@@ -1,8 +1,8 @@

 Package: OncoSimulR

 Type: Package

 Title: Forward Genetic Simulation of Cancer Progresion with Epistasis 

-Version: 2.3.10

-Date: 2016-07-14

+Version: 2.3.11

+Date: 2016-08-10

 Authors@R: c(person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),

 		     email = "rdiaz02@gmail.com"),

 	      person("Mark", "Taylor", role = "ctb", email = "ningkiling@gmail.com"))

@@ -1032,7 +1032,7 @@ evalGenotypeFitAndMut <- function(genotype,

 ## I am here: simplify this

 evalAllGenotypesORMut <- function(fmEffects,

-                                  order = TRUE, max = 256,

+                                  order = FALSE, max = 256,

                              addwt = FALSE,

                              model = "",

                              calledBy_ = "") {

@@ -1129,7 +1129,7 @@ evalAllGenotypesORMut <- function(fmEffects,

     return(df)

 }

-evalAllGenotypes <- function(fitnessEffects, order = TRUE, max = 256,

+evalAllGenotypes <- function(fitnessEffects, order = FALSE, max = 256,

                              addwt = FALSE,

                              model = "") {

     evalAllGenotypesORMut(

@@ -1190,7 +1190,7 @@ generateAllGenotypes <- function(fitnessEffects, order = TRUE, max = 256) {

 }

 evalAllGenotypesFitAndMut <- function(fitnessEffects, mutatorEffects,

-                                   order = TRUE, max = 256,

+                                   order = FALSE, max = 256,

                                    addwt = FALSE,

                                    model = "" ){

 ##                                   minimal = FALSE) {

@@ -59,8 +59,12 @@ rfitness <- function(g, c= 0.5,

         }

         m <- cbind(m, Fitness = fi)

         if(min_accessible_genotypes > 0) {

-            if(count_accessible_g(m, accessible_th) >= min_accessible_genotypes) {

+            num_accessible_genotypes <- count_accessible_g(m, accessible_th)

+            if(num_accessible_genotypes >= min_accessible_genotypes) {

                 done <- TRUE

+                attributes(m) <- c(attributes(m),

+                                   accessible_genotypes = num_accessible_genotypes,

+                                   accessible_th = accessible_th)

             } else {

                 ## Cannot start again with a fitness column

                 m <- m[, -ncol(m), drop = FALSE]

@@ -1,4 +1,8 @@

-Changes in version 2.3.9 (2017-07-14):

+Changes in version 2.3.11 (2017-08-10):

+	- evalAllGenotypes: order = FALSE by default.

+	- Clarified difference plotFitnessEffects and plotFitnessLandscape.

+	

+Changes in version 2.3.10 (2017-07-14):

 	- The windows check failure with mc.cores.

 Changes in version 2.3.9 (2017-07-09):

@@ -25,7 +25,7 @@ model = "")

 evalGenotypeMut(genotype, mutatorEffects, verbose = FALSE, echo = FALSE)

-evalAllGenotypes(fitnessEffects, order = TRUE, max = 256, addwt = FALSE,

+evalAllGenotypes(fitnessEffects, order = FALSE, max = 256, addwt = FALSE,

 model = "")

 evalAllGenotypesMut(mutatorEffects, max = 256, addwt = FALSE)

@@ -36,7 +36,7 @@ evalGenotypeFitAndMut(genotype, fitnessEffects,

                       model = "")

 evalAllGenotypesFitAndMut(fitnessEffects, mutatorEffects,

-                          order = TRUE, max = 256, addwt = FALSE,

+                          order = FALSE, max = 256, addwt = FALSE,

                           model = "")

@@ -64,8 +64,11 @@ evalAllGenotypesFitAndMut(fitnessEffects, mutatorEffects,

   \item{order}{

-    (For \code{evalAllGenotypes}). Does order matter? If it does, then generate not only all possible

-    combinations of the genes, but all possible permutations for each combination.

+

+    (For \code{evalAllGenotypes}). If TRUE, then order matters. If order

+    matters, then generate not only all possible combinations of the genes, but

+    all possible permutations for each combination.

