Nothing
pnpmle =function(n,t=15,C=0,b=200,seed=NULL,conf=0.95,dis=1){
## ==================================================================================
## Purpose: This function calculates the penalized NPMLE by Wang and Lindsay 2005
## input: n--- frequency and frequency data n
## t --- integer cutoff value defining less abundant species, default is 15
## conf--confidence level, a numerical value<1, default .95
## seed--random seed for bootstrap.
## bootrap--number of bootstrap samples.
## dis--0 or 1, 0 for NO display on screen, 1 for yes.
## output: Point estimator and bootstrap confidence interval.
## =================================================================================
if (t!=round(t)||t<0) stop("Error: The cutoff t to define less abundant species must be non-negative integer!")
if (ncol(n)!=2||is.numeric(n[,1])==FALSE||is.numeric(n[,2])==FALSE) {
stop("Error: The frequency of frequencies data n must be a matrix.")
}
if (C!=0 && C!=1){
stop("Error: The argument C must be equal to 0 (w/o confidence interval output) or 1 (with confidence inerval)")
}
if(is.null(seed)==FALSE) set.seed(seed)
if(is.numeric(conf)==FALSE||conf>1||conf<0) stop("Error: confidence level must be a numerical value between 0 and 1, e.g. 0.95")
n=as.matrix(n)
m=max(n[,1])
ntemp=cbind(c(1:m),rep(0,m))
ntemp[n[,1],2]=n[,2]
n=as.matrix(ntemp)
colnames(n)=NULL; rownames(n)=NULL
CI0=numeric(2);lb=0;ub=0;MLE=-1.0
if(t<=nrow(n) && nrow(n)<=50){
ntemp=c(n[1:t,2],rep(0,50-t))
while(MLE<0){
MLE=-1.0
p=rep(0.,10)
pi=p
noZeroP=0
theta0=0.0
results=.Fortran("Nwl",as.double(ntemp),as.integer(t),MLE=as.double(MLE),p=as.double(p),pi=as.double(pi),noZeroP=as.integer(noZeroP),PACKAGE="SPECIES")
MLE=results$MLE
}
MLE0=MLE+sum(n[,2])-sum(n[1:t,2])
p=results$p
pi=results$pi
noZeroP=results$noZeroP
theta0=results$theta0
temp=list(MLE0=MLE0,p=p,pi=pi,noZeroP=noZeroP,dis=dis)
class(temp)="pnpmleClass"
print(temp)
##bootstrap CI
if(C==1){
rep=as.integer(MLE)
results=bootPnpmle(p[1:noZeroP],pi[1:noZeroP],b,t,rep,conf)
lb=results[[1]]+sum(n[,2])-sum(n[1:t,2])
ub=results[[2]]+sum(n[,2])-sum(n[1:t,2])
}
} else if (t>nrow(n)&&nrow(n)<=50){
t=nrow(n)
ntemp=c(n[1:t,2],rep(0,50-t))
while(MLE<0){
MLE=-1.0
p=rep(0.,10)
pi=p
noZeroP=0
theta0=0.0
results=.Fortran("Nwl",as.double(ntemp),as.integer(t),MLE=as.double(MLE),p=as.double(p),pi=as.double(pi),noZeroP=as.integer(noZeroP),PACKAGE="SPECIES")
MLE=results$MLE
}
MLE=results$MLE
MLE0=MLE+sum(n[,2])-sum(n[1:t,2])
p=results$p
pi=results$pi
noZeroP=results$noZeroP
theta0=results$theta0
temp=list(MLE0=MLE0,p=p,pi=pi,noZeroP=noZeroP,dis=dis)
class(temp)="pnpmleClass"
print(temp)
##bootstrap CI
if(C==1){
rep=as.integer(MLE)
results=bootPnpmle(p[1:noZeroP],pi[1:noZeroP],b,t,rep,conf)
lb=results[[1]]
ub=results[[2]]
}
} else if (nrow(n)>50){
if(t>50){
##define less abundant species using t=50
t=50
}
ntemp=c(n[1:t,2])
while(MLE<0){
MLE=-1.0
p=rep(0.,10)
pi=p
noZeroP=0
theta0=0.0
results=.Fortran("Nwl",as.double(ntemp),as.integer(t),MLE=as.double(MLE),p=as.double(p),pi=as.double(pi),noZeroP=as.integer(noZeroP),PACKAGE="SPECIES")
MLE=results$MLE
}
MLE0=MLE+sum(n[,2])-sum(n[1:t,2])
p=results$p
pi=results$pi
noZeroP=results$noZeroP
amodel=results$amodel
amodel0=amodel
temp=list(MLE0=MLE0,p=p,pi=pi,noZeroP=noZeroP,dis=dis)
class(temp)="pnpmleClass"
print(temp)
if(C==1){
rep=as.integer(MLE)
results=bootPnpmle(p[1:noZeroP],pi[1:noZeroP],b,t,rep,conf)
lb=results[[1]]+sum(n[,2])-sum(n[1:t,2])
ub=results[[2]]+sum(n[,2])-sum(n[1:t,2])
}
}
if(C==1){
CI0=matrix(round(c(lb,ub)),1,2)
colnames(CI0)=c("lb","ub")
cat("\n")
return(list(Nhat=round(MLE0),CI=CI0))
} else if(C==0){
return(list(Nhat=round(MLE0)))
}
}
##function to boot pNPMLE
bootPnpmle=function(p,pi,b,t,rep,conf){
pistat=pi*(1-exp(-p))^(-1)/sum(pi*(1-exp(-p))^(-1))
SampleProb=untrunPmix(c(0:t),p,pistat)
SampleProb=SampleProb/(sum(SampleProb))
Nest=numeric(b)
cat("\n","Start bootstrap ", b," times:","\n")
for (i in 1:b){
MLE=-1.0
x=sample(c(0:t),rep,replace=TRUE,prob=SampleProb)
while(MLE<0){
boot=numeric(50)
for(j in 1:t){
boot[j]=sum(x==j)
}
MLE=-1.0
p=rep(0.,10)
pi=p
noZeroP=0
theta0=0.0
results=.Fortran("Nwl",as.double(boot),as.integer(t),MLE=as.double(MLE),p=as.double(p),pi=as.double(pi),noZeroP=as.integer(noZeroP),PACKAGE="SPECIES")
MLE=results$MLE
}
##cat(boot,"\n")
Nest[i]=results$MLE
if(i/20==round(i/20)){
cat("*")
if(i/100==round(i/100)){cat("\n")}
}else{cat(".")}
flush.console()
}
cat("\n")
Nest=sort(Nest)
lb=round(quantile(Nest,(1-conf)/2))
ub=round(quantile(Nest,1-(1-conf)/2))
return(list(lb,ub))
}
#S3 method for on-screen plot
print.pnpmleClass=function(x,...){ #results is list of (MLE0,p,pi,noZeroP,dis)
if(x$dis==1){
cat("Method: Penalized NPMLE method by Wang and Lindsay 2005.", "\n\n")
cat(' MLE= ', x$MLE0,"\n")
cat(' Estimated zero-truncated Poisson mixture components: ', "\n")
cat(' p= ', x$p[1:x$noZeroP],"\n")
cat(' pi= ', x$pi[1:x$noZeroP],"\n\n")
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.