#' Fitting Hierarchical Latent Trait Models (for Binary Responses).
#'
#' \code{hltm} fits a hierarchical latent trait model in which both
#' the mean and the variance of the latent preference (ability parameter)
#' may depend on person-specific covariates (\code{x} and \code{z}).
#' Specifically, the mean is specified as a linear combination of \code{x}
#' and the log of the variance is specified as a linear combination of
#' \code{z}.
#'
#' @inheritParams hgrm
#'
#' @return An object of class \code{hltm}.
#' \item{coefficients}{A data frame of parameter estimates, standard errors,
#' z values and p values.}
#' \item{scores}{A data frame of EAP estimates of latent preferences and
#' their approximate standard errors.}
#' \item{vcov}{Variance-covariance matrix of parameter estimates.}
#' \item{log_Lik}{The log-likelihood value at convergence.}
#' \item{N}{Number of units.}
#' \item{J}{Number of items.}
#' \item{H}{A vector denoting the number of response categories for each item.}
#' \item{ylevels}{A list showing the levels of the factorized response categories.}
#' \item{p}{The number of predictors for the mean equation.}
#' \item{q}{The number of predictors for the variance equation.}
#' \item{control}{List of control values.}
#' \item{call}{The matched call.}
#' @references Zhou, Xiang. 2019. "\href{https://doi.org/10.1017/pan.2018.63}{Hierarchical Item Response Models for Analyzing Public Opinion.}" Political Analysis.
#' @importFrom rms lrm.fit
#' @importFrom pryr compose
#' @importFrom pryr partial
#' @import stats
#' @export
#' @examples
#' y <- nes_econ2008[, -(1:3)]
#' x <- model.matrix( ~ party * educ, nes_econ2008)
#' z <- model.matrix( ~ party, nes_econ2008)
#'
#' dichotomize <- function(x) findInterval(x, c(mean(x, na.rm = TRUE)))
#' y[] <- lapply(y, dichotomize)
#' nes_m1 <- hltm(y, x, z)
#' nes_m1
hltm <- function(y, x = NULL, z = NULL, constr = c("latent_scale", "items"),
beta_set = 1L, sign_set = TRUE, init = c("naive", "glm", "irt"),
control = list()) {
# match call
cl <- match.call()
# check y and convert y into data.frame if needed
if(missing(y)) stop("`y` must be provided.")
if ((!is.data.frame(y) && !is.matrix(y)) || ncol(y) == 1L)
stop("'y' must be either a data.frame or a matrix with at least two columns.")
if(is.matrix(y)) y <- as.data.frame(y)
# number of units and items
N <- nrow(y)
J <- ncol(y)
# convert each y_j into an integer vector
y[] <- lapply(y, factor, exclude = c(NA, NaN))
ylevels <- lapply(y, levels)
y[] <- lapply(y, function(x) as.integer(x) - 1)
if (!is.na(invalid <- match(TRUE, vapply(y, invalid_ltm, logical(1L)))))
stop(paste(names(y)[invalid], "is not a dichotomous variable"))
H <- vapply(y, max, double(1L), na.rm = TRUE) + 1
# check x and z (x and z should contain an intercept column)
x <- x %||% as.matrix(rep(1, N))
z <- z %||% as.matrix(rep(1, N))
if (!is.matrix(x)) stop("`x` must be a matrix.")
if (!is.matrix(z)) stop("`z` must be a matrix.")