+    

 }

 \item{max}{

   (For \code{evalAllGenotypes}). By default, no output is shown if the

@@ -186,7 +189,7 @@ ofe2 <- allFitnessEffects(orderEffects = c("F > D" = -0.3, "D > F" = 0.4),

                                 "F" = "f1, f2, f3",

                                 "D" = "d1, d2") )

-evalAllGenotypes(ofe2, max = 325)[1:15, ]

+evalAllGenotypes(ofe2, order = TRUE, max = 325)[1:15, ]

 ## Next two are identical

 evalGenotype("d1 > d2 > f3", ofe2, verbose = TRUE)

@@ -6,7 +6,8 @@

 }

 \description{

   Plot the restriction table/graph of restrictions, the epistasis, and

-  the order effects in a fitnessEffects object.

+  the order effects in a fitnessEffects object. This is not a plot of

+  the fitness landscape; for that, see \code{\link{plotFitnessLandscape}}.

 }

 \usage{

 \method{plot}{fitnessEffects}(x, type = "graphNEL", layout = NULL,

@@ -81,7 +82,9 @@ Genes without interactions are not shown.

 }

 \seealso{

-  \code{\link{allFitnessEffects}}

+  \code{\link{allFitnessEffects}},

+  \code{\link{plotFitnessLandscape}}

+  

 }

 \examples{

@@ -10,7 +10,10 @@

 \description{ Show a plot of a fitness landscape. The plot is modeled

   after (actually, mostly a blatant copy of) that of MAGELLAN,

-  \url{http://wwwabi.snv.jussieu.fr/public/Magellan/}. 

+  \url{http://wwwabi.snv.jussieu.fr/public/Magellan/}.

+

+  Note: this is not a plot of the fitnessEffects object; for that, see

+  \code{\link{plot.fitnessEffects}}.

 }

@@ -183,7 +186,7 @@ Ramon Diaz-Uriarte

   of a total of 5000 genotypes you'll have to wait a while for the plot

   to finish.

- 

+  

 }

@@ -191,7 +194,8 @@ Ramon Diaz-Uriarte

   \code{\link{allFitnessEffects}},

   \code{\link{evalAllGenotypes}},

   \code{\link{allFitnessEffects}},

-  \code{\link{rfitness}}

+  \code{\link{rfitness}},

+  \code{\link{plot.fitnessEffects}}

 }

 \examples{

@@ -23,7 +23,7 @@ rfitness(g, c = 0.5, sd = 1, reference = "random", scale = NULL,

   \item{g}{Number of genes.}

   \item{c}{The decrease in fitness of a genotype per each unit increase

-    in Hamming distance from the reference genotype (\code{reference}).}

+    in Hamming distance from the reference genotype (see \code{reference}).}

   \item{sd}{The standard deviation of the random component (a normal

   distribution of mean 0 and standard deviation \code{sd}).}

@@ -34,7 +34,7 @@ rfitness(g, c = 0.5, sd = 1, reference = "random", scale = NULL,

   distance from this reference. If "random" a genotype will be randomly

   chosen as the reference. If "max" the genotype with all positions

   mutated will be chosen as the reference. If you pass a vector (e.g.,

-  \code{fittest = c(1, 0, 1, 0)}) that will be the reference genotype.}

+  \code{reference = c(1, 0, 1, 0)}) that will be the reference genotype.}

 \item{scale}{Either NULL (nothing is done) or a two-element vector. If a

   two-element vector, fitness is re-scaled between \code{scale[1]} (the

@@ -101,7 +101,12 @@ rfitness(g, c = 0.5, sd = 1, reference = "random", scale = NULL,

   column denotes gene mutated/not-mutated. (For ease of use in other

   functions, this matrix has class  "genotype_fitness_matrix".) 

+  If you have specified \code{min_accessible_genotypes > 0}, the return

+  object has added attributes \code{accessible_genotypes} and

+  \code{accessible_th} that show the number of accessible

+  genotypes under the specified  threshold.

 }

+  

 \references{

   Szendro I.~G. et al. (2013). Quantitative analyses of empirical

@@ -4,7 +4,9 @@ CMD check". They can be run by doing "test_dir()".

