",				  
2218
+ 				  "Total Population Size, max.")],
2219
+				  justify = c('left', 'left', rep('right', 8)),
2220
+				  ## caption = "\\label{tab:timingusual2}Benchmarks under some common use cases, set 2."
2221
+				  )	
2222
+```
1934 2223
 
1935
-knitr::kable(benchmark_3[, c("Model", "fitness", "time_per_simul",
1936
-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",
1937
-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 
1938
-	"TotalPopSize.Max.")], 
1939
-    booktabs = TRUE,
1940
-	col.names = c("Model",
1941
-				  "Fitness", "Elapsed Time, average per simulation (s)",
1942
-	              "Object Size, average per simulation (MB)",
1943
-				  "Number of Clones, median",
1944
-				  "Number of Iterations, median",
1945
-				  "Final Time, median",
1946
-				  "Total Population Size, median",
1947
-				  "Total Population Size, mean",
1948
-				  "Total Population Size, max."
1949
-				  ),
1950
-	align = "c")
2224
+\elandscape
2225
+
2226
+\clearpage
1951 2227
 
1952
-```
1953 2228
 
1954 2229
 Since we increased the maximum final time and forced runs to "reach
1955 2230
 cancer" the McFL run with the pancreas fitness specification takes a bit
... ...
@@ -1969,7 +2244,6 @@ take longer than their McFL counterparts) and the number of clones created
1969 2244
 is much smaller.
1970 2245
 
1971 2246
 
1972
-
1973 2247
 ## Can we use a large number of genes? {#lnum}
1974 2248
 
1975 2249
 Yes. In fact, in OncoSimulR there is no pre-set limit on genome
... ...
@@ -1996,18 +2270,16 @@ stop when the population grows over $1e6$ individuals:
1996 2270
 ```{r exp10000, echo = TRUE, eval = FALSE}
1997 2271
 ng <- 10000
1998 2272
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
1999
-t_e_10000 <- system.time(e_10000 <- oncoSimulPop(5,
2000
-                                                 u,
2001
-                                                 model = "Exp",
2002
-                                                 mu = 1e-7,
2003
-                                                 detectionSize = 1e6,
2004
-                                                 detectionDrivers = NA,
2005
-                                                 detectionProb = NA,
2006
-                                                 keepPhylog = TRUE,
2007
-                                                 onlyCancer = FALSE,
2008
-                                                 mutationPropGrowth = TRUE,
2009
-                                                 mc.cores = 1
2010
-                                ))
2273
+
2274
+t_e_10000 <- system.time(
2275
+    e_10000 <- oncoSimulPop(5, u, model = "Exp", mu = 1e-7,
2276
+                            detectionSize = 1e6,
2277
+                            detectionDrivers = NA,
2278
+                            detectionProb = NA,
2279
+                            keepPhylog = TRUE,
2280
+                            onlyCancer = FALSE,
2281
+                            mutationPropGrowth = TRUE,
2282
+                            mc.cores = 1))
2011 2283
 ```
2012 2284
 
2013 2285
 
... ...
@@ -2044,19 +2316,20 @@ NA` argument (this setting was explained in detail in section
2044 2316
 
2045 2317
 
2046 2318
 ```{r exp10000b, eval = FALSE, echo = TRUE}
2047
-t_e_10000b <- system.time(e_10000b <- oncoSimulPop(5,
2048
-                                                   u,
2049
-                                                   model = "Exp",
2050
-                                                   mu = 1e-7,
2051
-                                                   detectionSize = 1e6,
2052
-                                                   detectionDrivers = NA,
2053
-                                                   detectionProb = NA,
2054
-                                                   keepPhylog = TRUE,
2055
-                                                   onlyCancer = FALSE,
2056
-                                                   keepEvery = NA,
2057
-                                                   mutationPropGrowth = TRUE,
2058
-                                                   mc.cores = 1
2059
-                                ))
2319
+t_e_10000b <- system.time(
2320
+    e_10000b <- oncoSimulPop(5,
2321
+                             u,
2322
+                             model = "Exp",
2323
+                             mu = 1e-7,
2324
+                             detectionSize = 1e6,
2325
+                             detectionDrivers = NA,
2326
+                             detectionProb = NA,
2327
+                             keepPhylog = TRUE,
2328
+                             onlyCancer = FALSE,
2329
+                             keepEvery = NA,
2330
+                             mutationPropGrowth = TRUE,
2331
+                             mc.cores = 1
2332
+                             ))
2060 2333
 
2061 2334
 ```
2062 2335
 
