findPeak {WPKDE} | R Documentation |
using the result of kdeC
to find peaks
findPeak(estimate,filter)
estimate |
matrix returned by the |
filter |
a num value, filter the result less than argument value |
the function findPeak
can be executed after kdeC
to find peaks
The returned value is a matrix corresponding to input argument estimate
, the value in the returned matrix larger than 0 means it is a peak
Kunyu Ye
data.gen<-function(n.peaks=100, N=1e5, max.var=0.001, max.corr=0.5) { library(mvtnorm) dat<-matrix(0, nrow=N, ncol=2) all.m<-c(NA,NA) for(i in 1:n.peaks) { this.m<-runif(2) this.var<-runif(2, min=0.1*max.var, max=max.var) this.cov<-runif(1, min=-1*max.corr, max=max.corr) * sqrt(this.var[1])* sqrt(this.var[2]) this.s<-matrix(c(this.var[1], this.cov, this.cov, this.var[2]),ncol=2) dat[((i-1)*N/n.peaks+1):(i*N/n.peaks),]<-rmvnorm(N/n.peaks, mean=this.m, sigma=this.s) all.m<-rbind(all.m, this.m) } all.m[,1]<-(all.m[,1]-min(dat[,1]))/diff(range(dat[,1])) all.m[,2]<-(all.m[,2]-min(dat[,2]))/diff(range(dat[,2])) dat[,1]<-(dat[,1]-min(dat[,1]))/diff(range(dat[,1])) dat[,2]<-(dat[,2]-min(dat[,2]))/diff(range(dat[,2])) all.m<-all.m[-1,] return(list(dat=dat,m=all.m)) } r<-data.gen(n.peaks=100, N=1e5, max.var=0.001, max.corr=0.5) k1<-kdeC(r$dat, H=c(0.005,0.005), gridsize = c(501,501), cutNum=c(1,1)) matPeaks<-findPeak(estimate=k1$estimate,filter=0)