simData | R Documentation |
Simulated data for running examples
Three objects are loaded, two data frame(s) of simulated data each with 300 samples/columns and 153 variables/rows, and a vector of length 300 with the true cluster assignments.
simData
is simulated normal data of 300 observations with 51
relevant variables and the rest of the variables being noise, with
observations being in one of 3 groups. simCount
is simulated count
data of the same dimensions. trueCluster
gives the true cluster
identifications of the samples. The true clusters are each of size 100 and
are in order in the columns of the data.frames.
Elizabeth Purdom epurdom@stat.berkeley.edu
#code used to create data:
## Not run:
nvar<-51 #multiple of 3
n<-100
x<-cbind(matrix(rnorm(n*nvar,mean=5),nrow=nvar),
matrix(rnorm(n*nvar,mean=-5),nrow=nvar),
matrix(rnorm(n*nvar,mean=0),nrow=nvar))
#make some of them flipped effects (better for testing if both sig under/over
#expressed variables)
geneGroup<-sample(rep(1:3,each=floor(nvar/3)))
gpIndex<-list(1:n,(n+1):(n*2),(2*n+1):(n*3))
x[geneGroup==1,]<-x[geneGroup==1,unlist(gpIndex[c(3,1,2)])]
x[geneGroup==2,]<-x[geneGroup==2,unlist(gpIndex[c(2,3,1)])]
#add in differences in variable means
smp<-sample(1:nrow(x),10)
x[smp,]<-x[smp,]+10
#make different signal y
y<-cbind(matrix(rnorm(n*nvar,mean=1),nrow=nvar),
matrix(rnorm(n*nvar,mean=-1),nrow=nvar),
matrix(rnorm(n*nvar,mean=0),nrow=nvar))
y<-y[,sample(1:ncol(y))]+ matrix(rnorm(3*n*nvar,sd=3),nrow=nvar)
#add together the two signals
simData<-x+y
#add pure noise variables
simData<-rbind(simData,matrix(rnorm(3*n*nvar,mean=10),nrow=nvar),
matrix(rnorm(3*n*nvar,mean=5),nrow=nvar))
#make count data
countMean<-exp(simData/2)
simCount<-matrix(rpois(n=length(as.vector(countMean)), lambda
=as.vector(countMean)+.1),nrow=nrow(countMean),ncol=ncol(countMean))
#labels for the truth
trueCluster<-rep(c(1:3),each=n)
save(list=c("simCount","simData","trueCluster"),file="data/simData.rda")
## End(Not run)
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