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R/csem.R
In sem: Structural Equation Models
Defines functions
cmsem
csem
print.f
msemCompiledSolve
CompiledSolve
msemCompiledObjective
CompiledObjective
# File: csem.R
# Author: Zhenghua Nie
# Date: Mon 26 Dec 2011 23:54:22 EST
#
#
#
# Copyright (C) 2011 Zhenghua Nie. All Rights Reserved.
# This code is published under GNU GENERAL PUBLIC LICENSE.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed WITHOUT ANY WARRANTY. See the
# GNU General Public License for more details.
#
# If you do not have a copy of the GNU General Public License,
# write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
# The following function is a wrapper to compute the objective function and its gradient.
# If hessian=TRUE, csem will return Hessian computed by the numerical method, but
# is flexible to return Hessian computed by the analytical solution.
CompiledObjective <- function(par, model.description, gradient=TRUE, hessian=FALSE, objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik"), ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
res <- csem(model=model.description, start=par, objective=objective, opt.flag=0, gradient=gradient, opts=list("hessian"=hessian, "check.analyticals"=FALSE), ...)
ret <- list();
ret$f <- res$minimum
ret$parameters <- res$estimate
ret$C <- res$C
ret$A <- res$A
ret$P <- res$P
ret$gradient <- res$gradient
ret$hessian <- res$hessian
return(ret)
}
msemCompiledObjective <- function(par, model.description, gradient=TRUE, hessian=FALSE, objective=c("objectiveML", "objectiveGLS", "objectiveFIML"), ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
res <- cmsem(model=model.description, start=par, objective=objective, opt.flag=0, gradient=gradient, opts=list("hessian"=hessian, "check.analyticals"=FALSE), ...)
AA <- PP <- CC <- vector(model.description$G, mode="list")
indAP <- 1
indC <- 1
ff <- numeric(model.description$G)
for(g in 1:model.description$G)
{
m <- model.description$m[g]
n <- model.description$n[g]
AA[[g]] <- matrix(res$A[indAP:(indAP+m*m-1)], m, m);
PP[[g]] <- matrix(res$P[indAP:(indAP+m*m-1)], m, m);
indAP <- indAP + m*m;
CC[[g]] <- matrix(res$C[indC:(indC+n*n-1)], n, n);
indC <- indC + n*n;
ff[g] <- as.numeric(res$f[g])
}
ret <- list();
ret$f <- res$minimum
ret$parameters <- res$estimate
ret$C <- CC
ret$A <- AA
ret$P <- PP
ret$ff <- ff
ret$gradient <- res$gradient
ret$hessian <- res$hessian
return(ret)
}
# The wrapper function for solving optimization problems. Please note that the objective function is written in C/C++, we need to know the name.
CompiledSolve <- function(model.description, start, objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik"), gradient=TRUE, typsize=rep(1.0, length(start)), debug=FALSE, maxiter=100,...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
stepmax=max(1000.0 * sqrt(sum((start/typsize)^2)), 1000.0)
res <- csem(model=model.description, start, opt.flag=1, typsize=typsize,objective=objective,
gradient=gradient,
opts=list("iterlim"=maxiter, "print.level"=if(debug) 2 else 0,
"hessian"=TRUE, "check.analyticals"=FALSE, "stepmax"=stepmax), ...)
return(res)
}
# The wrapper function for solving optimization problems. Please note that the objective function is written in C/C++, we need to know the name.
msemCompiledSolve <- function(model.description, start, objective=c("objectiveML", "objectiveGLS", "objectiveFIML"),
gradient=TRUE,
typsize=rep(1.0, length(start)), debug=FALSE, maxiter=100,gradtol=1e-6, ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
stepmax=max(1000.0 * sqrt(sum((start/typsize)^2)), 1000.0)
res <- cmsem(model=model.description, start, opt.flag=1, typsize=typsize,objective=objective,
gradient=gradient,
opts=list("iterlim"=maxiter, "print.level"=if(debug) 2 else 0,"gradtol"=gradtol,
"hessian"=TRUE, "check.analyticals"=FALSE, "stepmax"=stepmax), ...)
