findPeak {WPKDE}

find peaks

Description

using the result of kdeC to find peaks

Usage

findPeak(estimate,filter)

Arguments

estimate

matrix returned by the kdeC function

filter

a num value, filter the result less than argument value filter and set 0 as default

Details

the function findPeak can be executed after kdeC to find peaks

Value

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

Author(s)

Kunyu Ye

Examples

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)