 They are kept separate because they are long running, many are overkills,

 and in a few it is possible some might fail even when there are no bugs

 (since we are comparing expected outcomes from a distribution, and we

-could end up in unlikely cases).

+could end up in unlikely cases). In fact, you should expect some tests to

+fail if you run them enough times (if they were to never fail, this could

+mean that the tests are not actually sensitive enough).

 Note that we will rarely want to use something like try_again (available

 from the github version of testthat) or repeated tries (e.g.,

@@ -6,14 +6,14 @@

 cat(paste("\n Starting sample-prob-long", date(), "\n"))

-p.value.threshold <- 0.01

+p.value.threshold <- 1e-7

 test_that("Increasing cPDetect decreases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 100

+    n <- 800

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -35,7 +35,8 @@ test_that("Increasing cPDetect decreases time, Exp" , {

                            onlyCancer = FALSE,

                            detectionDrivers = 99, mc.cores = 2)

         ta <- unlist(lapply(sa, function(x) x$FinalTime))

-        tb <- unlist(lapply(sb, function(x) x$FinalTime))         

+        tb <- unlist(lapply(sb, function(x) x$FinalTime))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -48,7 +49,7 @@ test_that("Increasing p2 decreases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 100

+    n <- 1200

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -70,7 +71,8 @@ test_that("Increasing p2 decreases time, Exp" , {

                            onlyCancer = FALSE,

                            detectionDrivers = 99, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -79,12 +81,11 @@ test_that("Increasing p2 decreases time, Exp" , {

 })

-

 test_that("Increasing n2 increases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 100

+    n <- 1200

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -92,7 +93,7 @@ test_that("Increasing n2 increases time, Exp" , {

                            model = "Exp",

                            initSize = 5000,

                            keepEvery = -9,

-                           detectionProb = c(p2 = .6, n2 = 7000, cPDetect = NULL),

+                           detectionProb = c(p2 = .6, n2 = 8000, cPDetect = NULL),

                            finalTime = NA,

                            onlyCancer = FALSE,

                            detectionDrivers = 99, mc.cores = 2)

@@ -101,12 +102,13 @@ test_that("Increasing n2 increases time, Exp" , {

                            model = "Exp",

                            initSize = 5000,

                            keepEvery = -9,

-                           detectionProb = c(p2 = .6, n2 = 5502, cPDetect = NULL),

+                           detectionProb = c(p2 = .6, n2 = 6002, cPDetect = NULL),

                            finalTime = NA,

                            onlyCancer = FALSE,

                            detectionDrivers = 99, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -121,7 +123,7 @@ test_that("Increasing checkSizePEvery increases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 100

+    n <- 1200

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -143,7 +145,8 @@ test_that("Increasing checkSizePEvery increases time, Exp" , {

                            onlyCancer = FALSE,

                            detectionDrivers = 99, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -14,7 +14,7 @@ test_that("Root name in module table or not", {

                                            "B" = 0.5),

                              geneToModule = c("A" = "a1, a2",

                                               "B" = "b1")))

-    expect_identical(evalAllGenotypes(fee), evalAllGenotypes(fee2))

+    expect_identical(evalAllGenotypes(fee, order = TRUE), evalAllGenotypes(fee2, order = TRUE))

     expect_error(allFitnessEffects(epistasis = c("A" = 0.3,

                                                  "B" = 0.5),

                                    geneToModule = c(

@@ -161,9 +161,11 @@ test_that("Bauer example: identical values fitness classes, diff. order", {

 test_that("Order effects, entry of labels and separation", {

     o1 <- evalAllGenotypes(allFitnessEffects(

-        orderEffects = c("d>f" = 0.4, "f > d" = -0.3) ))

+        orderEffects = c("d>f" = 0.4, "f > d" = -0.3) ),

+        order = TRUE)

     o2 <- evalAllGenotypes(allFitnessEffects(

-        orderEffects = c("f > d" = -0.3, "d > f" = 0.4) ))