... ...
@@ -2089,19 +2362,20 @@ reasonable decision depends on the problem; see also below.
2089 2362
 ```{r exp50000, echo = TRUE, eval = FALSE}
2090 2363
 ng <- 50000
2091 2364
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2092
-t_e_50000 <- system.time(e_50000 <- oncoSimulPop(5,
2093
-                                                 u,
2094
-                                                 model = "Exp",
2095
-                                                 mu = 1e-7,
2096
-                                                 detectionSize = 1e6,
2097
-                                                 detectionDrivers = NA,
2098
-                                                 detectionProb = NA,
2099
-                                                 keepPhylog = TRUE,
2100
-                                                 onlyCancer = FALSE,
2101
-                                                 keepEvery = NA,
2102
-                                                 mutationPropGrowth = FALSE,
2103
-                                                 mc.cores = 1
2104
-                                                 ))
2365
+t_e_50000 <- system.time(
2366
+    e_50000 <- oncoSimulPop(5,
2367
+                            u,
2368
+                            model = "Exp",
2369
+                            mu = 1e-7,
2370
+                            detectionSize = 1e6,
2371
+                            detectionDrivers = NA,
2372
+                            detectionProb = NA,
2373
+                            keepPhylog = TRUE,
2374
+                            onlyCancer = FALSE,
2375
+                            keepEvery = NA,
2376
+                            mutationPropGrowth = FALSE,
2377
+                            mc.cores = 1
2378
+                            ))
2105 2379
 
2106 2380
 
2107 2381
 t_e_50000
... ...
@@ -2131,19 +2405,20 @@ What if we had not pruned?
2131 2405
 ```{r exp50000np, echo = TRUE, eval = FALSE}
2132 2406
 ng <- 50000
2133 2407
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2134
-t_e_50000np <- system.time(e_50000np <- oncoSimulPop(5,
2135
-                                                 u,
2136
-                                                 model = "Exp",
2137
-                                                 mu = 1e-7,
2138
-                                                 detectionSize = 1e6,
2139
-                                                 detectionDrivers = NA,
2140
-                                                 detectionProb = NA,
2141
-                                                 keepPhylog = TRUE,
2142
-                                                 onlyCancer = FALSE,
2143
-                                                 keepEvery = 1,
2144
-                                                 mutationPropGrowth = FALSE,
2145
-                                                 mc.cores = 1
2146
-                                                 ))
2408
+t_e_50000np <- system.time(
2409
+    e_50000np <- oncoSimulPop(5,
2410
+                              u,
2411
+                              model = "Exp",
2412
+                              mu = 1e-7,
2413
+                              detectionSize = 1e6,
2414
+                              detectionDrivers = NA,
2415
+                              detectionProb = NA,
2416
+                              keepPhylog = TRUE,
2417
+                              onlyCancer = FALSE,
2418
+                              keepEvery = 1,
2419
+                              mutationPropGrowth = FALSE,
2420
+                              mc.cores = 1
2421
+                              ))
2147 2422
 
2148 2423
 t_e_50000np
2149 2424
 ##   user  system elapsed
... ...
@@ -2176,19 +2451,20 @@ What about the `mutationPropGrowth` setting? We will rerun the example in
2176 2451
 ng <- 50000
2177 2452
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2178 2453
 
2179
-t_e_50000c <- system.time(e_50000c <- oncoSimulPop(5,
2180
-                                                 u,
2181
-                                                 model = "Exp",
2182
-                                                 mu = 1e-7,
2183
-                                                 detectionSize = 1e6,
2184
-                                                 detectionDrivers = NA,
2185
-                                                 detectionProb = NA,
2186
-                                                 keepPhylog = TRUE,
2187
-                                                 onlyCancer = FALSE,
2188
-                                                 keepEvery = NA,
2189
-                                                 mutationPropGrowth = TRUE,
2190
-                                                 mc.cores = 1
2191
-                                                 ))
2454
+t_e_50000c <- system.time(
2455
+    e_50000c <- oncoSimulPop(5,
2456
+                             u,
2457
+                             model = "Exp",
2458
+                             mu = 1e-7,
2459
+                             detectionSize = 1e6,
2460
+                             detectionDrivers = NA,
2461
+                             detectionProb = NA,
2462
+                             keepPhylog = TRUE,
2463
+                             onlyCancer = FALSE,
2464
+                             keepEvery = NA,
2465
+                             mutationPropGrowth = TRUE,
2466
+                             mc.cores = 1
2467
+                             ))
2192 2468
 
2193 2469
 t_e_50000c
2194 2470
 ##    user  system elapsed 
... ...
@@ -2238,19 +2514,20 @@ Let's start with  `mutationPropGrowth = FALSE` and `keepEvery = NA`:
2238 2514
 ng <- 50000
2239 2515
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2240 2516
 
