Description Usage Arguments Details Value Author(s) Examples
using the result of kdeC
to find peaks
1 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.