+        orderEffects = c("f > d" = -0.3, "d > f" = 0.4) ),

+        order = TRUE)

     o1s <- o1[order(o1$Genotype),   ]

     o2s <- o2[order(o2$Genotype),   ]

     expect_equal(o1s, o2s)

@@ -175,7 +177,7 @@ test_that("Order effects, modules 1", {

                                   c("Root" = "Root",

                                     "F" = "f1, f2",

                                     "D" = "d1, d2") )

-    ag <- evalAllGenotypes(ofe1)

+    ag <- evalAllGenotypes(ofe1, order = TRUE)

     expect_true(all.equal(ag[c(17, 39, 19, 29), "Fitness"], c(1.4, 0.7, 1.4, 0.7)))

     expect_true(all.equal(ag[c(43, 44), "Fitness"], c(1.4, 1.4)))

     expect_true(all(ag[41:52, "Fitness"] == 1.4))

@@ -188,7 +190,7 @@ test_that("Order effects, modules 2", {

                                   c("Root" = "Root",

                                     "F" = "f1, f2, f3",

                                     "D" = "d1, d2") )

-    ag2 <- evalAllGenotypes(ofe2, max = 326)

+    ag2 <- evalAllGenotypes(ofe2, max = 326, order = TRUE)

     oe <- c(grep("^f.*d.*", ag2[, 1]), grep("^d.*f.*", ag2[, 1]))

     expect_true(all(ag2[grep("^d.*f.*", ag2[, 1]), "Fitness"] == 1.4))

     expect_true(all(ag2[grep("^f.*d.*", ag2[, 1]), "Fitness"] == 0.7))

@@ -200,10 +202,10 @@ test_that("Order effects, modules 2", {

 test_that("Order effects, twisted module names", {

     o1 <- evalAllGenotypes(allFitnessEffects(

       orderEffects = c("F > D" = -0.3, "D > F" = 0.4),  

-        geneToModule = c("Root" = "Root", "F" = "d", "D" = "f")))

+        geneToModule = c("Root" = "Root", "F" = "d", "D" = "f")), order = TRUE)

     o2 <- evalAllGenotypes(allFitnessEffects(

       orderEffects = c("D>F" = 0.4, "F >D" = -0.3),  

-        geneToModule = c("Root" = "Root", "F" = "d", "D" = "f")))

+        geneToModule = c("Root" = "Root", "F" = "d", "D" = "f")), order = TRUE)

     o1s <- o1[order(o1$Genotype),   ]

     o2s <- o2[order(o2$Genotype),   ]

     expect_equal(o1s, o2s)

@@ -225,7 +227,7 @@ test_that("Order effects, three-gene-orders and modules 1", {

                                     "M" = "m",

                                     "F" = "f",

                                     "D" = "d") )

-    ag <- evalAllGenotypes(o3)

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

     expect_true(all.equal(ag[, "Fitness"],

                         c(rep(1, 4),

                           1.1,

@@ -318,7 +320,7 @@ test_that("No interaction genes and order effects, 1", {

     foi1 <- allFitnessEffects(

         orderEffects = c("D>B" = -0.3, "B > D" = 0.3),

         noIntGenes = c("A" = 0.05, "C" = -.2, "E" = .1))

-    agoi1 <- evalAllGenotypes(foi1,  max = 325)

+    agoi1 <- evalAllGenotypes(foi1,  max = 325, order = TRUE)

     rn <- 1:nrow(agoi1)

     names(rn) <- agoi1[, 1]

     expect_true(all.equal(agoi1[rn[LETTERS[1:5]], "Fitness"], c(1.05, 1, 0.8, 1, 1.1)))

@@ -349,7 +351,7 @@ test_that("No interaction genes and order effects, 2", {

     foi1 <- allFitnessEffects(

         orderEffects = c("D>B" = -0.3, "B > D" = 0.3),

         noIntGenes = c("M" = 0.05, "A" = -.2, "J" = .1))

-    agoi1 <- evalAllGenotypes(foi1,  max = 325)

