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## File Name: xxirt_newton_raphson.R
## File Version: 0.191
xxirt_newton_raphson <- function(em_out, em_args, maxit_nr, optimizer_nr,
control_nr, verbose=TRUE)
{
partable <- em_out$partable
customTheta <- em_out$customTheta
G <- em_args$G
Theta <- em_args$Theta
items <- em_args$items
item_list <- em_args$item_list
ncat <- em_args$ncat
partable_index <- em_args$partable_index
dat <- em_args$dat
dat_resp_bool <- em_args$dat_resp_bool
resp_index <- em_args$resp_index
group <- em_args$group
weights <- em_args$weights
par1 <- par_items <- xxirt_partable_extract_freeParameters( partable=partable )
par2 <- par_Theta <- xxirt_parTheta_extract_freeParameters( customTheta=customTheta )
par0 <- par <- c(par1, par2)
NPI <- length(par_items)
NPT <- length(par_Theta)
NP <- NPI+NPT
em_args$parindex_items <- 1:NPI
em_args$parindex_Theta <- (NPI+1):(NPI+NPT)
x <- par0
# define parameter tables for estimation
I <- ncol(dat)
partable$item_group_comp <- 0
for (ii in 1:I){
ind_ii <- which(partable$itemnr==ii)
partable$item_group_comp[ ind_ii ] <- 1:length(ind_ii)
}
partable$item_group_comp[ partable$parfree==0 ] <- 0
t2 <- table(partable$parindex)
i3 <- as.numeric( names(t2)[ t2 > 1] )
partable$item_group_comp[ partable$parindex %in% i3 ] <- 0
MIGC <- max(partable$item_group_comp)
em_args$partable <- partable
em_args$MIGC <- MIGC
em_args$customTheta <- customTheta
em_args$NPI <- NPI
em_args$NPT <- NPT
em_args$NP <- NP
i1 <- stats::aggregate(partable$itemnr, list(partable$parindex), min )
i2 <- stats::aggregate(partable$itemnr, list(partable$parindex), max )
free_pars_design <- data.frame( pid=1:NPI, type='item', parlabel=names(par1) )
free_pars_design$one_item <- i1[,2]==i2[,2]
free_pars_design$itemnr <- ifelse(free_pars_design$one_item, i1[,2], -9 )
partable_free <- partable[ partable$parfree==1 & partable$est, ]
free_pars_design$item_group_comp <- partable_free$item_group_comp
em_args$free_pars_design <- free_pars_design
em_args$group0 <- em_args$group - 1
test <- TRUE
test <- FALSE
if (test){
ll1 <- xxirt_nr_optim_fun(x=x, em_args=em_args)
grad1 <- xxirt_nr_grad_fun_numapprox(x=x, em_args=em_args)
grad2 <- xxirt_nr_grad_fun_Rcpp(x=x, em_args=em_args)
# Revalpr_round('grad1-grad2',5)
}
#-- optimize
control_nr$maxit <- maxit_nr
if (verbose){
cat( paste0('****** Newton-Raphson Optimization ********\n'))
utils::flush.console()
}
res_opt_nr <- sirt_optimizer(optimizer=optimizer_nr,
par=x, fn=xxirt_nr_optim_fun,
grad=xxirt_nr_grad_fun_Rcpp, hessian=FALSE,
control=control_nr, em_args=em_args )
#-- collect output
x <- res_opt_nr$par
partable <- xxirt_partable_include_freeParameters( partable=em_args$partable,
x=x[ em_args$parindex_items ] )
customTheta <- xxirt_parTheta_include_freeParameters(
customTheta=em_args$customTheta,
x=x[ em_args$parindex_Theta ])
#-- optimization function values
opt_values <- xxirt_nr_optim_fun(x=x, em_args=em_args, output_all=TRUE)
#--- output
res <- list(res_opt_nr=res_opt_nr, partable=partable, customTheta=customTheta,
opt_values=opt_values)
return(res)
}
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