#reoraginize the matrix A, P, C
AA <- PP <- CC <- vector(model.description$G, mode="list")
indAP <- 1
indC <- 1
ff <- numeric(model.description$G)
for(g in 1:model.description$G)
{
m <- as.integer(model.description$m[g])
n <- as.integer(model.description$n[g])
AA[[g]] <- matrix(as.numeric(res$A)[indAP:(indAP+m*m-1)], m, m);
PP[[g]] <- matrix(as.numeric(res$P)[indAP:(indAP+m*m-1)], m, m);
indAP <- indAP + m*m;
CC[[g]] <- matrix(as.numeric(res$C)[indC:(indC+n*n-1)], n, n);
indC <- indC + n*n;
ff[g] <- as.numeric(res$f[g])
}
ret <- list();
ret$minimum <- res$minimum
ret$estimate <- res$estimate
ret$gradient <- res$gradient
ret$hessian <- res$hessian
ret$code <- res$code
ret$iterations <- res$iterations
ret$C <- CC
ret$A <- AA
ret$P <- PP
ret$ff <- ff
return(ret)
}
print.f <- function(input)
{
print(input); # call R function "print"
}
#optimze:0 we only compute the objective function, gradients or hessian and return them.
#
csem <- function(model=NULL, start=NULL,opt.flag=1, typsize=rep(1, model$t), objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik", "test_objective"),
gradient=TRUE,
opts=list("hessian"=1, "fscale"=1, "gradtol"=1e-6, "steptol"=1e-6, "stepmax"=max(1000 * sqrt(sum((start/typsize)^2)), 1000), "iterlim"=100,
"ndigit"=12,"print.level"=0, "check.analyticals"=1),
csem.environment = new.env(), ...){
environment(print.f) <- csem.environment;
## Write wrappers around user-defined functions to pass additional
## arguments
print.f.wrapper <- function(x){ print.f(x,...) }
if(missing(model)) stop("Must provide the model.")
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
if(missing(typsize) || is.null(typsize)) typsize <- rep(1, model$t)
if(missing(start)) start <- rep(0.10, model$t)
if(length(opts$print.level)==0)
print.level <- 0
else
print.level <- as.integer(opts$print.level)
if(print.level < 0 || print.level > 2) stop("'print.level' must be in {0, 1, 2}")
## the following is for generating gradient.
if(objective != "objectivelogLik")
{
arrows.1.seq <- model$ram[model$ram[, 1]==1 & model$ram[, 4]!=0, 4]
arrows.2.seq <- model$ram[model$ram[, 1]==2 & model$ram[, 4]!=0, 4]
}
# this function is modfied from ipoptr developed by Jelmer Ypma (http://www.ucl.ac.uk/~uctpjyy/ipoptr.html).
# Please reference the license of ipoptr.
get.option.types <- function(opts) {
# define types of nlm options, we should add all options here.
nlm.option.types <- list(
"fscale"="numeric",
"gradtol"="numeric",
"steptol"="numeric",
"stepmax"="numeric",
"hessian"="integer",
"iterlim"="integer",
"ndigit"="integer",
"print.level"="integer",
"check.analyticals"="integer"
)
# initialize list with options sorted by type
converted.opts <- list( "integer"=list(), "string"=list(), "numeric"=list() )
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
# check if we have at least 1 element in the list, otherwise the
# loop runs from 1 to down 0 and we get errors
if ( length( opts ) > 0 ) {
# loop over all options and give them the correct type
for ( i in 1:length( opts ) ) {
tmp.type <- nlm.option.types[[match( names(opts)[i], names(nlm.option.types) )]]
if ( is.null( tmp.type ) ) {
# determine type
if ( is.character(opts[[i]]) ) {
tmp.type <- "string"
} else if ( is.wholenumber(opts[[i]]) ) {
tmp.type <- "integer"
} else {
tmp.type <- "numeric"
}
cat( paste( "Warning: ", names(opts)[i], " is not a recognized option, we try to pass it to nlm as ", tmp.type, "\n" ) )
