summary.gSlc: Summary of the generalized additive mixed model fit produced by `gSlc`

Description

A graphical table showing, for key model parameters, the Markov chain Monte Carlo samples, diagnostic plots and numerical summaries.

Usage

# S3 method for gSlc
summary(object,colour=TRUE,paletteNumber=1,...)

Arguments

object

A gSlc() fit object.

colour

Boolean flag: TRUE = produce a colour graphical table, FALSE = produce a black and white graphical table .

paletteNumber

If colour = TRUE then there there are two possible colour palettes. These are determined by whether paletteNumber is set to 1 or 2. The default is paletteNumber=1.

...

place-holder for additional arguments.

Details

The columns of the graphical table are:

1. parameter name, 2. trace plot of the Markov chain Monte Carlo sample, 3. plot of Markov chain Monte Carlo sample against its lag 1 sample, 4. sample autocorrelation function, 5. kernel density estimate of the posterior density function, 6. posterior mean and 95% credible interval.

References

Pham, T.H. and Wand, M.P. (2018). Generalized additive mixed model analysis via gammSlice. Australian and New Zealand Journal of Statistics, 60, 279-300.

Examples

Run this code

# NOT RUN {
library(gammSlice)
set.seed(39402) ; m <- 100 ; n <- 2
beta0True <- 0.5 ; betaxTrue <- 1.7 ; sigsqTrue <- 0.8
idnum <- rep(1:m,each=n) ; x <- runif(m*n)
U <- rep(rnorm(m,0,sqrt(sigsqTrue)),each=n)
mu <- 1/(1+exp(-(beta0True+betaxTrue*x+U)))
y <- rbinom((m*n),1,mu)
fit1 <- gSlc(y ~ x,random = list(idnum = ~1),family = "binomial",
             control = gSlc.control(nBurn=150,nKept=100,nThin=1))
summary(fit1)
summary(fit1,paletteNumber = 2)
summary(fit1,colour = FALSE)

# }
# NOT RUN {
# Re-fit with higher Markov chain Monte Carlo sample:

fit2 <- gSlc(y ~ x,random = list(idnum = ~1),family = "binomial")
summary(fit2)
summary(fit2,paletteNumber = 2)
summary(fit2,colour = FALSE)
# }