2241
-t_mc_50000_nmpg <- system.time(mc_50000_nmpg <- oncoSimulPop(5,
2242
-                                                   u,
2243
-                                                   model = "McFL",
2244
-                                                   mu = 1e-7,
2245
-                                                   detectionSize = 1e6,
2246
-                                                   detectionDrivers = NA,
2247
-                                                   detectionProb = NA,
2248
-                                                   keepPhylog = TRUE,
2249
-                                                   onlyCancer = FALSE,
2250
-                                                   keepEvery = NA,
2251
-                                                   mutationPropGrowth = FALSE,
2252
-                                                   mc.cores = 1
2253
-                                                   ))
2517
+t_mc_50000_nmpg <- system.time(
2518
+    mc_50000_nmpg <- oncoSimulPop(5,
2519
+                                  u,
2520
+                                  model = "McFL",
2521
+                                  mu = 1e-7,
2522
+                                  detectionSize = 1e6,
2523
+                                  detectionDrivers = NA,
2524
+                                  detectionProb = NA,
2525
+                                  keepPhylog = TRUE,
2526
+                                  onlyCancer = FALSE,
2527
+                                  keepEvery = NA,
2528
+                                  mutationPropGrowth = FALSE,
2529
+                                  mc.cores = 1
2530
+                                  ))
2254 2531
 t_mc_50000_nmpg
2255 2532
 ##   user  system elapsed 
2256 2533
 ##  30.46    0.54   31.01 
... ...
@@ -2277,19 +2554,20 @@ Setting `keepEvery = 1` (i.e., keeping track of clones with an
2277 2554
 interval of 1):
2278 2555
 
2279 2556
 ```{r mc50000_kp, echo = TRUE, eval = FALSE}
2280
-t_mc_50000_nmpg_k <- system.time(mc_50000_nmpg_k <- oncoSimulPop(5,
2281
-                                                   u,
2282
-                                                   model = "McFL",
2283
-                                                   mu = 1e-7,
2284
-                                                   detectionSize = 1e6,
2285
-                                                   detectionDrivers = NA,
2286
-                                                   detectionProb = NA,
2287
-                                                   keepPhylog = TRUE,
2288
-                                                   onlyCancer = FALSE,
2289
-                                                   keepEvery = 1,
2290
-                                                   mutationPropGrowth = FALSE,
2291
-                                                   mc.cores = 1
2292
-                                                   ))
2557
+t_mc_50000_nmpg_k <- system.time(
2558
+    mc_50000_nmpg_k <- oncoSimulPop(5,
2559
+                                    u,
2560
+                                    model = "McFL",
2561
+                                    mu = 1e-7,
2562
+                                    detectionSize = 1e6,
2563
+                                    detectionDrivers = NA,
2564
+                                    detectionProb = NA,
2565
+                                    keepPhylog = TRUE,
2566
+                                    onlyCancer = FALSE,
2567
+                                    keepEvery = 1,
2568
+                                    mutationPropGrowth = FALSE,
2569
+                                    mc.cores = 1
2570
+                                    ))
2293 2571
 
2294 2572
 t_mc_50000_nmpg_k
2295 2573
 ##    user  system elapsed 
... ...
@@ -2323,19 +2601,20 @@ detection size by a factor of 3:
2323 2601
 ng <- 50000
2324 2602
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2325 2603
 
2326
-t_mc_50000_nmpg_3e6 <- system.time(mc_50000_nmpg_3e6 <- oncoSimulPop(5,
2327
-                                                   u,
2328
-                                                   model = "McFL",
2329
-                                                   mu = 1e-7,
2330
-                                                   detectionSize = 3e6,
2331
-                                                   detectionDrivers = NA,
2332
-                                                   detectionProb = NA,
2333
-                                                   keepPhylog = TRUE,
2334
-                                                   onlyCancer = FALSE,
2335
-                                                   keepEvery = NA,
2336
-                                                   mutationPropGrowth = FALSE,
2337
-                                                   mc.cores = 1
2338
-                                                   ))
2604
+t_mc_50000_nmpg_3e6 <- system.time(
2605
+    mc_50000_nmpg_3e6 <- oncoSimulPop(5,
2606
+                                      u,
2607
+                                      model = "McFL",
2608
+                                      mu = 1e-7,
2609
+                                      detectionSize = 3e6,
2610
+                                      detectionDrivers = NA,
2611
+                                      detectionProb = NA,
2612
+                                      keepPhylog = TRUE,
2613
+                                      onlyCancer = FALSE,
2614
+                                      keepEvery = NA,
2615
+                                      mutationPropGrowth = FALSE,
2616
+                                      mc.cores = 1
2617
+                                      ))
2339 2618
 t_mc_50000_nmpg_3e6
2340 2619
 ##    user  system elapsed 
2341 2620
 ##  77.240   1.064  78.308 
... ...
@@ -2367,19 +2646,20 @@ Let us use the same `detectionSize = 1e6` as in the first example
2367 2646
 ng <- 50000
2368 2647
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2369 2648
 
2370
-t_mc_50000_nmpg_5mu <- system.time(mc_50000_nmpg_5mu <- oncoSimulPop(5,
2371
-                                                   u,
2372
-                                                   model = "McFL",
2373
-                                                   mu = 5e-7,
2374
-                                                   detectionSize = 1e6,
2375
-                                                   detectionDrivers = NA,
2376
-                                                   detectionProb = NA,
2377
-                                                   keepPhylog = TRUE,
2378
-                                                   onlyCancer = FALSE,
2379
-                                                   keepEvery = NA,
2380
-                                                   mutationPropGrowth = FALSE,
2381
-                                                   mc.cores = 1
2382
-                                                   ))
2649
+t_mc_50000_nmpg_5mu <- system.time(
2650
+    mc_50000_nmpg_5mu <- oncoSimulPop(5,
2651
+                                      u,
2652
+                                      model = "McFL",
2653
+                                      mu = 5e-7,
2654
+                                      detectionSize = 1e6,
2655
+                                      detectionDrivers = NA,
2656
+                                      detectionProb = NA,
2657
+                                      keepPhylog = TRUE,
2658
+                                      onlyCancer = FALSE,
2659
+                                      keepEvery = NA,
2660
+                                      mutationPropGrowth = FALSE,
2661
+                                      mc.cores = 1
2662
+                                      ))
2383 2663
 
2384 2664
 t_mc_50000_nmpg_5mu
2385 2665
 ##    user  system elapsed 
... ...
@@ -2415,19 +2695,20 @@ with further details in \@ref(prune)).
2415 2695
 Finally, let's run the above example but with `keepEvery = 1`:
2416 2696
 
2417 2697
 ```{r mcf5muk, echo = TRUE, eval = FALSE}
2418
-t_mc_50000_nmpg_5mu_k <- system.time(mc_50000_nmpg_5mu_k <- oncoSimulPop(5,
2419
-                                                   u,
2420
-                                                   model = "McFL",
2421
-                                                   mu = 5e-7,
2422
-                                                   detectionSize = 1e6,
2423
-                                                   detectionDrivers = NA,
2424
-                                                   detectionProb = NA,
2425
-                                                   keepPhylog = TRUE,
2426
-                                                   onlyCancer = FALSE,
2427
-                                                   keepEvery = 1,
2428
-                                                   mutationPropGrowth = FALSE,
2429
-                                                   mc.cores = 1
2430
-                                                   ))
2698
+t_mc_50000_nmpg_5mu_k <- system.time(
2699
+    mc_50000_nmpg_5mu_k <- oncoSimulPop(5,
2700
+                                        u,
2701
+                                        model = "McFL",
2702
+                                        mu = 5e-7,
2703
+                                        detectionSize = 1e6,
2704
+                                        detectionDrivers = NA,
2705
+                                        detectionProb = NA,
2706
+                                        keepPhylog = TRUE,
2707
+                                        onlyCancer = FALSE,
2708
+                                        keepEvery = 1,
2709
+                                        mutationPropGrowth = FALSE,
2710
+                                        mc.cores = 1
2711
+                                        ))
2431 2712
 												   