}
if ( tmp.type=="string" ) {
converted.opts$string[[ names(opts)[i] ]] <- as.character(opts[[i]])
} else if ( tmp.type=="integer" ) {
converted.opts$integer[[ names(opts)[i] ]] <- as.integer(opts[[i]])
} else if ( tmp.type=="numeric" ) {
converted.opts$numeric[[ names(opts)[i] ]] <- as.numeric(opts[[i]])
} else {
stop(paste("Type of option ", names(opts)[i], " not recognized"))
}
}
}
return ( converted.opts )
}
if(objective != "objectivelogLik")
{
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"options" = get.option.types(opts),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"S" = model$S,
"logdetS" = as.numeric(model$logdetS),
"invS" = model$invS,
"N" = as.integer(model$N),
"m" = as.integer(model$m),
"n" = as.integer(model$n),
"t" = as.integer(model$t),
"fixed" = model$fixed,
"ram" = model$ram,
"sel.free" = model$sel.free,
"arrows.1" = model$arrows.1,
"arrows.1.free" = model$arrows.1.free,
"one.head" = model$one.head,
"arrows.2t" = model$arrows.2t,
"arrows.2" = model$arrows.2,
"arrows.2.free" = model$arrows.2.free,
"unique.free.1" = model$unique.free.1,
"unique.free.2" = model$unique.free.2,
"J" = model$J,
"correct" = model$correct,
"param.names" = model$param.names,
"var.names" = model$var.names,
"one.free" = model$one.free,
"two.free" = model$two.free,
"raw" = as.integer(model$raw),
"arrows.1.seq" = arrows.1.seq,
"arrows.2.seq" = arrows.2.seq,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment)
attr(ret, "class") <- "csem"
}
else
{
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"t" = length(start),
"options" = get.option.types(opts),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"tri" = model$tri,
"posn.intercept" = model$posn.intercept,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment
)
}
# add the current call to the list
# ret$call <- match.call()
solution <- .Call("csemSolve", ret)
# ret$environment <- NULL
# ret$solution <- solution
ret <- solution #this is for simplifing the interface.
# add solution variables to object
#ret$status <- solution$status
return(ret)
}
cmsem <- function(model=NULL, start=NULL,opt.flag=1, typsize=rep(1, model$t), objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "test_objective"),
gradient=TRUE,
opts=list("hessian"=1, "fscale"=1, "gradtol"=1e-6, "steptol"=1e-6, "stepmax"=max(1000 * sqrt(sum((start/typsize)^2)), 1000), "iterlim"=100,
"ndigit"=12,"print.level"=0, "check.analyticals"=1),
csem.environment = new.env(), ...){
environment(print.f) <- csem.environment;
## Write wrappers around user-defined functions to pass additional
## arguments
print.f.wrapper <- function(x){ print.f(x,...) }
if(missing(model)) stop("Must provide the model.")
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
if(missing(typsize) || is.null(typsize)) typsize <- rep(1, model$t)
if(missing(start)) start <- rep(0.10, model$t)
if(length(opts$print.level)==0)
print.level <- 0
else
print.level <- as.integer(opts$print.level)
if(print.level < 0 || print.level > 2) stop("'print.level' must be in {0, 1, 2}")
## the following is for generating gradient.
G <- model$G
arrows.1.seq <- arrows.2.seq <- vector(G, mode="list")
for(g in 1:G)
{
arrows.1.seq[[g]] <- model$ram[[g]][model$ram[[g]][, 1]==1 & model$ram[[g]][, 4]!=0, 4]
arrows.2.seq[[g]] <- model$ram[[g]][model$ram[[g]][, 1]==2 & model$ram[[g]][, 4]!=0, 4]
