HFB: High Frequency via Bootstrap

In saeidamiri1/mlgwsa: Genome Wide-Association Study (mlgwsa)

Usage

HFB(x,alpha)

Arguments

x	matrix including the bootstrap of influence variables
alpha	minimum relative frquency tof influence variables.

Details

To have more reliable informative variables, we implement the HFB that run the proposed method on the resample from data.

References

Amiri, S., Ivo, D. (2017). Information Theoretic Approach for Genome-Wide Association Study of Parkinson's Disease.

Examples

### obtaine the size of dataset.
#nD<-dim(SNPgroupD)[1]
#nC<-dim(SNPgroupC)[1]

### number of bootstrap
#B<-500
#vsfeentR<-list()
### the following loop achieves the bootsrap and obtain the informative variables
#for(b in 1:B){

#  sxx1<-sort(unique(sample(nD,nD,replace=TRUE)))
#  sxx2<-sort(unique(sample(nC,nC,replace=TRUE)))

#  SNPgroupDR<-SNPgroupD[ sxx1,]
#  SNPgroupCR<-SNPgroupC[ sxx2,]
#  DfeR<-IBC(SNPgroupDR)
#  CfeR<-IBC(SNPgroupCR)

#  vsfeentR[[b]]<-which(CfeR/2>DfeR)
#}

#Alpha<-0.8
#hfb<-HFB(vsfeentR,Alpha)
#length(hfb)

#### the following codes give the dengrogram on the informative variables
#### obtained via HFB
#dt1<-Distwhole(SNPgroupD,SNPgroupC,vsfeent1)
### Calculate hierarchial dendrogram
#hcdt1<-hclust(as.dist(dt1), method = "average", members = NULL)
### plot the dendrogram
#plot(hcdt1,label=labfem)

saeidamiri1/mlgwsa documentation built on May 29, 2019, 9:10 a.m.