Description Usage Arguments Value Examples
View source: R/residuals.glmb.R
These functions are all methods for class glmb
or summary.glmb
objects.
1 2 3 4 5 |
object |
an object of class |
ysim |
Optional simulated data for the data y. |
... |
further arguments to or from other methods |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | 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)
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