+    agoi1 <- evalAllGenotypes(foi1,  max = 325, order = TRUE)

     rn <- 1:nrow(agoi1)

     names(rn) <- agoi1[, 1]

     expect_true(all.equal(agoi1[rn[c("B", "D", "M", "A", "J")], "Fitness"],

@@ -883,7 +885,7 @@ test_that("a silly epistasis example", {

     expect_silent(sv <- allFitnessEffects(

                      orderEffects = c("A > B" = 0.1),

                      epistasis = c("A" = 1)))

-    expect_output(print(evalAllGenotypes(sv)), "Genotype")

+    expect_output(print(evalAllGenotypes(sv, order = TRUE)), "Genotype")

 })

 test_that("can run without keeping input", {

@@ -255,7 +255,10 @@ test_that("mut and fitness both needed when needed", {

     expect_error(evalGenotypeFitAndMut("a, b", mutatorEffects = fm),

                  'argument "fitnessEffects" is missing',

                  fixed = TRUE)

-    expect_error(evalAllGenotypesFitAndMut(fe),

+    expect_error(evalAllGenotypesFitAndMut(fe, order = TRUE),

+                 'argument "mutatorEffects" is missing',

+                 fixed = TRUE)

+    expect_error(evalAllGenotypesFitAndMut(fe, order = FALSE),

                  'argument "mutatorEffects" is missing',

                  fixed = TRUE)

     expect_error(evalAllGenotypesFitAndMut(fm),

@@ -332,6 +335,9 @@ test_that("We cannot pass mutator/fitness objects to the wrong functions", {

     expect_error(evalAllGenotypesMut(fe2),

                  "You are trying to get the mutator effects of a fitness specification.",

                  fixed = TRUE)

+    expect_error(evalAllGenotypesMut(fe2),

+                 "You are trying to get the mutator effects of a fitness specification.",

+                 fixed = TRUE)

     expect_error(evalAllGenotypes(fm2),

                  "You are trying to get the fitness of a mutator specification.",

                  fixed = TRUE)

@@ -368,7 +374,7 @@ test_that("evaluating genotype and mutator", {

                     c(1.5 * 1.1, 5))

     expect_equivalent(dplyr::filter(ou, Genotype == "a, b, c, e")[, c(2, 3)],

                     c(1.3 * 1.5 * 1.1, 10 * 5))

-    oo <- evalAllGenotypesFitAndMut(fe, fm)

+    oo <- evalAllGenotypesFitAndMut(fe, fm, order = TRUE)

     expect_equivalent(dplyr::filter(oo, Genotype == "a")[, c(2, 3)],

                     c(1, 10))

     expect_equivalent(dplyr::filter(oo, Genotype == "b")[, c(2, 3)],

@@ -1,12 +1,12 @@

 cat(paste("\n Starting sample-prob", date(), "\n"))

-p.value.threshold <- 0.01

+p.value.threshold <- 1e-4

 test_that("Increasing cPDetect decreases time" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 15

+    n <- 35

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -28,7 +28,8 @@ test_that("Increasing cPDetect decreases time" , {

                            onlyCancer = TRUE,

                            detectionDrivers = NA, mc.cores = 2)

         ta <- unlist(lapply(sa, function(x) x$FinalTime))

-        tb <- unlist(lapply(sb, function(x) x$FinalTime))         

+        tb <- unlist(lapply(sb, function(x) x$FinalTime))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -41,7 +42,7 @@ test_that("Increasing p2 decreases time" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 25

+    n <- 35

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -78,7 +79,7 @@ test_that("Increasing n2 increases time" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -116,7 +117,7 @@ test_that("Increasing checkSizePEvery increases time" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -138,7 +139,8 @@ test_that("Increasing checkSizePEvery increases time" , {

                            onlyCancer = TRUE,

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -155,7 +157,7 @@ test_that("Increasing cPDetect decreases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 20

+    n <- 30

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -177,7 +179,8 @@ test_that("Increasing cPDetect decreases time, Exp" , {

                            onlyCancer = TRUE,

                            detectionDrivers = NA, mc.cores = 2)

         ta <- unlist(lapply(sa, function(x) x$FinalTime))

-        tb <- unlist(lapply(sb, function(x) x$FinalTime))         

