residuals.glmb: Accessing Bayesian Generalized Linear Model Fits

In glmbayes: Bayesian Generalized Linear Models (IID Samples)

Accessing Bayesian Generalized Linear Model Fits

Description

Extract deviance residuals from fitted Bayesian GLM objects. The residuals use the family's deviance residuals function as in residuals.glm \insertCiteMcCullagh1989glmbayes.

Usage

## S3 method for class 'glmb'
residuals(object, ysim = NULL, ...)

## S3 method for class 'rglmb'
residuals(object, ysim = NULL, ...)

## S3 method for class 'lmb'
residuals(object, ysim = NULL, ...)

Arguments

object	an object of class glmb, typically the result of a call to glmb
ysim	Optional simulated data for the data y.
...	further arguments to or from other methods

Details

These functions are all methods for class glmb, lmb, or summary.glmb objects.

Value

A matrix DevRes of dimension n times p containing the Deviance residuals for each draw. If ysim is provided, the residuals are based on a comparison to the simulated data instead. The credible intervals for residuals based on simulated data should be a more appropriate measure of whether individual residuals represent outliers or not.

References

\insertAllCited

See Also

predict.glmb, summary.glmb, glmb, glmbayes-package; rglmb, rlmb, lmb; residuals.glm

Examples

data(menarche,package="MASS")

## ----Analysis Setup-----------------------------------------------------------
## Number of variables in model
Age=menarche$Age
nvars=2
## Reference Ages for setting of priors and Age_Difference
ref_age1=13  # user can modify this
ref_age2=15  ## user can modify this
## Define variables used later in analysis
Age2=menarche$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=menarche$Age,Total=menarche$Total,
                               Menarche=menarche$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")

## Posterior predictive check (bayesplot): disabled in package examples.
## Former pp_check.glmb() lives in legacy_code/pp_check.glmb.R — source after
## install.packages("bayesplot") to restore, then:
##   bayesplot::pp_check(glmb.out1, ndraws = 100L, fun = "hist")

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

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

## Simulate new data and get residuals for simulated data

ysim1=simulate(glmb.out1,nsim=1,seed=10401L,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),na.rm=TRUE)
res_high=apply(res_ysim_out1,2,FUN=quantile,probs=c(0.975),na.rm=TRUE)


oldpar <- par(no.readonly = TRUE)
par(mar = c(5, 4, 4, 2) + 0.1)  # Standard margin setup

# 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)

par(oldpar)

glmbayes documentation built on Aug. 5, 2026, 1:07 a.m.