2432 2713
 t_mc_50000_nmpg_5mu_k
2433 2714
 ##    user  system elapsed 
... ...
@@ -2470,19 +2751,20 @@ default of `mutationPropGrowth = TRUE`:
2470 2751
 ng <- 50000
2471 2752
 u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng/2), rep(-0.1, ng/2)))
2472 2753
 
2473
-t_mc_50000 <- system.time(mc_50000 <- oncoSimulPop(5,
2474
-                                                   u,
2475
-                                                   model = "McFL",
2476
-                                                   mu = 1e-7,
2477
-                                                   detectionSize = 1e6,
2478
-                                                   detectionDrivers = NA,
2479
-                                                   detectionProb = NA,
2480
-                                                   keepPhylog = TRUE,
2481
-                                                   onlyCancer = FALSE,
2482
-                                                   keepEvery = NA,
2483
-                                                   mutationPropGrowth = TRUE,
2484
-                                                   mc.cores = 1
2485
-                                                   ))
2754
+t_mc_50000 <- system.time(
2755
+    mc_50000 <- oncoSimulPop(5,
2756
+                             u,
2757
+                             model = "McFL",
2758
+                             mu = 1e-7,
2759
+                             detectionSize = 1e6,
2760
+                             detectionDrivers = NA,
2761
+                             detectionProb = NA,
2762
+                             keepPhylog = TRUE,
2763
+                             onlyCancer = FALSE,
2764
+                             keepEvery = NA,
2765
+                             mutationPropGrowth = TRUE,
2766
+                             mc.cores = 1
2767
+                             ))
2486 2768
 
2487 2769
 t_mc_50000
2488 2770
 ##    user  system elapsed 
... ...
@@ -2518,19 +2800,20 @@ single out a couple of cases here.
2518 2800
 First, we repeat the run shown in section \@ref(mc50000ex5):
2519 2801
 
