Mlmmc: Linear Mixed Model (Covariance Parameters)

In ppham27/setsim: Software tool for the visualization of confidence regions in statistical models

Description

This function is a private function that returns the basic statistics of a selected model. It is only used in conjunction with boundary or independent sampling method.

Usage

Mlmmc(y, fit, cov)

Arguments

y	response variables.
fit	basic statistics after fitting a linear mixed model by class lme.
cov	a covariance matrix of the parameters. System will use default covariance matrix if it is not specified.

Details

It is recommended to use REML estimation instead of ML estimation for covariance parameters. It can be specified by argument method="REML".

Examples

## Not run: 
#############################################################
################-----IndependentSampling-----################
#############################################################
library(MASS)
library(nlme)
y <- Oxide$Thickness
fit <- lme(Thickness~Wafer,data=Oxide,random=~1|Source,method="REML")
########################################################
out_i <- independent("Mlmmc",y,fit,B=1000)
########################################################
out_i$diag    # out_i$diag is equivalent to out_i$diagnosis
out_i$ind[100:140,]    # out_i$ind is equivalent to out_i$independent.sample
out_i$num    #out_i$num is equivalent to out_i$numWald.interval
out_i$sim    #out_i$sim is equivalent to out_i$simWald.interval
########################################################
par(mfrow=c(2,2))
plot(out_i$ind[,7]~out_i$ind[,6],xlab=expression(beta[1]),ylab=expression(beta[2]),cex=0.5)
points(out_i$MLE[2],out_i$MLE[3],pch=16,col="red",cex=1.5)
plot(out_i$ind[,7]~out_i$ind[,5],xlab=expression(beta[0]),ylab=expression(beta[2]),cex=0.5)
points(out_i$MLE[1],out_i$MLE[3],pch=16,col="red",cex=1.5)
plot(out_i$ind[,6]~out_i$ind[,5],xlab=expression(beta[0]),ylab=expression(beta[1]),cex=0.5)
points(out_i$MLE[1],out_i$MLE[2],pch=16,col="red",cex=1.5)

##########################################################
################-----BoundarySampling-----################
##########################################################
library(MASS)
library(nlme)
y <- Oxide$Thickness
fit <- lme(Thickness~Wafer,data=Oxide,random=~1|Source,method="REML")
target <- "level"
targetvalue <- c(0.5,0.9)
########################################################
out_b <- boundary("Mlmmc",y,fit,target,targetvalue,B=1000)
########################################################
out_b$diag    # out_b$diag is equivalent to out_b$diagnosis
out_b$bound[1:20,]    # out_b$bound is equivalent to out_b$boundary.sample
out_b$num    # out_b$num is equivalent to out_b$numWald.interval
out_b$sim    # out_b$sim is equivalent to out_b$simWald.interval
out_b$convnum   # out_b$convnum is equivalent to out_b$convnumWald
out_b$convsim   # out_b$convsim is equivalent to out_b$convsimWald
########################################################
par(mfrow=c(2,2))
plot(out_b$bound[,7]~out_b$bound[,6],xlab=expression(beta[1]),ylab=expression(beta[2]),cex=0.5)
points(out_b$MLE[2],out_b$MLE[3],pch=16,col="red",cex=1.5)
plot(out_b$bound[,7]~out_b$bound[,5],xlab=expression(beta[0]),ylab=expression(beta[2]),cex=0.5)
points(out_b$MLE[1],out_b$MLE[3],pch=16,col="red",cex=1.5)
plot(out_b$bound[,6]~out_b$bound[,5],xlab=expression(beta[0]),ylab=expression(beta[1]),cex=0.5)
points(out_b$MLE[1],out_b$MLE[2],pch=16,col="red",cex=1.5)

## End(Not run)

ppham27/setsim documentation built on May 25, 2019, 11:25 a.m.