residuals.glmb: Accessing Bayesian Generalized Linear Model Fits

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

Description

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

Usage

## S3 method for class 'glmb'
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

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.

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.