summary.gSlc {gammSlice} | R Documentation |
gSlc
A graphical table showing, for key model parameters, the Markov chain Monte Carlo samples, diagnostic plots and numerical summaries.
## S3 method for class 'gSlc' summary(object,colour=TRUE,paletteNumber=1,...)
object |
A |
colour |
Boolean flag: |
paletteNumber |
If |
... |
place-holder for additional arguments. |
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.
Tung Pham tungstats@gmail.com and Matt Wand matt.wand@uts.edu.au.
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.
gSlc
, plot.gSlc
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) ## End(Not run)