}
get.option.types <- function(opts) {
# define types of nlm options, we should add all options here.
nlm.option.types <- list(
"fscale"="numeric",
"gradtol"="numeric",
"steptol"="numeric",
"stepmax"="numeric",
"hessian"="integer",
"iterlim"="integer",
"ndigit"="integer",
"print.level"="integer",
"check.analyticals"="integer"
)
# initialize list with options sorted by type
converted.opts <- list( "integer"=list(), "string"=list(), "numeric"=list() )
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
# check if we have at least 1 element in the list, otherwise the
# loop runs from 1 to down 0 and we get errors
if ( length( opts ) > 0 ) {
# loop over all options and give them the correct type
for ( i in 1:length( opts ) ) {
tmp.type <- nlm.option.types[[match( names(opts)[i], names(nlm.option.types) )]]
if ( is.null( tmp.type ) ) {
# determine type
if ( is.character(opts[[i]]) ) {
tmp.type <- "string"
} else if ( is.wholenumber(opts[[i]]) ) {
tmp.type <- "integer"
} else {
tmp.type <- "numeric"
}
cat( paste( "Warning: ", names(opts)[i], " is not a recognized option, we try to pass it to nlm as ", tmp.type, "\n" ) )
}
if ( tmp.type=="string" ) {
converted.opts$string[[ names(opts)[i] ]] <- as.character(opts[[i]])
} else if ( tmp.type=="integer" ) {
converted.opts$integer[[ names(opts)[i] ]] <- as.integer(opts[[i]])
} else if ( tmp.type=="numeric" ) {
converted.opts$numeric[[ names(opts)[i] ]] <- as.numeric(opts[[i]])
} else {
stop(paste("Type of option ", names(opts)[i], " not recognized"))
}
}
}
return ( converted.opts )
}
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"options" = get.option.types(opts),
"G" = as.integer(model$G),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"S" = model$S,
"logdetS" = model$logdetS,
"invS" = model$invS,
"N" = model$N,
"m" = model$m,
"n" = model$n,
"t" = as.integer(model$t),
"fixed" = model$fixed,
"ram" = model$ram,
"sel.free" = model$sel.free,
"arrows.1" = model$arrows.1,
"arrows.1.free" = model$arrows.1.free,
"one.head" = model$one.head,
"arrows.2t" = model$arrows.2t,
"arrows.2" = model$arrows.2,
"arrows.2.free" = model$arrows.2.free,
"unique.free.1" = model$unique.free.1,
"unique.free.2" = model$unique.free.2,
"J" = model$J,
"correct" = model$correct,
"param.names" = model$param.names,
"var.names" = model$var.names,
"one.free" = model$one.free,
"two.free" = model$two.free,
"raw" = as.integer(model$raw),
"arrows.1.seq" = arrows.1.seq,
"arrows.2.seq" = arrows.2.seq,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment)
attr(ret, "class") <- "cmsem"
# add the current call to the list
# ret$call <- match.call()
solution <- .Call("cmsemSolve", ret)
# ret$environment <- NULL
# ret$solution <- solution
ret <- solution #this is for simplifing the interface.
# add solution variables to object
#ret$status <- solution$status
return(ret)
}
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Defines functions cmsem csem print.f msemCompiledSolve CompiledSolve msemCompiledObjective CompiledObjective
# File: csem.R
# Author: Zhenghua Nie
# Date: Mon 26 Dec 2011 23:54:22 EST
#
#
#
# Copyright (C) 2011 Zhenghua Nie. All Rights Reserved.
# This code is published under GNU GENERAL PUBLIC LICENSE.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed WITHOUT ANY WARRANTY. See the
# GNU General Public License for more details.
#
# If you do not have a copy of the GNU General Public License,
# write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
# The following function is a wrapper to compute the objective function and its gradient.
# If hessian=TRUE, csem will return Hessian computed by the numerical method, but
# is flexible to return Hessian computed by the analytical solution.
CompiledObjective <- function(par, model.description, gradient=TRUE, hessian=FALSE, objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik"), ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
res <- csem(model=model.description, start=par, objective=objective, opt.flag=0, gradient=gradient, opts=list("hessian"=hessian, "check.analyticals"=FALSE), ...)
ret <- list();
ret$f <- res$minimum
ret$parameters <- res$estimate
ret$C <- res$C
ret$A <- res$A
ret$P <- res$P
ret$gradient <- res$gradient
ret$hessian <- res$hessian
return(ret)
}
msemCompiledObjective <- function(par, model.description, gradient=TRUE, hessian=FALSE, objective=c("objectiveML", "objectiveGLS", "objectiveFIML"), ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
res <- cmsem(model=model.description, start=par, objective=objective, opt.flag=0, gradient=gradient, opts=list("hessian"=hessian, "check.analyticals"=FALSE), ...)
AA <- PP <- CC <- vector(model.description$G, mode="list")
indAP <- 1
indC <- 1
ff <- numeric(model.description$G)
for(g in 1:model.description$G)
{
m <- model.description$m[g]
n <- model.description$n[g]
AA[[g]] <- matrix(res$A[indAP:(indAP+m*m-1)], m, m);
PP[[g]] <- matrix(res$P[indAP:(indAP+m*m-1)], m, m);
indAP <- indAP + m*m;
CC[[g]] <- matrix(res$C[indC:(indC+n*n-1)], n, n);
indC <- indC + n*n;
ff[g] <- as.numeric(res$f[g])
}
ret <- list();
ret$f <- res$minimum
ret$parameters <- res$estimate
ret$C <- CC
ret$A <- AA
ret$P <- PP
ret$ff <- ff
ret$gradient <- res$gradient
ret$hessian <- res$hessian
return(ret)
}
# The wrapper function for solving optimization problems. Please note that the objective function is written in C/C++, we need to know the name.