2520 2802
 ```{r mcf5muk005, echo = TRUE, eval = FALSE}
2521
-t_mc_50000_nmpg_5mu_k <- system.time(mc_50000_nmpg_5mu_k <- oncoSimulPop(2,
2522
-                                                   u,
2523
-                                                   model = "McFL",
2524
-                                                   mu = 5e-7,
2525
-                                                   detectionSize = 1e6,
2526
-                                                   detectionDrivers = NA,
2527
-                                                   detectionProb = NA,
2528
-                                                   keepPhylog = TRUE,
2529
-                                                   onlyCancer = FALSE,
2530
-                                                   keepEvery = 1,
2531
-                                                   mutationPropGrowth = FALSE,
2532
-                                                   mc.cores = 1
2533
-                                                   ))
2803
+t_mc_50000_nmpg_5mu_k <- system.time(
2804
+    mc_50000_nmpg_5mu_k <- oncoSimulPop(2,
2805
+                                        u,
2806
+                                        model = "McFL",
2807
+                                        mu = 5e-7,
2808
+                                        detectionSize = 1e6,
2809
+                                        detectionDrivers = NA,
2810
+                                        detectionProb = NA,
2811
+                                        keepPhylog = TRUE,
2812
+                                        onlyCancer = FALSE,
2813
+                                        keepEvery = 1,
2814
+                                        mutationPropGrowth = FALSE,
2815
+                                        mc.cores = 1
2816
+                                        ))
2534 2817
 t_mc_50000_nmpg_5mu_k
2535 2818
 ##    user  system elapsed 
2536 2819
 ## 305.512   5.164 310.711 
... ...
@@ -2566,19 +2849,20 @@ $10^6$ starting from an equilibrium population of 500 we need about
2566 2849
 Next, let us rerun \@ref(mc50000ex1):
2567 2850
 
2568 2851
 ```{r mc50000_1_005, echo = TRUE, eval = FALSE}
2569
-t_mc_50000_nmpg <- system.time(mc_50000_nmpg <- oncoSimulPop(5,
2570
-                                                   u,
2571
-                                                   model = "McFL",
2572
-                                                   mu = 1e-7,
2573
-                                                   detectionSize = 1e6,
2574
-                                                   detectionDrivers = NA,
2575
-                                                   detectionProb = NA,
2576
-                                                   keepPhylog = TRUE,
2577
-                                                   onlyCancer = FALSE,
2578
-                                                   keepEvery = NA,
2579
-                                                   mutationPropGrowth = FALSE,
2580
-                                                   mc.cores = 1
2581
-                                                   ))
2852
+t_mc_50000_nmpg <- system.time(
2853
+    mc_50000_nmpg <- oncoSimulPop(5,
2854
+                                  u,
2855
+                                  model = "McFL",
2856
+                                  mu = 1e-7,
2857
+                                  detectionSize = 1e6,
2858
+                                  detectionDrivers = NA,
2859
+                                  detectionProb = NA,
2860
+                                  keepPhylog = TRUE,
2861
+                                  onlyCancer = FALSE,
2862
+                                  keepEvery = NA,
2863
+                                  mutationPropGrowth = FALSE,
2864
+                                  mc.cores = 1
2865
+                                  ))
2582 2866
 t_mc_50000_nmpg
2583 2867
 ##    user  system elapsed 
2584 2868
 ## 111.236   0.596 111.834 
... ...
@@ -2790,22 +3074,23 @@ u <- allFitnessEffects(noIntGenes = c(rep(0.1, ng)))
2790 3074
 
