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

Description

Parametric bootstrap for bivariate normally distributed data

Usage

 1 2 3 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

 1 2 3 4 5 6 7 8 9 10 11 12 # 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 May 1, 2019, 9:21 p.m.