sirt (version 1.14-0)

mcmc.list.descriptives: Computation of Descriptive Statistics for a mcmc.list Object

Description

Computation of descriptive statistics, Rhat convergence statistic and MAP for a mcmc.list object. The Rhat statistic is computed by splitting one Monte Carlo chain into three segments of equal length. The MAP is the mode estimate of the posterior distribution which is approximated by the mode of the kernel density estimate.

Usage

mcmc.list.descriptives( mcmcobj , quantiles=c(.025,.05,.1,.5,.9,.95,.975) )

Arguments

mcmcobj

Object of class mcmc.list

quantiles

Quantiles to be calculated for all parameters

Value

A data frame with descriptive statistics for all parameters in the mcmc.list object.

See Also

See mcmclist2coda for writing an object of class mcmc.list into a coda file (see also the coda package).

Examples

## Not run: 
# miceadds::library_install("coda")
# miceadds::library_install("R2WinBUGS")
# 
# #############################################################################
# # EXAMPLE 1: Logistic regression 
# #############################################################################
# 
# #***************************************
# # (1) simulate data
# set.seed(8765)
# N <- 500
# x1 <- stats::rnorm(N)
# x2 <- stats::rnorm(N)
# y <- 1*( stats::plogis( -.6 + .7*x1 + 1.1 *x2 ) > stats::runif(N) )
# 
# #***************************************
# # (2) estimate logistic regression with glm
# mod <- stats::glm( y ~ x1 + x2 , family="binomial" )
# summary(mod)
# 
# #***************************************
# # (3) estimate model with rcppbugs package
# b <- rcppbugs::mcmc.normal( stats::rnorm(3),mu=0,tau=0.0001)
# y.hat <- rcppbugs::deterministic(function(x1,x2,b) { 
#              stats::plogis( b[1] + b[2]*x1 + b[3]*x2 ) }, x1 , x2 , b)
# y.lik <- rcppbugs::mcmc.bernoulli( y , p = y.hat, observed = TRUE)
# m <- rcppbugs::create.model(b, y.hat, y.lik)
# 
# #*** estimate model in rcppbugs; 5000 iterations, 1000 burnin iterations
# ans <- rcppbugs::run.model(m, iterations=5000, burn=1000, adapt=1000, thin=5)
# print(rcppbugs::get.ar(ans))     # get acceptance rate
# print(apply(ans[["b"]],2,mean))  # get means of posterior
# 
# #*** convert rcppbugs into mcmclist object
# mcmcobj <- data.frame( ans$b  )
# colnames(mcmcobj) <- paste0("b",1:3)
# mcmcobj <- as.matrix(mcmcobj)
# class(mcmcobj) <- "mcmc"
# attr(mcmcobj, "mcpar") <- c( 1 , nrow(mcmcobj) , 1 )
# mcmcobj <- coda::as.mcmc.list( mcmcobj )
# 
# # plot results
# plot(mcmcobj)
# 
# # summary
# summ1 <-  mcmc.list.descriptives( mcmcobj )
# summ1
# ## End(Not run)