2791 3075
 
2792 3076
 ```{r ex-large-mf, eval = FALSE, echo = TRUE}
2793
-t_mc_k_50_1e11 <- system.time(mc_k_50_1e11 <- oncoSimulPop(5,
2794
-                                                     u,
2795
-                                                     model = "McFL",
2796
-                                                     mu = 1e-7,
2797
-                                                     detectionSize = 1e11,
2798
-                                                     initSize = 1e5,
2799
-                                                     detectionDrivers = NA,
2800
-                                                     detectionProb = NA,
2801
-                                                     keepPhylog = TRUE,
2802
-                                                     onlyCancer = FALSE,
2803
-                                                     mutationPropGrowth = FALSE,
2804
-                                                     keepEvery = 1,
2805
-                                                     finalTime = 5000,
2806
-                                                     mc.cores = 1,
2807
-                                                     max.wall.time = 600
2808
-                                                     ))
3077
+t_mc_k_50_1e11 <- system.time(
3078
+    mc_k_50_1e11 <- oncoSimulPop(5,
3079
+                                 u,
3080
+                                 model = "McFL",
3081
+                                 mu = 1e-7,
3082
+                                 detectionSize = 1e11,
3083
+                                 initSize = 1e5,
3084
+                                 detectionDrivers = NA,
3085
+                                 detectionProb = NA,
3086
+                                 keepPhylog = TRUE,
3087
+                                 onlyCancer = FALSE,
3088
+                                 mutationPropGrowth = FALSE,
3089
+                                 keepEvery = 1,
3090
+                                 finalTime = 5000,
3091
+                                 mc.cores = 1,
3092
+                                 max.wall.time = 600
3093
+                                 ))
2809 3094
 
2810 3095
 ## Recoverable exception ti set to DBL_MIN. Rerunning.
2811 3096
 ## Recoverable exception ti set to DBL_MIN. Rerunning.
... ...
@@ -2836,24 +3121,25 @@ cases).
2836 3121
 Now the exponential model with `detectionSize = 1e11`:
2837 3122
 
2838 3123
 ```{r ex-large-exp, eval = FALSE, echo = TRUE}
2839
-t_exp_k_50_1e11 <- system.time(exp_k_50_1e11 <- oncoSimulPop(5,
2840
-                                                     u,
2841
-                                                     model = "Exp",
2842
-                                                     mu = 1e-7,
2843
-                                                     detectionSize = 1e11,
2844
-                                                     initSize = 1e5,
2845
-                                                     detectionDrivers = NA,
2846
-                                                     detectionProb = NA,
2847
-                                                     keepPhylog = TRUE,
2848
-                                                     onlyCancer = FALSE,
2849
-                                                     mutationPropGrowth = FALSE,
2850
-                                                     keepEvery = 1,
2851
-                                                     finalTime = 5000,
2852
-                                                     mc.cores = 1,
2853
-                                                     max.wall.time = 600,
2854
-                                                     errorHitWallTime = FALSE,
2855
-                                                     errorHitMaxTries = FALSE
2856
-                                                     ))
3124
+t_exp_k_50_1e11 <- system.time(
3125
+    exp_k_50_1e11 <- oncoSimulPop(5,
3126
+                                  u,
3127
+                                  model = "Exp",
3128
+                                  mu = 1e-7,
3129
+                                  detectionSize = 1e11,
3130
+                                  initSize = 1e5,
3131
+                                  detectionDrivers = NA,
3132
+                                  detectionProb = NA,
3133
+                                  keepPhylog = TRUE,
3134
+                                  onlyCancer = FALSE,
3135
+                                  mutationPropGrowth = FALSE,
3136
+                                  keepEvery = 1,
3137
+                                  finalTime = 5000,
3138
+                                  mc.cores = 1,
3139
+                                  max.wall.time = 600,
3140
+                                  errorHitWallTime = FALSE,
3141
+                                  errorHitMaxTries = FALSE
3142
+                                  ))
2857 3143
 
2858 3144
 ## Recoverable exception ti set to DBL_MIN. Rerunning.
2859 3145
 ## Hitted wall time. Exiting.
... ...
@@ -2935,7 +3221,7 @@ To summarize this section, we have seen:
2935 3221
 
2936 3222
 
2937 3223
 
2938
-
3224
+\clearpage
2939 3225
 
2940 3226
 
2941 3227
 # Specifying fitness effects {#specfit}
... ...
@@ -4060,7 +4346,7 @@ c(1.01 * 1.02, 1.02, 1.02 * 1.1, 0.1 * 1.3, 1.03,
4060 4346
 
4061 4347
 ## Order effects {#oe}
4062 4348
 
4063
-As explained in the introduction (\@ref(intro)), by order effects we
4349
+As explained in the introduction (section \@ref(introdd)), by order effects we
4064 4350
 mean a phenomenon such as the one shown empirically by @Ortmann2015:
4065 4351
 the fitness of a double mutant "A", "B" is different depending on
4066 4352
 whether "A" was acquired before "B" or "B" before "A". This, of
... ...
@@ -4386,7 +4672,7 @@ evalAllGenotypes(sv, order = FALSE, addwt = TRUE, model = "Bozic")
4386 4672
 ```
4387 4673
 
4388 4674
 What gives here? The simulation code would alert you of this (see section
4389
-\@ref(fit-neg-pos)) in this particular case because there are ``-1",
4675
+\@ref(ex-0-death)) in this particular case because there are ``-1",
4390 4676
 which might indicate that this is not what you want. The problem is that
4391 4677
 you probably want the Death rate to be infinity (the birth rate was 0, so
4392 4678
 no clone viability, when we used birth rates ---section \@ref(noviab)).
... ...
@@ -5207,6 +5493,7 @@ plot(mue1, addtot = TRUE,
5207 5493
 par(op)
5208 5494
 ``` 
5209 5495
 
5496
+\clearpage
5210 5497
 
5211 5498
 # Plotting fitness landscapes {#plot-fit-land}
5212 5499
 
... ...
@@ -5287,6 +5574,8 @@ plot(evalAllGenotypes(pancr, order = FALSE), use_ggrepel = TRUE)
5287 5574
 
5288 5575
 <!-- \clearpage -->
5289 5576
 
5577
+\clearpage
5578
+
5290 5579
 # Specifying fitness effects: some examples from the literature {#litex}
5291 5580
 
