Bootstrap

Description

The standard error analysis and the bootstrap analysis of -2log(Lambda).

Usage

bootstrap(x,n,p,g,distr,ncov,popPAR,B=99,replace=TRUE,
itmax=1000,epsilon=1e-5)
bootstrap.noc(x,n,p,g1,g2,distr,ncov,B=99,replace=TRUE,
itmax=1000,epsilon=1e-5)

Arguments

Details

The distribution type, distr, is one of the following values: "mvn" for a multivariate normal, "mvt" for a multivariate t-distribution, "msn" for a multivariate skew normal distribution and "mst" for a multivariate skew t-distribution.

The covariance matrix type, represented by the ncov parameter, may be any one of the following: ncov=1 for a common variance, ncov=2 for a common diagonal variance, ncov=3 for a general variance, ncov =4 for a diagonal variance, ncov=5 for sigma(h)*I(p)(diagonal covariance with same identical diagonal element values).

When replace is FALSE, parametric bootstrap is used; otherwise replacement method is used.

Value

bootstrap gives standard errors. bootstrap.noc returns a list with components ret, a B by (g2-g1) matrix of -2log(Lambda), vlk, the loglikehood for each g in the range of g1 to g2, and pvalue, the p-values of g vs g+1. The results of fitting mixture models are stored in curent working directory, which can be used via command in R: obj <- dget("ReturnOf_g_???.ret").

References

McLachlan G.J. and Krishnan T. (2008). The EM Algorithm and Extensions (2nd). New Jersay: Wiley.

McLachlan G.J. and Peel D. (2000). Finite Mixture Models. New York: Wiley.

See Also

EmSkew,rdemmix

Examples

n1=300;n2=300;n3=400;
nn <-c(n1,n2,n3)
n <- sum(nn)
p <- 2
g <- 3


sigma<-array(0,c(p,p,g))
for(h in 1:3) sigma[,,h]<-diag(p)

mu  <- cbind(c(4,-4),c(3.5,4),c( 0, 0))

# for other distributions, 
#delta <- cbind(c(3,3),c(1,5),c(-3,1))
#dof    <- c(3,5,5)

distr="mvn"
ncov=3

#first we generate a data set
set.seed(111) #random seed is set 
dat <- rdemmix(nn,p,g,distr,mu,sigma,dof=NULL,delta=NULL)

#start from initial partition
clust<- rep(1:g,nn)
obj <- EmSkewfit1(dat,g,clust,distr,ncov,itmax=1000,epsilon=1e-5)

# do bootstrap (stadard error analysis)

## Not run: 
std <- bootstrap(dat,n,p,g,distr,ncov,obj,B=19,
replace=TRUE,itmax=1000,epsilon=1e-5)
print(std)

# do booststrap analysis of -2log(Lambda).

# alternatively data can be input as follow,
# dat <- read.table("mydata.txt",header=TRUE)
# p   <- ncol(dat)
# n   <- nrow(dat)

lad <- bootstrap.noc(dat,n,p,2,4,distr,ncov,B=19,
replace=FALSE,itmax=1000,epsilon=1e-5)
print(lad)

# return of g=2
obj2 <- dget("ReturnOf_g_2.ret")

# return of g=3
obj3 <- dget("ReturnOf_g_3.ret")

# return of g=4
obj4 <- dget("ReturnOf_g_4.ret")

#The posterior probability matrix for (g=3) is obtained by

tau <- obj3$tau



## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(EMMIXskew)
Loading required package: lattice
Loading required package: mvtnorm
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EMMIXskew/bootstrap.Rd_%03d_medium.png", width=480, height=480)
> ### Name: bootstrap
> ### Title: Bootstrap
> ### Aliases: bootstrap bootstrap.noc
> ### Keywords: cluster datasets
> 
> ### ** Examples
> 
> 
> n1=300;n2=300;n3=400;
> nn <-c(n1,n2,n3)
> n <- sum(nn)
> p <- 2
> g <- 3
> 
> 
> sigma<-array(0,c(p,p,g))
> for(h in 1:3) sigma[,,h]<-diag(p)
> 
> mu  <- cbind(c(4,-4),c(3.5,4),c( 0, 0))
> 
> # for other distributions, 
> #delta <- cbind(c(3,3),c(1,5),c(-3,1))
> #dof    <- c(3,5,5)
> 
> distr="mvn"
> ncov=3
> 
> #first we generate a data set
> set.seed(111) #random seed is set 
> dat <- rdemmix(nn,p,g,distr,mu,sigma,dof=NULL,delta=NULL)
> 
> #start from initial partition
> clust<- rep(1:g,nn)
> obj <- EmSkewfit1(dat,g,clust,distr,ncov,itmax=1000,epsilon=1e-5)
> 
> # do bootstrap (stadard error analysis)
> 
> ## Not run: 
> ##D std <- bootstrap(dat,n,p,g,distr,ncov,obj,B=19,
> ##D replace=TRUE,itmax=1000,epsilon=1e-5)
> ##D print(std)
> ##D 
> ##D # do booststrap analysis of -2log(Lambda).
> ##D 
> ##D # alternatively data can be input as follow,
> ##D # dat <- read.table("mydata.txt",header=TRUE)
> ##D # p   <- ncol(dat)
> ##D # n   <- nrow(dat)
> ##D 
> ##D lad <- bootstrap.noc(dat,n,p,2,4,distr,ncov,B=19,
> ##D replace=FALSE,itmax=1000,epsilon=1e-5)
> ##D print(lad)
> ##D 
> ##D # return of g=2
> ##D obj2 <- dget("ReturnOf_g_2.ret")
> ##D 
> ##D # return of g=3
> ##D obj3 <- dget("ReturnOf_g_3.ret")
> ##D 
> ##D # return of g=4
> ##D obj4 <- dget("ReturnOf_g_4.ret")
> ##D 
> ##D #The posterior probability matrix for (g=3) is obtained by
> ##D 
> ##D tau <- obj3$tau
> ##D 
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>