CompiledSolve <- function(model.description, start, objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik"), gradient=TRUE, typsize=rep(1.0, length(start)), debug=FALSE, maxiter=100,...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
stepmax=max(1000.0 * sqrt(sum((start/typsize)^2)), 1000.0)
res <- csem(model=model.description, start, opt.flag=1, typsize=typsize,objective=objective,
gradient=gradient,
opts=list("iterlim"=maxiter, "print.level"=if(debug) 2 else 0,
"hessian"=TRUE, "check.analyticals"=FALSE, "stepmax"=stepmax), ...)
return(res)
}
# The wrapper function for solving optimization problems. Please note that the objective function is written in C/C++, we need to know the name.
msemCompiledSolve <- function(model.description, start, objective=c("objectiveML", "objectiveGLS", "objectiveFIML"),
gradient=TRUE,
typsize=rep(1.0, length(start)), debug=FALSE, maxiter=100,gradtol=1e-6, ...)
{
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
stepmax=max(1000.0 * sqrt(sum((start/typsize)^2)), 1000.0)
res <- cmsem(model=model.description, start, opt.flag=1, typsize=typsize,objective=objective,
gradient=gradient,
opts=list("iterlim"=maxiter, "print.level"=if(debug) 2 else 0,"gradtol"=gradtol,
"hessian"=TRUE, "check.analyticals"=FALSE, "stepmax"=stepmax), ...)
#reoraginize the matrix A, P, C
AA <- PP <- CC <- vector(model.description$G, mode="list")
indAP <- 1
indC <- 1
ff <- numeric(model.description$G)
for(g in 1:model.description$G)
{
m <- as.integer(model.description$m[g])
n <- as.integer(model.description$n[g])
AA[[g]] <- matrix(as.numeric(res$A)[indAP:(indAP+m*m-1)], m, m);
PP[[g]] <- matrix(as.numeric(res$P)[indAP:(indAP+m*m-1)], m, m);
indAP <- indAP + m*m;
CC[[g]] <- matrix(as.numeric(res$C)[indC:(indC+n*n-1)], n, n);
indC <- indC + n*n;
ff[g] <- as.numeric(res$f[g])
}
ret <- list();
ret$minimum <- res$minimum
ret$estimate <- res$estimate
ret$gradient <- res$gradient
ret$hessian <- res$hessian
ret$code <- res$code
ret$iterations <- res$iterations
ret$C <- CC
ret$A <- AA
ret$P <- PP
ret$ff <- ff
return(ret)
}
print.f <- function(input)
{
print(input); # call R function "print"
}
#optimze:0 we only compute the objective function, gradients or hessian and return them.
#
csem <- function(model=NULL, start=NULL,opt.flag=1, typsize=rep(1, model$t), objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "objectivelogLik", "test_objective"),
gradient=TRUE,
opts=list("hessian"=1, "fscale"=1, "gradtol"=1e-6, "steptol"=1e-6, "stepmax"=max(1000 * sqrt(sum((start/typsize)^2)), 1000), "iterlim"=100,
"ndigit"=12,"print.level"=0, "check.analyticals"=1),
csem.environment = new.env(), ...){
environment(print.f) <- csem.environment;
## Write wrappers around user-defined functions to pass additional
## arguments
print.f.wrapper <- function(x){ print.f(x,...) }
if(missing(model)) stop("Must provide the model.")
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
if(missing(typsize) || is.null(typsize)) typsize <- rep(1, model$t)
if(missing(start)) start <- rep(0.10, model$t)
if(length(opts$print.level)==0)
print.level <- 0
else
print.level <- as.integer(opts$print.level)
if(print.level < 0 || print.level > 2) stop("'print.level' must be in {0, 1, 2}")
## the following is for generating gradient.
if(objective != "objectivelogLik")
{
arrows.1.seq <- model$ram[model$ram[, 1]==1 & model$ram[, 4]!=0, 4]
arrows.2.seq <- model$ram[model$ram[, 1]==2 & model$ram[, 4]!=0, 4]
