sub.sim: Take Subsamples of Data

In sskm: Stable Sparse K-Means

Description

Function to take subsamples of data to be used to apply stable sparse k-means algorithm.

Usage

sub.sim(data, N, prop = 0.5, B = 100)

Arguments

data	The original data matrix to take subsamples of.
N	The original number of samples in your data.
prop	The proportion of the original number of samples you wish each subsample to have. Must be a number greater than 0 and less than 1. The default is prop=0.5.
B	The number of subsamples you to make. The default is B=100.

Details

The data matrix should have each row be a new observation and each column a different gene.

Value

A list of B m*p matrices containing subsamples of original data where m = prop*N and p = the number of columns in original matrix. Each row reflects a randomly selected observation from original data. Each column is a feature from original dataset.

Author(s)

Abraham Apfel

Examples

#Simulate data matrix
dat1<-matrix(rnorm(200,-1,1),20,10)
dat2<-matrix(rnorm(800,0,1),20,40)
C1<-cbind(dat1,dat2)
C2<-matrix(rnorm(1000,0,1),20,50)
dat3<-matrix(rnorm(200,1,1),20,10)
dat4<-matrix(rnorm(800,0,1),20,40)
C3<-cbind(dat3,dat4)
orig.sample<-rbind(C1,C2,C3)

#Take B=4 subsamples
sub.sample<-sub.sim(data=orig.sample,N=60,prop=0.5,B=4)

#Calculate gap statistic to aid in tuning parameter selection for each subsample
tun_par<-tun_calc(data=sub.sample,k=3,wb=NULL,nperms=5,quiet=FALSE)

#Create list based on highest gap statistic to be used as wb parameter
max.gap<-replicate(n=4,expr=list())
for(i in 1:4){
  max.gap[[i]]<-tun_par[[i]]$bestw
}
pi1<-c(0.2,0.3,0.4)
#Apply Stable Sparse K-means algorithm on subsamples
res<-stablecluster(data=sub.sample,k=3,wb=max.gap,nstart=5,maxiter=6,orig=orig.sample,N=60,pi=pi1)

sskm documentation built on May 29, 2017, 10:43 p.m.