Nothing
"gblr" <- function(formula, data = NULL, family, link, mcmc = list(), prior = list(),
marginal.likelihood = TRUE, algorithm = c("AM", "KS"), verbose = FALSE) {
cl <- match.call()
ywdata <- parse.formula(formula, data = data)
yobs <- ywdata[[1]]
yname <- ywdata[[2]]
wdata <- ywdata[[3]]
wnames <- ywdata[[4]]
nobs <- nrow(yobs)
nparw <- ncol(wdata)
ndimw <- nparw - 1
if (family %in% c("bernoulli", "poisson", "negative.binomial", "poisson.gamma")) {
if (link %in% c("probit", "logit", "log")) {
if (family %in% c("poisson", "negative.binomial", "poisson.gamma") && link != "log") {
stop(paste(link, " link is not recognized in ", family, " model", sep = ""))
}
if (family == "bernoulli" && link == "log") {
stop(paste(link, " link is not recognized in ", family, " model", sep = ""))
}
} else {
stop(paste(link, " link is not recognized.", sep = ""))
}
} else {
stop(paste(family, " model is not recognized.", sep = ""))
}
if (family == "bernoulli") {
chk <- unique(yobs)
if (length(chk) != 2L) {
stop("bernoulli models only allow binary responses")
} else {
yobs <- factor(yobs, labels = 0:1)
yobs <- as.integer(paste(yobs))
}
}
if (family %in% c("poisson", "negative.binomial", "poisson.gamma")) {
chk <- (abs(yobs - round(yobs)) < .Machine$double.eps^0.5)
if (sum(chk) != length(yobs))
stop(paste(family, " model only allow count responses.", sep = ""))
}
if (family == "bernoulli" && link == "logit") {
algorithm = match.arg(algorithm)
}
privals <- list(beta_m0 = numeric(nparw), beta_v0 = diag(100, nparw),
kappa_m0 = 1, kappa_v0 = 100)
privals[names(prior)] <- prior
beta_m0 <- privals$beta_m0
beta_v0 <- privals$beta_v0
kappa_m0 <- privals$kappa_m0
kappa_v0 <- privals$kappa_v0
mcvals <- list(nblow = 10000, smcmc = 1000, nskip = 10, ndisp = 1000)
mcvals[names(mcmc)] <- mcmc
nblow <- mcvals$nblow
smcmc <- mcvals$smcmc
nskip <- mcvals$nskip
ndisp <- mcvals$ndisp
stime <- proc.time()
options(warn=-1)
if (family == "bernoulli" && link == "probit") {
betam <- glm(yobs ~ wdata - 1, family = binomial(link = probit))$coef
fout <- .Fortran("gbprobitAC", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.integer(nobs), as.integer(nparw), as.integer(nblow),
as.integer(nskip), as.integer(smcmc), as.integer(ndisp),
betaps = matrix(0, smcmc, nparw), loglikeps = numeric(smcmc),
logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
}
if (family == "bernoulli" && link == "logit") {
betam <- glm(yobs ~ wdata - 1, family = binomial(link = logit))$coef
if (algorithm == 'KS') {
fout <- .Fortran("gblogitKS", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.integer(nobs), as.integer(nparw),
as.integer(nblow), as.integer(nskip), as.integer(smcmc), as.integer(ndisp),
betaps = matrix(0, smcmc, nparw), loglikeps = numeric(smcmc),
logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
} else {
fout <- .Fortran("gblogitMH", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.integer(nobs), as.integer(nparw),
as.integer(nblow), as.integer(nskip), as.integer(smcmc), as.integer(ndisp),
betaps = matrix(0, smcmc, nparw), loglikeps = numeric(smcmc),
logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
}
}
if (family == "poisson") {
glmfit <- glm(y ~ wdata - 1, family = poisson(link = log))
betam <- glmfit$coef
mub <- glmfit$coef
Sb <- vcov(glmfit)
fout <- .Fortran("gbpoisMH", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.double(mub), as.matrix(Sb), as.integer(nobs),
