Mgamma: Gamma Regression Model

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

Mgamma(y, fit, cov)

Arguments

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

Details

Both "log" link and "inverse" link are available in the system. The link information is stored in argument "fit" when the "lm" class is created.

Examples

## Not run: 
#############################################################
################-----IndependentSampling-----################
#############################################################
#########Log Link#########
library(MASS)
data(data.loggamma)
x <- log(data.loggamma$Brain)
y <- data.loggamma$Body
fit <- glm(y ~ x, family = Gamma(link = "log"))
#########Inverse Link#########
library(MASS)
data(data.inversegamma)
attach(data.inversegamma)
x <- log(data.inversegamma$Time)
y <- data.inversegamma$Plasma
fit <- glm(y ~ x, family = Gamma(link = "inverse"))
########################################################
out_i <- independent("Mgamma",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),pty = "s")
plot(out_i$ind[,5],out_i$ind[,6],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)
plot(out_i$ind[,5],out_i$ind[,7],xlab=expression(beta[0]),ylab=expression(nu),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[,7],xlab=expression(beta[1]),ylab=expression(nu),cex=0.5)
points(out_i$MLE[2],out_i$MLE[3],pch=16,col="red",cex=1.5)

##########################################################
################-----BoundarySampling-----################
##########################################################
#########Log Link#########
library(MASS)
data(data.loggamma)
x <- log(data.loggamma$Brain)
y <- data.loggamma$Body
fit <- glm(y ~ x, family = Gamma(link = "log"))
target <- "level"
targetvalue <- c(0.5,0.9)
#########Inverse Link#########
library(MASS)
data(data.inversegamma)
x <- log(data.inversegamma$Time)
y <- data.inversegamma$Plasma
fit <- glm(y ~ x, family = Gamma(link = "inverse"))
target <- "level"
targetvalue <- c(0.5,0.9)
########################################################
out_b <- boundary("Mgamma",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$bound[,5],out_b$bound[,6],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)
plot(out_b$bound[,5],out_b$bound[,7],xlab=expression(beta[0]),ylab=expression(nu),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[,7],xlab=expression(beta[1]),ylab=expression(nu),cex=0.5)
points(out_b$MLE[2],out_b$MLE[3],pch=16,col="red",cex=1.5)

## End(Not run)

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