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# Randomly allocating observations into groups, for, e.g. cross-validation | |
kk <- 10 # Number of partitions, as in "kk-fold cross-validation." | |
# Here is a data.frame full of good data: | |
nn <- 1003 | |
myData <- data.frame(matrix(rnorm(nn * 3), ncol = 3)) | |
colnames(myData) <- LETTERS[1:3] | |
# This does not work: | |
whichK <- sample(LETTERS[1:kk], nrow(myData), replace = T) |
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# Simple ggplot2 heatmap | |
# with colorBrewer "spectral" palette | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "reshape2", "RColorBrewer") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Generate a random matrix | |
# This can be any type of numeric matrix, |
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# A simple approach to visually-weighted regression plots | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "reshape2", "MASS") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Generate some data: | |
nn <- 1000 | |
myData <- data.frame(X = rnorm(nn), |
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# Note: using the devel versions of both packages! | |
library(DESeq) # version 1.9.11 | |
library(edgeR) # version 2.99.8 | |
library(VennDiagram) | |
# Read in data ------------------------------------------------------------ | |
## Use pasilla data | |
datafile = system.file( "extdata/pasilla_gene_counts.tsv", package="pasilla" ) | |
datafile |
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