if (FALSE) {
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
}
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