5292 5581
 ## Bauer et al., 2014 {#bauer}
... ...
@@ -5915,7 +6204,7 @@ plot(rv2, expandModules = TRUE,   autofit = TRUE)
5915 6204
 <!-- %%       autofit = TRUE, -->
5916 6205
 <!-- %%       scale_char = 8) -->
5917 6206
 
5918
-
6207
+\clearpage
5919 6208
 
5920 6209
 # Running and plotting the simulations: starting, ending, and examples {#simul}
5921 6210
 
... ...
@@ -6920,6 +7209,7 @@ streamgraph(lb1, Genotype, Y, Time, scale = "continuous",
6920 7209
 <!-- %% but it gives me problems with knitr, etc). -->
6921 7210
 
6922 7211
 
7212
+\clearpage
6923 7213
 
6924 7214
 # Sampling multiple simulations {#sample}
6925 7215
 
... ...
@@ -7335,6 +7625,7 @@ are examining a large number of different scenarios.
7335 7625
 
7336 7626
 
7337 7627
 
7628
+\clearpage
7338 7629
 
7339 7630
 # Showing the genealogical relationships of clones {#phylog}
7340 7631
 
... ...
@@ -7552,6 +7843,7 @@ This is so far disabled in function `oncoSimulSample`, since
7552 7843
 that function is optimized for other uses. This might change in the future.
7553 7844
 
7554 7845
 
7846
+\clearpage
7555 7847
 
7556 7848
 # Generating random fitness landscapes {#gener-fit-land}
7557 7849
 
... ...
@@ -7616,10 +7908,10 @@ functions, or you could export them (`to_Magellan`) and plot them externally
7616 7908
 ## A small example
7617 7909
 rfitness(3)
7618 7910
 
7619
-## A 5-gene example, where the reference genotype is the one the one with
7620
-## all positions mutated, similar to Greene and Crona, 2014.  We will plot
7621
-## the landscape and use it for simulations We downplay the random
7622
-## component with a sd = 0.5
7911
+## A 5-gene example, where the reference genotype is the one the
7912
+## one with all positions mutated, similar to Greene and Crona,
7913
+## 2014.  We will plot the landscape and use it for simulations
7914
+## We downplay the random component with a sd = 0.5
7623 7915
 
7624 7916
 r1 <- rfitness(5, reference = rep(1, 5), sd = 0.6)
7625 7917
 plot(r1)
... ...
@@ -7636,6 +7928,7 @@ oncoSimulIndiv(allFitnessEffects(genotFitness = r1))
7636 7928
 
7637 7929
 <!-- % @  -->
7638 7930
 
7931
+\clearpage
7639 7932
 
7640 7933
 # Measures of evolutionary predictability and genotype diversity
7641 7934
 
... ...
@@ -7703,14 +7996,13 @@ example:
7703 7996
 
7704 7997
 
7705 7998
 ```{r lod_pom_ex}
7706
-pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A", 
7707
-                                          "TP53", "TP53", "MLL3"),
7708
-                                      child = c("KRAS","SMAD4", "CDNK2A", 
7709
-                                          "TP53", "MLL3",
7710
-                                          rep("PXDN", 3), rep("TGFBR2", 2)),
7711
-                                      s = 0.05,
7712
-                                      sh = -0.3,
7713
-                                      typeDep = "MN"))
7999
+pancr <- allFitnessEffects(
8000
+    data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A", 
8001
+                          "TP53", "TP53", "MLL3"),
8002
+               child = c("KRAS","SMAD4", "CDNK2A", 
8003
+                         "TP53", "MLL3",
8004
+                         rep("PXDN", 3), rep("TGFBR2", 2)),
8005
+               s = 0.05, sh = -0.3, typeDep = "MN"))
7714 8006
 
7715 8007
 pancr16 <- oncoSimulPop(16, pancr, model = "Exp", keepPhylog = TRUE,
7716 8008
                         mc.cores = 2)
... ...
@@ -7718,21 +8010,23 @@ pancr16 <- oncoSimulPop(16, pancr, model = "Exp", keepPhylog = TRUE,
7718 8010
 ## Look a the first POM 
7719 8011
 str(POM(pancr16)[1:3])
7720 8012
 
7721
-## Note here that for each simulation there is a "all_paths" and a "lod_single",
7722
-## as explained above
8013
+## Note here that for each simulation there is a "all_paths" and
8014
+## a "lod_single", as explained above
7723 8015
 LOD(pancr16)[1:2]
7724 8016
 
7725
-## The diversity of LOD (lod_single)  and POM might or might not be identical
8017
+## The diversity of LOD (lod_single) and POM might or might not
8018
+## be identical
7726 8019
 diversityPOM(POM(pancr16))
7727 8020
 diversityLOD(LOD(pancr16))
7728 8021
 
7729
-## Show the genotypes and their diversity (which might, or might not,
7730
-## differ from the diversity of LOD and POM)
8022
+## Show the genotypes and their diversity (which might, or might
8023
+## not, differ from the diversity of LOD and POM)
7731 8024
 sampledGenotypes(samplePop(pancr16))
7732 8025
 
7733 8026
 ```
7734 8027
 
