Nothing
gim.default <- function(formula, family, data, model,
nsample = NULL, ncase = NULL, nctrl = NULL,
ref = NULL, ...){
argu <- list(...)
if(is.null(argu$niter)){
niter <- 100
}else{
niter <- argu$niter
if(niter < 2){
stop('niter should at least be 2')
}
}
if(is.null(argu$tol)){
tol <- 1e-4
}else{
tol <- argu$tol
}
if(is.null(argu$silent)){
silent <- TRUE
}else{
silent <- FALSE
}
data <- clean.data(data)
cm <- collapse.model(family, model, nsample, ncase, nctrl)
model <- cm$model
nsample <- cm$nsample
ncase <- cm$ncase
nctrl <- cm$nctrl
fp <- formula.parse(formula, family, data, model, ref)
model <- fp$model
data <- fp$data
ref <- fp$ref
outcome <- fp$outcome
fit0 <- fp$fit0
type <- fp$type
ini <- init(fit0, family, data, ref, model, nsample, ncase, nctrl, outcome, type)
para <- ini$para
map <- ini$map
bet0 <- ini$bet0
pr0 <- ini$pr0
Delta <- ini$Delta
sample.info <- ini$sample.info
eps <- 1.
while(niter > 0){
#message('Running Newton-Raphson algorithm on first stage...')
V <- optimal.Sigma0(para, map, family, ref, model, sample.info, pr0, Delta, outcome, type, bet0)
if(eps < tol){
break
}
fit <- NR(para, map, family, data, ref, V, bet0, sample.info, outcome, type, silent)
#fit <- loop(para, map, family, data, ref, V, bet0, sample.info, outcome, type, silent)
eps <- max(abs(para - fit$coefficients))
para <- fit$coefficients
niter <- niter - 1
}
fit$vcov <- mcov(para, map, family, data, ref, model, sample.info, V, bet0, outcome, type)
fit <- reorganize(fit, map, family, type)
fit$call <- match.call()
fit$V.bet <- V
class(fit) <- 'gim'
fit
}
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