}
# this function is modfied from ipoptr developed by Jelmer Ypma (http://www.ucl.ac.uk/~uctpjyy/ipoptr.html).
# Please reference the license of ipoptr.
get.option.types <- function(opts) {
# define types of nlm options, we should add all options here.
nlm.option.types <- list(
"fscale"="numeric",
"gradtol"="numeric",
"steptol"="numeric",
"stepmax"="numeric",
"hessian"="integer",
"iterlim"="integer",
"ndigit"="integer",
"print.level"="integer",
"check.analyticals"="integer"
)
# initialize list with options sorted by type
converted.opts <- list( "integer"=list(), "string"=list(), "numeric"=list() )
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
# check if we have at least 1 element in the list, otherwise the
# loop runs from 1 to down 0 and we get errors
if ( length( opts ) > 0 ) {
# loop over all options and give them the correct type
for ( i in 1:length( opts ) ) {
tmp.type <- nlm.option.types[[match( names(opts)[i], names(nlm.option.types) )]]
if ( is.null( tmp.type ) ) {
# determine type
if ( is.character(opts[[i]]) ) {
tmp.type <- "string"
} else if ( is.wholenumber(opts[[i]]) ) {
tmp.type <- "integer"
} else {
tmp.type <- "numeric"
}
cat( paste( "Warning: ", names(opts)[i], " is not a recognized option, we try to pass it to nlm as ", tmp.type, "\n" ) )
}
if ( tmp.type=="string" ) {
converted.opts$string[[ names(opts)[i] ]] <- as.character(opts[[i]])
} else if ( tmp.type=="integer" ) {
converted.opts$integer[[ names(opts)[i] ]] <- as.integer(opts[[i]])
} else if ( tmp.type=="numeric" ) {
converted.opts$numeric[[ names(opts)[i] ]] <- as.numeric(opts[[i]])
} else {
stop(paste("Type of option ", names(opts)[i], " not recognized"))
}
}
}
return ( converted.opts )
}
if(objective != "objectivelogLik")
{
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"options" = get.option.types(opts),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"S" = model$S,
"logdetS" = as.numeric(model$logdetS),
"invS" = model$invS,
"N" = as.integer(model$N),
"m" = as.integer(model$m),
"n" = as.integer(model$n),
"t" = as.integer(model$t),
"fixed" = model$fixed,
"ram" = model$ram,
"sel.free" = model$sel.free,
"arrows.1" = model$arrows.1,
"arrows.1.free" = model$arrows.1.free,
"one.head" = model$one.head,
"arrows.2t" = model$arrows.2t,
"arrows.2" = model$arrows.2,
"arrows.2.free" = model$arrows.2.free,
"unique.free.1" = model$unique.free.1,
"unique.free.2" = model$unique.free.2,
"J" = model$J,
"correct" = model$correct,
"param.names" = model$param.names,
"var.names" = model$var.names,
"one.free" = model$one.free,
"two.free" = model$two.free,
"raw" = as.integer(model$raw),
"arrows.1.seq" = arrows.1.seq,
"arrows.2.seq" = arrows.2.seq,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment)
attr(ret, "class") <- "csem"
}
else
{
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"t" = length(start),
"options" = get.option.types(opts),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"tri" = model$tri,
"posn.intercept" = model$posn.intercept,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment
)
}
# add the current call to the list
# ret$call <- match.call()
solution <- .Call("csemSolve", ret)
# ret$environment <- NULL
# ret$solution <- solution
ret <- solution #this is for simplifing the interface.
# add solution variables to object
#ret$status <- solution$status
return(ret)
}
cmsem <- function(model=NULL, start=NULL,opt.flag=1, typsize=rep(1, model$t), objective=c("objectiveML", "objectiveGLS", "objectiveFIML", "test_objective"),
gradient=TRUE,
opts=list("hessian"=1, "fscale"=1, "gradtol"=1e-6, "steptol"=1e-6, "stepmax"=max(1000 * sqrt(sum((start/typsize)^2)), 1000), "iterlim"=100,
"ndigit"=12,"print.level"=0, "check.analyticals"=1),
csem.environment = new.env(), ...){
environment(print.f) <- csem.environment;
## Write wrappers around user-defined functions to pass additional
## arguments
print.f.wrapper <- function(x){ print.f(x,...) }
if(missing(model)) stop("Must provide the model.")
