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# last modified 2015-06-09 by J. Fox
# some changes by Benjamin K Goodrich 2015-01-20
miSem <- function(model, ...){
UseMethod("miSem")
}
miSem.semmod <- function(model, ..., data, formula = ~., raw=FALSE, fixed.x=NULL, objective=objectiveML,
n.imp=5, n.chains=n.imp, n.iter=30, seed=sample(1e6, 1), mi.args=list(), show.progress=TRUE){
cls <- gsub("\\.", "", deparse(substitute(objective)))
cls <- gsub("2", "", cls)
cls <- c(cls, "sem")
warn <- options(warn=-1)
on.exit(options(warn))
initial.fit <- sem(model, ..., data=data, formula=formula, raw=raw, fixed.x=fixed.x,
objective = if (raw) objectiveFIML else objective)
options(warn)
class(initial.fit) <- if (raw) c("objectiveFIML", "sem") else cls
coefficients <- coefficients(initial.fit)
coef.names <- names(coefficients)
var.names <- initial.fit$var.names
ram <- initial.fit$ram
ram[coef.names, "start value"] <- coefficients
N <- nrow(data)
if (!is.null(fixed.x)) fixed.x <- apply(outer(var.names, fixed.x, "=="), 2, which)
mi.args$n.chains <- n.chains
mi.args$n.iter <- n.iter
mi.args$seed <- seed
mi.args$y <- data
if (show.progress) cat("\n Beginning", n.imp, "imputations\n")
mi.data <- do.call("mi", mi.args)
if (show.progress) cat("\n Imputations complete\n")
# has.tcltk <- require("tcltk")
# if (has.tcltk) pb <- tkProgressBar("Fitting", "Imputation no.: ", 0, n.imp)
if (show.progress) {
cat("\n Fitting model to imputations:\n")
pb <- txtProgressBar(min=0, max=n.imp, style=3)
}
fits <- complete(mi.data, m = n.imp, include_missing = FALSE)
for (i in seq_along(fits)) {
# if (has.tcltk) setTkProgressBar(pb, i, label=sprintf("Imputation no.: %d", i))
if (show.progress) setTxtProgressBar(pb, i)
data.i <- model.frame(formula, data=fits[[i]])
data.i <- model.matrix(formula, data=fits[[i]])
colnames(data.i)[colnames(data.i) == "(Intercept)"] <- "Intercept"
S <- if (raw) rawMoments(data.i) else {
data.i <- data.i[, colnames(data.i) != "Intercept"]
cov(data.i)
}
fit <- sem(ram, S=S, N=N, data=data.i, raw=raw, param.names=coef.names, var.names=var.names, fixed.x=fixed.x,
optimizer=initial.fit$optimizer, objective=objective, ...)
class(fit) <- cls
fits[[i]] <- fit
}
# if (has.tcltk) close(pb)
if (show.progress) close(pb)
result <- list(initial.fit=initial.fit, mi.fits=fits, imputations=mi.data, seed=seed, mi.data=mi.data)
class(result) <- "miSem"
result
}
miSem.semmodList <- function(model, ..., data, formula = ~., group, raw=FALSE,
fixed.x=NULL, objective=msemObjectiveML,
n.imp=5, n.chains=n.imp, n.iter=30, seed=sample(1e6, 1), mi.args=list(),
show.progress=TRUE){
if (missing(formula)) formula <- as.formula(paste("~ . -", group))
warn <- options(warn=-1)
on.exit(options(warn))
initial.fit <- sem(model, ..., data=na.omit(data), formula=formula, group=group, raw=raw, fixed.x=fixed.x,
objective = objective)
options(warn)
coefficients <- coefficients(initial.fit)
coef.names <- names(coefficients)
var.names <- initial.fit$var.names
ram <- initial.fit$ram
groups <- initial.fit$groups
group <- initial.fit$group
G <- length(groups)
for (g in 1:G){
pars <- ram[[g]][, "parameter"]
free <- pars != 0
ram[[g]][free, "start value"] <- coefficients[pars[free]]
}
mi.args$n.chains <- n.chains
mi.args$n.iter <- n.iter
mi.args$seed <- seed
mi.args$y <- data
if (show.progress) cat("\n Beginning", n.imp, "imputations\n")
mi.data <- do.call("mi", mi.args)
if (show.progress) cat("\n Imputations complete\n")
fits <- complete(mi.data, m = n.imp, include_missing = FALSE)
# has.tcltk <- require("tcltk")
# if (has.tcltk) pb <- tkProgressBar("Fitting", "Imputation no.: ", 0, n.imp)
if (show.progress) {
cat("\n Fitting model to imputations:\n")
pb <- txtProgressBar(min=0, max=n.imp, style=3)
}
for (i in 1:n.imp){
# if (has.tcltk) setTkProgressBar(pb, i, label=sprintf("Imputation no.: %d", i))
if (show.progress) setTxtProgressBar(pb, i)
data.i <- fits[[i]]
group.i <- data.i[, group]
data.i <- model.frame(formula, data=data.i)
data.i <- model.matrix(formula, data=data.i)
colnames(data.i)[colnames(data.i) == "(Intercept)"] <- "Intercept"
S <- data.out <- vector(G, mode="list")
N <- numeric(G)
for (g in 1:G){
data.g <- data.i[group.i == groups[g], ]
data.out[[g]] <- data.g
N[g] <- nrow(data.g)
S[[g]] <- if (raw) rawMoments(data.g) else {
data.g <- data.g[, colnames(data.g) != "Intercept"]
cov(data.g)
}
}
fit <- sem(ram, S=S, N=N, group=group, groups=groups, raw=raw, data=data.out,
fixed.x=initial.fit$fixed.x, param.names=coef.names, var.names=var.names,
optimizer=initial.fit$optimizer, objective=objective, ...)
fits[[i]] <- fit
}
# if (has.tcltk) close(pb)
if (show.progress) close(pb)
result <- list(initial.fit=initial.fit, mi.fits=fits, imputations=mi.data, seed=seed, mi.data=mi.data)
class(result) <- "miSem"
result
}
print.miSem <- function(x, ...){
coefs <- sapply(x$mi.fits, coef)
vars <- sapply(x$mi.fits, function(x) diag(vcov(x)))
table <- matrix(0, NROW(coefs), 4)
table[, 1] <- rowMeans(coefs)
ses <- sqrt(rowMeans(vars) + apply(coefs, 1, var) * (1 + 1/NCOL(coefs)))
table[, 2] <- ses
table[, 3] <- table[, 1]/table[, 2]
table[, 4] <- 2*pnorm(abs(table[, 3]), lower.tail=FALSE)
rownames(table) <- rownames(coefs)
cat("\nCoefficients:\n")
colnames(table) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
printCoefmat(table, ...)
invisible(x)
}
summary.miSem <- function(object, digits=max(3, getOption("digits") - 2), ...){
coefs <- lapply(object$mi.fits, coef)
table <- do.call("cbind", coefs)
rownames(table) <- names(coefs[[1]])
table <- cbind(table, rowMeans(table), coef(object$initial.fit))
colnames(table) <- c(paste("Imputation", 1:length(coefs)), "Averaged", "Initial Fit")
result <- list(object=object, mi.results=table, digits=digits)
class(result) <- "summary.miSem"
result
}
print.summary.miSem <- function(x, ...){
cat("\nCoefficients by imputation:\n")
print(x$mi.results, digits=x$digits, ...)
print(x$object, digits=x$digits, ...)
invisible(x)
}
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