+        tb <- unlist(lapply(sb, function(x) x$FinalTime))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -190,7 +193,7 @@ test_that("Increasing p2 decreases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -213,6 +216,7 @@ test_that("Increasing p2 decreases time, Exp" , {

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

         (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -226,7 +230,7 @@ test_that("Increasing n2 increases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -248,7 +252,8 @@ test_that("Increasing n2 increases time, Exp" , {

                            onlyCancer = TRUE,

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -263,7 +268,7 @@ test_that("Increasing checkSizePEvery increases time, Exp" , {

     gi <- rep(0.1, 10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -285,7 +290,8 @@ test_that("Increasing checkSizePEvery increases time, Exp" , {

                            onlyCancer = TRUE,

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -306,7 +312,7 @@ test_that("Increasing cPDetect decreases time" , {

     gi <- rep(0.0,  10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 15

+    n <- 25

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -328,7 +334,8 @@ test_that("Increasing cPDetect decreases time" , {

                            onlyCancer = FALSE,

                            detectionDrivers = NA, mc.cores = 2)

         ta <- unlist(lapply(sa, function(x) x$FinalTime))

-        tb <- unlist(lapply(sb, function(x) x$FinalTime))         

+        tb <- unlist(lapply(sb, function(x) x$FinalTime))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -341,7 +348,7 @@ test_that("Increasing p2 decreases time" , {

     gi <- rep(0.0,  10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 20

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -363,7 +370,8 @@ test_that("Increasing p2 decreases time" , {

                            onlyCancer = FALSE,

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -377,7 +385,7 @@ test_that("Increasing n2 increases time" , {

     gi <- rep(0.0,  10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -415,7 +423,7 @@ test_that("Increasing checkSizePEvery increases time" , {

     gi <- rep(0.0,  10)

     names(gi) <- letters[1:10]

     oi <- allFitnessEffects(noIntGenes = gi)

-    n <- 30

+    n <- 40

     max.tries <- 4  

     for(tries in 1:max.tries) {

         sa <- oncoSimulPop(n,

@@ -437,7 +445,8 @@ test_that("Increasing checkSizePEvery increases time" , {

                            onlyCancer = FALSE,

                            detectionDrivers = NA, mc.cores = 2)

         (ta <- unlist(lapply(sa, function(x) x$FinalTime)))

-        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))         

+        (tb <- unlist(lapply(sb, function(x) x$FinalTime)))

+        print(suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value))

         T1 <- suppressWarnings(wilcox.test(ta, tb, alternative = "greater")$p.value < p.value.threshold)

         if(T1) break;

     }

@@ -316,7 +316,7 @@ sv2 <- allFitnessEffects(epistasis = c("-A : B" = sb,

                              "B" = "b",

                              "C" = "c"),

                          drvNames = c("a1", "a2", "b", "c"))

-evalAllGenotypes(sv2, order = FALSE, addwt = TRUE)

+evalAllGenotypes(sv2, addwt = TRUE)

 ## 2. Simulate the data. Here we use the "McFL" model and set explicitly

 ##    parameters for mutation rate, initial size and size of the population

@@ -413,7 +413,7 @@ pancr <- allFitnessEffects(

                sh = -0.9,

                typeDep = "MN"),

     drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53", "MLL3", "TGFBR2", "PXDN"))

-## How does it look like?

+## How does the fitnessEffects object look like?

 plot(pancr)

 @ 

 <<fig.width=6.5, fig.height=10>>=

@@ -606,11 +606,16 @@ fem4 <- allFitnessEffects(genotFitness = m4)

 (The message is just telling you what the program guess you wanted.)

-That's it. You can plot that object

+That's it. You can plot that fitnessEffects object

 <<out.width="6cm", out.height = "6cm">>=

 plot(fem4)

 @ 

-or see what OncoSimulR thinks the fitnesses are, with the

+

+In this case, that plot is not very interesting (compare with the

+\texttt{plot(pancr)} we saw in \qref{quickexample} or the plots in

+\qref{posetslong}).  