if(missing(objective)) objective <- "objectiveML"
objective <- match.arg(objective)
if(missing(typsize) || is.null(typsize)) typsize <- rep(1, model$t)
if(missing(start)) start <- rep(0.10, model$t)
if(length(opts$print.level)==0)
print.level <- 0
else
print.level <- as.integer(opts$print.level)
if(print.level < 0 || print.level > 2) stop("'print.level' must be in {0, 1, 2}")
## the following is for generating gradient.
G <- model$G
arrows.1.seq <- arrows.2.seq <- vector(G, mode="list")
for(g in 1:G)
{
arrows.1.seq[[g]] <- model$ram[[g]][model$ram[[g]][, 1]==1 & model$ram[[g]][, 4]!=0, 4]
arrows.2.seq[[g]] <- model$ram[[g]][model$ram[[g]][, 1]==2 & model$ram[[g]][, 4]!=0, 4]
}
get.option.types <- function(opts) {
# define types of nlm options, we should add all options here.
nlm.option.types <- list(
"fscale"="numeric",
"gradtol"="numeric",
"steptol"="numeric",
"stepmax"="numeric",
"hessian"="integer",
"iterlim"="integer",
"ndigit"="integer",
"print.level"="integer",
"check.analyticals"="integer"
)
# initialize list with options sorted by type
converted.opts <- list( "integer"=list(), "string"=list(), "numeric"=list() )
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
# check if we have at least 1 element in the list, otherwise the
# loop runs from 1 to down 0 and we get errors
if ( length( opts ) > 0 ) {
# loop over all options and give them the correct type
for ( i in 1:length( opts ) ) {
tmp.type <- nlm.option.types[[match( names(opts)[i], names(nlm.option.types) )]]
if ( is.null( tmp.type ) ) {
# determine type
if ( is.character(opts[[i]]) ) {
tmp.type <- "string"
} else if ( is.wholenumber(opts[[i]]) ) {
tmp.type <- "integer"
} else {
tmp.type <- "numeric"
}
cat( paste( "Warning: ", names(opts)[i], " is not a recognized option, we try to pass it to nlm as ", tmp.type, "\n" ) )
}
if ( tmp.type=="string" ) {
converted.opts$string[[ names(opts)[i] ]] <- as.character(opts[[i]])
} else if ( tmp.type=="integer" ) {
converted.opts$integer[[ names(opts)[i] ]] <- as.integer(opts[[i]])
} else if ( tmp.type=="numeric" ) {
converted.opts$numeric[[ names(opts)[i] ]] <- as.numeric(opts[[i]])
} else {
stop(paste("Type of option ", names(opts)[i], " not recognized"))
}
}
}
return ( converted.opts )
}
ret <- list(
"objective" = objective,
"gradient" = as.integer(gradient),
"opt.flg" = as.integer(opt.flag),
"start" = start,
"options" = get.option.types(opts),
"G" = as.integer(model$G),
"data" = model$data,
"pattern.number" = model$pattern.number,
"valid.data.patterns" = model$valid.data.patterns,
"S" = model$S,
"logdetS" = model$logdetS,
"invS" = model$invS,
"N" = model$N,
"m" = model$m,
"n" = model$n,
"t" = as.integer(model$t),
"fixed" = model$fixed,
"ram" = model$ram,
"sel.free" = model$sel.free,
"arrows.1" = model$arrows.1,
"arrows.1.free" = model$arrows.1.free,
"one.head" = model$one.head,
"arrows.2t" = model$arrows.2t,
"arrows.2" = model$arrows.2,
"arrows.2.free" = model$arrows.2.free,
"unique.free.1" = model$unique.free.1,
"unique.free.2" = model$unique.free.2,
"J" = model$J,
"correct" = model$correct,
"param.names" = model$param.names,
"var.names" = model$var.names,
"one.free" = model$one.free,
"two.free" = model$two.free,
"raw" = as.integer(model$raw),
"arrows.1.seq" = arrows.1.seq,
"arrows.2.seq" = arrows.2.seq,
"typsize" = typsize,
"print.f" = print.f.wrapper,
"csem.environment"=csem.environment)
attr(ret, "class") <- "cmsem"
# add the current call to the list
# ret$call <- match.call()
solution <- .Call("cmsemSolve", ret)
# ret$environment <- NULL
# ret$solution <- solution
ret <- solution #this is for simplifing the interface.
# add solution variables to object
#ret$status <- solution$status
return(ret)
}
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