as.integer(nparw), as.integer(nblow), as.integer(nskip), as.integer(smcmc),
as.integer(ndisp), betaps = matrix(0, smcmc, nparw),
loglikeps = numeric(smcmc), logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
}
if (family == "negative.binomial") {
betam <- glm.nb(yobs ~ wdata - 1)$coef
fout <- .Fortran("gbnegbinMH", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.double(kappa_m0), as.double(kappa_v0),
as.integer(nobs), as.integer(nparw), as.double(nblow), as.integer(nskip),
as.integer(smcmc), as.integer(ndisp), betaps = matrix(0, smcmc, nparw),
kappas = numeric(smcmc), loglikeps = numeric(smcmc),
logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
}
if (family == "poisson.gamma") {
betam <- glm.nb(yobs ~ wdata - 1)$coef
fout <- .Fortran("gbpoisgammMH", as.integer(verbose), as.integer(yobs), as.matrix(wdata),
as.double(betam), as.double(beta_m0), as.matrix(beta_v0),
as.double(kappa_m0), as.double(kappa_v0), as.integer(nobs),
as.integer(nparw), as.double(nblow), as.integer(nskip),
as.integer(smcmc), as.integer(ndisp), betaps = matrix(0, smcmc, nparw),
kappas = numeric(smcmc), loglikeps = numeric(smcmc),
logpriorps = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")
}
options(warn=0)
mcmctime <- proc.time() - stime
mcmc.draws <- list()
mcmc.draws$beta <- fout$betaps
if (family == "negative.binomial" || family == "poisson.gamma") {
mcmc.draws$kappa <- fout$kappas
}
loglik.draws <- list()
loglik.draws$loglike <- fout$loglikeps
loglik.draws$logprior <- fout$logpriorps
loglik.draws$logjoint <- fout$loglikeps + fout$logpriorps
fit.draws <- list()
fit.draws$wbeta <- fout$betaps %*% t(wdata)
post.est <- list()
betam <- apply(fout$betaps, 2, mean)
betas <- apply(fout$betaps, 2, sd)
post.est$betam <- betam
post.est$betas <- betas
if (family == "negative.binomial" || family == "poisson.gamma") {
kappam <- mean(fout$kappas)
kappas <- sd(fout$kappas)
post.est$kappam <- kappam
post.est$kappas <- kappas
}
if (marginal.likelihood) {
if (family == "negative.binomial" || family == "poisson.gamma") {
betapost <- cbind(fout$betaps, fout$kappas)
} else {
betapost <- fout$betaps
}
beta_mn <- colMeans(betapost)
beta_cov <- cov(betapost)
beta_covi <- solve(beta_cov)
lndetbcov <- log(det(beta_cov))
logg <- .Fortran("gbglmgetlogg", as.matrix(betapost), as.integer(smcmc),
as.integer(ncol(betapost)), as.double(beta_mn),
as.matrix(beta_covi), as.double(lndetbcov),
logg = numeric(smcmc), NAOK = TRUE, PACKAGE = "bsamGP")$logg
logjoint <- fout$loglikeps + fout$logpriorps
ratiog <- logg - logjoint
mratio <- max(ratiog)
lilg <- exp(ratiog - mratio)
lil <- -mratio - log(mean(lilg))
}
res.out <- list()
res.out$call <- cl
res.out$model <- "gblr"
res.out$family <- family
res.out$link <- link
res.out$y <- yobs
res.out$w <- wdata
res.out$n <- nobs
res.out$ndimw <- ndimw
res.out$nparw <- nparw
res.out$yname <- yname
res.out$wnames <- wnames
res.out$prior <- privals
res.out$mcmctime <- mcmctime
res.out$mcmc <- mcvals
res.out$mcmc.draws <- mcmc.draws
res.out$fit.draws <- fit.draws
res.out$loglik.draws <- loglik.draws
res.out$post.est <- post.est
res.out$marglik <- marginal.likelihood
if (marginal.likelihood) {
res.out$lmarg <- lil[1]
}
if (family == 'bernoulli' && link == 'logit') {
res.out$algorithm <- algorithm
}
class(res.out) <- "blm"
res.out
}
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