sirt (version 1.14-0)

mcmc_coef: Some Methods for Objects of Class mcmc.list

Description

Some methods for objects of class mcmc.list created from the coda package.

Usage

## coefficients	
mcmc_coef(mcmcobj, exclude = "deviance")
## covariance matrix 
mcmc_vcov(mcmcobj, exclude = "deviance")
## confidence interval
mcmc_confint( mcmcobj, parm, level = .95, exclude="deviance" )
## summary function
mcmc_summary( mcmcobj , quantiles=c(.025,.05,.50,.95,.975) )
## plot function
mcmc_plot(mcmcobj, ...)
## inclusion of derived parameters in mcmc object
mcmc_derivedPars( mcmcobj , derivedPars )
## Wald test for parameters
mcmc_WaldTest( mcmcobj , hypotheses )
"summary"(object, digits=3, ...)

Arguments

mcmcobj

Objects of class mcmc.list as created by coda::mcmc

exclude

Vector of parameters which should be excluded in calculations

parm

Optional vector of parameters

level

Confidence level

quantiles

Vector of quantiles to be computed.

...

Parameters to be passed to mcmc_plot. See plot.amh for arguments.

derivedPars

List with derived parameters (see examples).

hypotheses

List with hypotheses of the form $g_i( \bold{\theta})=0$.

object

Object of class mcmc_WaldTest.

digits

Number of digits used for rounding.

See Also

coda::mcmc

Examples

## Not run: 
# #############################################################################
# # EXAMPLE 1: Logistic regression in rcppbugs package 
# #############################################################################
# 
# 
# #***************************************
# # (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
# library(rcppbugs)
# 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)
# model <- rcppbugs::create.model(b, y.hat, y.lik)
# 
# #*** estimate model in rcppbugs; 5000 iterations, 1000 burnin iterations
# n.burnin <- 500 ; n.iter <- 2000 ; thin <- 2
# ans <- rcppbugs::run.model(model , iterations=n.iter, burn=n.burnin, adapt=200, thin=thin)
# 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( n.burnin+1 , n.iter , thin )
# mcmcobj <- coda::mcmc( mcmcobj )
# 
# # coefficients, variance covariance matrix and confidence interval
# mcmc_coef(mcmcobj)
# mcmc_vcov(mcmcobj)
# mcmc_confint( mcmcobj , level = .90 )
# 
# # summary and plot
# mcmc_summary(mcmcobj)
# mcmc_plot(mcmcobj, ask=TRUE)
# 
# # include derived parameters in mcmc object
# derivedPars <- list( "diff12" = ~ I(b2-b1) , "diff13" = ~ I(b3-b1) )
# mcmcobj2 <- mcmc_derivedPars(mcmcobj , derivedPars = derivedPars )
# mcmc_summary(mcmcobj2)
# 
# #*** Wald test for parameters
#  # hyp1: b2 - 0.5 = 0
#  # hyp2: b2 * b3 = 0
# hypotheses <- list( "hyp1" = ~ I( b2 - .5 )  , "hyp2" = ~ I( b2*b3 ) )
# test1 <- mcmc_WaldTest( mcmcobj , hypotheses=hypotheses )
# summary(test1)
# ## End(Not run)