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

In alexanderrobitzsch/sirt: Supplementary Item Response Theory Models

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 <-  sirt::mcmc.list.descriptives( mcmcobj )
summ1

## End(Not run)

alexanderrobitzsch/sirt documentation built on Dec. 1, 2024, 2:18 a.m.