anova.glmb: Analysis of Deviance for Bayesian Generalized Linear Model...

In knygren/glmbayes: Bayesian Generalized Linear Models (iid Samples)

Description

Compute an analysis of deviance table for one (current implementation) or more (future) Bayesian generalized linear model fits,

Usage

## S3 method for class 'glmb'
anova(object, ...)

Arguments

object	an object of class glmb, typically the result of a call to glmb
...	Other arguments passed to or from other methods.

Details

Specifying a single object (currently only implementation) gives a sequential analysis of deviance table for that fit. That is the reductions in residual deviance as each term of the formula is addded in turn are given as the rows of a table, plus the residual deviances themselves.

Value

An object of class "anova" inheriting from class "data.frame".

Examples

data(menarche2)
## ----Analysis Setup-----------------------------------------------------------
## Number of variables in model
Age=menarche2$Age
nvars=2
## Reference Ages for setting of priors and Age_Difference
ref_age1=13  # user can modify this
ref_age2=15  ## user can modify this
## Define variables used later in analysis
Age2=menarche2$Age-ref_age1
Age_Diff=ref_age2-ref_age1
mu1=as.matrix(c(0,1.098612),ncol=1)
V1<-1*diag(nvars)
V1[1,1]=0.18687882
V1[2,2]=0.10576217
V1[1,2]=-0.03389182
V1[2,1]=-0.03389182
Menarche_Model_Data=data.frame(Age=menarche2$Age,Total=menarche2$Total,
                               Menarche=menarche2$Menarche,Age2)
glmb.out1<-glmb(n=1000,cbind(Menarche, Total-Menarche) ~Age2,family=binomial(logit),
                pfamily=dNormal(mu=mu1,Sigma=V1),data=Menarche_Model_Data)

# Prediction from original model
pred1=predict(glmb.out1,type="response")

## Get Original Residuals, their means, and credible bounds
res_out=residuals(glmb.out1)
colMeans(res_out)

## Set up to simulate new data and residuals
res_mean=colMeans(res_out)
res_low1=apply(res_out,2,FUN=quantile,probs=c(0.025))
res_high1=apply(res_out,2,FUN=quantile,probs=c(0.975))

## Simulate new data and get residuals for simulated data

ysim1=simulate(glmb.out1,nsim=1,seed=NULL,pred=pred1,family="binomial",
               prior.weights=weights(glmb.out1))


res_ysim_out1=residuals(glmb.out1,ysim=ysim1)
res_low=apply(res_ysim_out1,2,FUN=quantile,probs=c(0.025))
res_high=apply(res_ysim_out1,2,FUN=quantile,probs=c(0.975))

# Plot Credible Interval bounds for Deviance Residuals

plot(res_mean~Age,ylim=c(-2.5,2.5),
main="Credible Interval Bound for Menarche - Logit Model Deviance Residuals",
xlab = "Age", ylab = "Avg. Dev. Res")
lines(Age, 0*res_mean,lty=1)
lines(Age, res_low,lty=1)
lines(Age, res_high,lty=1)
lines(Age, res_low1,lty=2)
lines(Age, res_high1,lty=2)

knygren/glmbayes documentation built on Sept. 4, 2020, 4:39 p.m.