+

+You can also check what OncoSimulR thinks the fitnesses are, with the

 \Rfunction{evalAllGenotypes} function that we will use repeatedly below

 (of course, here we should see the same fitnesses we entered):

@@ -619,6 +624,12 @@ evalAllGenotypes(fem4)

 @ 

+And you can plot the fitness landscape:

+

+<<>>=

+plotFitnessLandscape(evalAllGenotypes(fem4))

+@ 

+

 To specify the mapping you can also use a matrix (or data frame) with

 $g + 1$ columns; each of the first $g$ columns contains a 1 or a 0

@@ -629,8 +640,9 @@ genotypes (we will assume that the missing genotypes have fitness 1):

 <<out.width="6cm", out.height = "6cm">>=

 m6 <- cbind(c(1, 1), c(1, 0), c(2, 3))

 fem6 <- allFitnessEffects(genotFitness = m6)

-evalAllGenotypes(fem6, addwt = TRUE, order = FALSE)

+evalAllGenotypes(fem6, addwt = TRUE)

 plot(fem6)

+plotFitnessLandscape(evalAllGenotypes(fem6))

 @ 

@@ -1532,7 +1544,7 @@ o3 <- allFitnessEffects(orderEffects = c(

                               "D" = "d") )

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

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

 @ 

 %% <<>>=

@@ -1576,7 +1588,7 @@ ofe1 <- allFitnessEffects(orderEffects = c("F > D" = -0.3, "D > F" = 0.4),

                               c("F" = "f1, f2",

                                 "D" = "d1, d2") )

-ag <- evalAllGenotypes(ofe1)

+ag <- evalAllGenotypes(ofe1, order = TRUE)

 @ 

@@ -1644,7 +1656,7 @@ ofe2 <- allFitnessEffects(orderEffects = c("F > D" = -0.3, "D > F" = 0.4),

                           geneToModule =

                               c("F" = "f1, f2, f3",

                                 "D" = "d1, d2") )

-ag2 <- evalAllGenotypes(ofe2, max = 325)

+ag2 <- evalAllGenotypes(ofe2, max = 325, order = TRUE)

 @ 

@@ -1689,7 +1701,7 @@ We can get the fitness of all genotypes (we set $max = 325$ because that

 is the number of possible genotypes):

 <<>>=

-agoi1 <- evalAllGenotypes(foi1,  max = 325)

+agoi1 <- evalAllGenotypes(foi1,  max = 325, order = TRUE)

 head(agoi1)

 @ 

@@ -4009,7 +4021,7 @@ very different for genotypes with the same number of mutations, and does

 not increase in a simple way with drivers:

 <<>>=

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

+evalAllGenotypes(examplesFitnessEffects[["o3"]], addwt = TRUE, order = TRUE)

 @ 

 A few figures could help:

@@ -1,15 +1,15 @@

 \usepackage[%

-		shash={66cf4f1},

-		lhash={66cf4f192d83ab3aa9ff159405ab08c1ffd05be3},

-		authname={Ramon Diaz-Uriarte},

-		authemail={rdiaz02@users.noreply.github.com},

-		authsdate={2016-07-11},

-		authidate={2016-07-11 08:27:58 +0200},

-		authudate={1468218478},

-		commname={Ramon Diaz-Uriarte},

-		commemail={rdiaz02@users.noreply.github.com},

-		commsdate={2016-07-11},

-		commidate={2016-07-11 08:27:58 +0200},

-		commudate={1468218478},

+		shash={55f3e48},

+		lhash={55f3e487f322e90ab69c172ab452f5b5401fa8a2},

+		authname={ramon diaz-uriarte (at Phelsuma)},

+		authemail={rdiaz02@gmail.com},

+		authsdate={2016-07-30},

+		authidate={2016-07-30 11:53:43 +0200},

+		authudate={1469872423},

+		commname={ramon diaz-uriarte (at Phelsuma)},

+		commemail={rdiaz02@gmail.com},

+		commsdate={2016-07-30},

+		commidate={2016-07-30 11:53:43 +0200},

+		commudate={1469872423},

 		refnames={ (HEAD -> master, origin/master, origin/HEAD)}

 	]{gitsetinfo}

\ No newline at end of file