boot: Parametric bootstrap

In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

Parametric bootstrap

Description

Parametric bootstrap for bivariate normally distributed data

Usage

CAMANboot(obs1, obs2, var1, var2, lambda11, lambda12, 
     prob1, lambda21, lambda22, prob2, rep, 
     data,numiter=10000,acc=1.e-7)

Arguments

obs1	the first column of the observations

obs2	the second column of the observations

data	a data frame

var1	Variance of the first column of the observations(except meta-analysis)

var2	Variance of the second column of the observations (except meta-analysis)

lambda11

first means of the first column of the observations

lambda12

first means of the second column of the observations

prob1

first mixing weight

lambda21

second means of the first column of the observations

lambda22

second means of the second column of the observations

prob2

second mixing weight

numiter

parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000.

acc	convergence criterion. Default is 1.e-7

rep	number of repetitions

Examples

# Parametric bootstrap for bivariate normally distributed data
data(CT)
library(mvtnorm)
hom1<-c(3.142442)
hom2<-c(-1.842393)
p1<-c(1)
start1<-c(2.961984,3.226141)
start2<-c(-2.578836, -1.500823)
pvem<-c(0.317,0.683)
CAMANboot(obs1=logitTPR, obs2=logitTNR, var1=varlogitTPR, var2=varlogitTNR,
          lambda11=hom1, lambda12=hom2, prob1=p1, 
          lambda21=start1, lambda22=start2, prob2=pvem,rep=3,data=CT)

CAMAN documentation built on Sept. 22, 2023, 5:12 p.m.