7735 8028
 
8029
+\clearpage
7736 8030
 
7737 8031
 # FAQ, odds and ends 
7738 8032
 
... ...
@@ -8155,6 +8449,7 @@ questions where OncoSimulR could help.
8155 8449
 <!-- %% plot(otp) -->
8156 8450
 <!-- %% @  -->
8157 8451
 
8452
+\clearpage
8158 8453
 
8159 8454
 # Using v.1 posets and simulations {#v1}
8160 8455
 
... ...
@@ -8181,8 +8476,8 @@ plotPoset(p1, addroot = TRUE)
8181 8476
 ``` 
8182 8477
 
8183 8478
 ```{r, fig.height=3}
8184
-## A simple way to create a poset where no gene (in a set of 15) depends
8185
-## on any other.
8479
+## A simple way to create a poset where no gene (in a set of 15)
8480
+## depends on any other.
8186 8481
 p4 <- cbind(0L, 15L)
8187 8482
 plotPoset(p4, addroot = TRUE)
8188 8483
 ``` 
... ...
@@ -8448,6 +8743,7 @@ computing fitness, however, should deal with all of this just fine.
8448 8743
 
8449 8744
 
8450 8745
 
8746
+\clearpage
8451 8747
 
8452 8748
 # Session info and packages used
8453 8749
 
... ...
@@ -1,5 +1,6 @@
1 1
 \usepackage{rotating}
2 2
 \usepackage{array}
3
+\usepackage{pdflscape}
3 4
 
4 5
 \newcolumntype{L}[1]{>{\raggedright\let\newline\\
5 6
 \arraybackslash\hspace{0pt}}m{#1}}
... ...
@@ -9,7 +10,74 @@
9 10
 \arraybackslash\hspace{0pt}}m{#1}}
10 11
 \newcolumntype{P}[1]{>{\raggedright\tabularxbackslash}p{#1}}
11 12
 
12
-%%%Local Variables:
13
+%% http://stackoverflow.com/a/27334272
14
+% \newcommand{\blandscape}{\begin{landscape}}
15
+% \newcommand{\elandscape}{\end{landscape}}
16
+
17
+%% landscape does not allow sensible centering. Sideways does it.
18
+%% But does not rotate the page in the viewer
19
+% \newcommand{\bslandscape}{
20
+%     % \newgeometry{textwidth=297mm, textheight=100mm, left=0mm, bottom = 0mm}
21
+%   \newgeometry{margin=.2cm}
22
+%   \thispagestyle{empty}
23
+%   \begin{landscape}
24
+%     \begin{center}
25
+%       \footnotesize
26
+%   }
27
+%   \newcommand{\eslandscape}{
28
+%   \end{center}
29
+%   \end{landscape}
30
+%   \restoregeometry
31
+% }
32
+
33
+%%http://tex.stackexchange.com/a/212843
34
+% \newenvironment{rotatepage}%
35
+%     {\pagebreak[4]\global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 90}}%
36
+%     {\pagebreak[4]\global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 0}}%
37
+
38
+% \usepackage{etoolbox}    
39
+% \BeforeBeginEnvironment{sidewaystable*}{\begin{rotatepage}}
40
+% \AfterEndEnvironment{sidewaystable*}{\end{rotatepage}}  
41
+
42
+
43
+
44
+\newcommand{\blandscape}{
45
+  \clearpage
46
+  \pagebreak[4]
47
+  \global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 90}
48
+  \begin{sidewaystable*}[t!]
49
+  \centering
50
+  \footnotesize
51
+}
52
+
53
+\newcommand{\elandscape}{
54
+\end{sidewaystable*}
55
+\clearpage
56
+\pagebreak[4]
57
+\global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 0}
58
+}
59
+
60
+\newcommand{\bslandscape}{
61
+  \clearpage
62
+  \pagebreak[4]
63
+  \global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 90}
64
+
65
+  \newgeometry{margin=0.1cm} 
66
+  \begin{sidewaystable*}[t!]
67
+  \centering
68
+  \footnotesize
69
+}
70
+
71
+\newcommand{\eslandscape}{
72
+\end{sidewaystable*}
73
+\restoregeometry
74
+\clearpage
75
+\pagebreak[4]
76
+\global\pdfpageattr\expandafter{\the\pdfpageattr/Rotate 0}
77
+}
78
+
79
+
80
+%%% Local Variables:
13 81
 %%% mode: latex
14 82
 %%% TeX-master: t
15 83
 %%% End: