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# bootstrapped standard errors and confidence intervals for sem
# last modified 2015-06-09 by J. Fox
boot.sem <- function(...) {
.Deprecated("bootSem", package="sem")
bootSem(...)
}
bootSem <- function (model, ...){
UseMethod("bootSem")
}
bootSem.sem <- function(model, R=100, Cov=cov, data=model$data, max.failures=10, show.progress=TRUE, ...){
refit <- function(){
indices <- sample(N, N, replace=TRUE)
S <- Cov(data[indices,])
refitted.model <- sem(ram, S, N, param.names=coef.names, var.names=var.names,
optimizer=model$optimizer, objective=model$objective, ...)
refitted.model$coeff
}
# if (!require("boot")) stop("package boot not available")
# has.tcltk <- require("tcltk")
# pb <- tkProgressBar("Bootstrap Sampling", "Bootstrap sample: ", 0, R)
if (show.progress){
cat("\n", R, "bootstrap replications\n")
pb <- txtProgressBar(min=0, max=R, style=3)
}
# the following 2 lines borrowed from boot in package boot
if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) runif(1)
seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
warn <- options(warn=-2)
on.exit(options(warn)) # insure restore even in event of error
nErrors <- 0
if (is.null(data)) stop("the model object doesn't contain a data matrix")
N <- nrow(data)
coefficients <- model$coeff
coef.names <- names(coefficients)
var.names <- model$var.names
ram <- model$ram
ram[coef.names, "start value"] <- coefficients
coefs <- matrix(numeric(0), R, length(coefficients))
colnames(coefs) <- coef.names
for (b in 1:R){
# setTkProgressBar(pb, b, label=sprintf("Bootstrap sample: %d", b))
if (show.progress) setTxtProgressBar(pb, b)
for (try in 1:(max.failures + 1)){
if (try > max.failures) stop("more than ", max.failures, " consecutive convergence failures")
res <- try(refit(), silent=TRUE)
if (inherits(res, "try-error")) nErrors <- nErrors + 1
else {
coefs[b,] <- res
break()
}
}
}
options(warn)
if (nErrors > 0) warning("there were", nErrors,
"apparent convergence failures;\nthese are discarded from the",
R, "bootstrap replications returned")
res <- list(t0=coefficients, t=coefs, R=R, data=data, seed=seed,
statistic=refit, sim="ordinary", stype="i", call=match.call(),
strata=rep(1, N), weights=rep(1/N, N))
res$call[[1]] <- as.name("bootSem")
if (show.progress) close(pb)
class(res) <- c("bootsem", "boot")
res
}
bootSem.msem <- function(model, R=100, Cov=cov, data=model$data, max.failures=10, show.progress=TRUE, ...){
refit <- function(){
for (g in 1:G){
indices <- sample(N[g], N[g], replace=TRUE)
S[[g]] <- Cov(data[[g]][indices, ])
}
refitted.model <- sem(ram, S, N, param.names=coef.names, var.names=var.names,
optimizer=model$optimizer, objective=model$objective, fixed.x=model$fixed.x, ...)
refitted.model$coeff
}
# if (!require("boot")) stop("package boot not available")
# has.tcltk <- require("tcltk")
# pb <- tkProgressBar("Bootstrap Sampling", "Bootstrap sample: ", 0, R)
if (show.progress){
cat("\n", R, "bootstrap replications\n")
pb <- txtProgressBar(min=0, max=R, style=3)
}
# the following 2 lines borrowed from boot in package boot
if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) runif(1)
seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
warn <- options(warn=-2)
on.exit(options(warn)) # insure restore even in event of error
nErrors <- 0
if (is.null(data)) stop("the model object doesn't contain data matrices")
N <- sapply(data, nrow)
coefficients <- model$coeff
coef.names <- names(coefficients)
var.names <- model$var.names
ram <- model$ram
groups <- model$groups
group <- model$group
G <- length(groups)
S <- vector(G, mode="list")
for (g in 1:G){
pars <- ram[[g]][, "parameter"]
free <- pars != 0
ram[[g]][free, "start value"] <- coefficients[pars[free]]
}
coefs <- matrix(numeric(0), R, length(coefficients))
colnames(coefs) <- coef.names
for (b in 1:R){
# setTkProgressBar(pb, b, label=sprintf("Bootstrap sample: %d", b))
if (show.progress) setTxtProgressBar(pb, b)
for (try in 1:(max.failures + 1)){
if (try > max.failures) stop("more than ", max.failures, " consecutive convergence failures")
res <- try(refit(), silent=TRUE)
if (inherits(res, "try-error")) nErrors <- nErrors + 1
else {
coefs[b,] <- res
break()
}
}
}
options(warn)
if (nErrors > 0) warning("there were", nErrors,
"apparent convergence failures;\nthese are discarded from the",
R, "bootstrap replications returned")
res <- list(t0=coefficients, t=coefs, R=R, data=data, seed=seed,
statistic=refit, sim="ordinary", stype="i", call=match.call(),
strata=rep(1:G, N), weights=rep(1/N, N))
res$call[[1]] <- as.name("bootSem")
if (show.progress) close(pb)
class(res) <- c("bootsem", "boot")
res
}
print.bootsem <- function(x, digits=getOption("digits"), ...){
t <- x$t
t0 <- x$t0
result <- data.frame("Estimate"=t0, "Bias"=colMeans(t) - t0,
"Std.Error"=apply(t, 2, sd))
rownames(result) <- names(t0)
cat("Call: ")
dput(x$call)
cat("\n")
print(result, digits=digits)
invisible(x)
}
summary.bootsem <- function(object,
type=c("perc", "bca", "norm", "basic", "none"), level=0.95, ...){
type <- match.arg(type)
t <- object$t
t0 <- object$t0
result <- data.frame("Estimate"=t0, "Bias"=colMeans(t) - t0,
"Std.Error"=apply(t, 2, sd))
if (type != "none"){
p <- length(t0)
lower <- upper <- rep(0, p)
low <- if (type == "norm") 2 else 4
up <- if (type == "norm") 3 else 5
for (i in 1:p){
ci <- as.vector(boot.ci(object, type=type, index=i,
conf=level)[[type, exact=FALSE]])
lower[i] <- ci[low]
upper[i] <- ci[up]
}
result$Lower <- lower
result$Upper <- upper
}
rownames(result) <- names(t0)
result <- list(table=result, call=object$call, level=level, type=type)
class(result) <- "summary.bootsem"
result
}
print.summary.bootsem <- function(x, digits=getOption("digits"), ...){
cat("Call: ")
dput(x$call)
cat("\n")
if (x$type != "none") {
cat(paste("Lower and upper limits are for the", 100*x$level,
"percent", x$type, "confidence interval\n\n"))
}
print(x$table, digits=digits)
invisible(return(x))
}
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