if (nrow(x) != N || nrow(z) != N) stop("both 'x' and 'z' must have the same number of rows as 'y'")
p <- ncol(x)
q <- ncol(z)
colnames(x) <- colnames(x) %||% paste0("x", 1:p)
colnames(z) <- colnames(z) %||% paste0("x", 1:q)
# check beta_set and sign_set
stopifnot(beta_set %in% 1:J, is.logical(sign_set))
# check constraint
constr <- match.arg(constr)
init <- match.arg(init)
# control parameters
con <- list(max_iter = 150, max_iter2 = 15, eps = 1e-03, eps2 = 1e-03, K = 25, C = 4)
con[names(control)] <- control
# set environments for utility functions
environment(loglik_ltm) <- environment(theta_post_ltm) <- environment(dummy_fun_ltm) <- environment(tab2df_ltm) <- environment()
# GL points
K <- con[["K"]]
theta_ls <- con[["C"]] * GLpoints[[K]][["x"]]
qw_ls <- con[["C"]] * GLpoints[[K]][["w"]]
# imputation
y_imp <- y
if(anyNA(y)) y_imp[] <- lapply(y, impute)
# pca for initial values of theta_eap
theta_eap <- {
tmp <- princomp(y_imp, cor = TRUE)$scores[, 1]
(tmp - mean(tmp, na.rm = TRUE))/sd(tmp, na.rm = TRUE)
}
# initialization of alpha and beta parameters
if (init == "naive"){
alpha <- rep(0, J)
beta <- vapply(y, function(y) cov(y, theta_eap, use = "complete.obs")/var(theta_eap), double(1L))
} else if (init == "glm"){
pseudo_logit <- lapply(y_imp, function(y) glm.fit(cbind(1, theta_eap), y, family = binomial("logit"))[["coefficients"]])
beta <- vapply(pseudo_logit, function(x) x[2L], double(1L))
alpha <- vapply(pseudo_logit, function(x) x[1L], double(1L))
} else {
ltm_coefs <- ltm(y ~ z1)[["coefficients"]]
beta <- ltm_coefs[, 2, drop = TRUE]
alpha <- ltm_coefs[, 1, drop = TRUE]
}
# initial values of gamma and lambda
lm_opr <- tcrossprod(solve(crossprod(x)), x)
gamma <- lm_opr %*% theta_eap
lambda <- rep(0, q)
fitted_mean <- as.double(x %*% gamma)
fitted_var <- rep(1, N)
# EM algorithm
for (iter in seq(1, con[["max_iter"]])) {
# store previous parameters
alpha_prev <- alpha
beta_prev <- beta
gamma_prev <- gamma
lambda_prev <- lambda
# construct w_ik
posterior <- Map(theta_post_ltm, theta_ls, qw_ls)
w <- {
tmp <- matrix(unlist(posterior), N, K)
t(sweep(tmp, 1, rowSums(tmp), FUN = "/"))
}
# maximization
pseudo_tab <- lapply(y, dummy_fun_ltm)
pseudo_y <- lapply(pseudo_tab, tab2df_ltm, theta_ls = theta_ls)
pseudo_logit <- lapply(pseudo_y, function(df) glm.fit(cbind(1, df[["x"]]),
df[["y"]], weights = df[["wt"]], family = quasibinomial("logit"))[["coefficients"]])
beta <- vapply(pseudo_logit, function(x) x[2L], double(1L))
alpha <- vapply(pseudo_logit, function(x) x[1L], double(1L))
# EAP and VAP estimates of latent preferences
theta_eap <- t(theta_ls %*% w)
theta_vap <- t(theta_ls^2 %*% w) - theta_eap^2
# variance regression
gamma <- lm_opr %*% theta_eap
r2 <- (theta_eap - x %*% gamma)^2 + theta_vap
if (ncol(z)==1) lambda <- log(mean(r2)) else{
s2 <- glm.fit(x = z, y = r2, intercept = FALSE, family = Gamma(link = "log"))[["fitted.values"]]
loglik <- -0.5 * (log(s2) + r2/s2)
LL0 <- sum(loglik)
dLL <- 1
for (m in seq(1, con[["max_iter2"]])) {
gamma <- lm.wfit(x, theta_eap, w = 1/s2)[["coefficients"]]
r2 <- (theta_eap - x %*% gamma)^2 + theta_vap
var_reg <- glm.fit(x = z, y = r2, intercept = FALSE, family = Gamma(link = "log"))
s2 <- var_reg[["fitted.values"]]
loglik <- -0.5 * (log(s2) + r2/s2)
LL_temp <- sum(loglik)
dLL <- LL_temp - LL0
if (dLL < con[["eps2"]])
break
LL0 <- LL_temp
}
lambda <- var_reg[["coefficients"]]
}
# location constraint
tmp <- mean(x %*% gamma)
alpha <- unlist(Map(function(x, y) x + tmp * y, alpha, beta))
gamma[1L] <- gamma[1L] - tmp
# scale constraint
tmp <- mean(z %*% lambda)
gamma <- gamma/exp(tmp/2)
beta <- beta * exp(tmp/2)
lambda[1L] <- lambda[1L] - tmp
# direction contraint
if (sign_set == (beta[beta_set] < 0)) {
gamma <- -gamma
beta <- -beta
}
fitted_mean <- as.double(x %*% gamma)
fitted_var <- exp(as.double(z %*% lambda))
cat(".")
# check convergence
if (sqrt(mean((beta - beta_prev)^2)) < con[["eps"]]) {
cat("\n converged at iteration", iter, "\n")
break
} else if (iter == con[["max_iter"]]) {
stop("algorithm did not converge; try increasing max_iter.")
break
} else next
}
gamma <- setNames(as.double(gamma), paste("x", colnames(x), sep = ""))
lambda <- setNames(as.double(lambda), paste("z", colnames(z), sep = ""))
# inference
pik <- matrix(unlist(Map(partial(dnorm, x = theta_ls), mean = fitted_mean, sd = sqrt(fitted_var))),
N, K, byrow = TRUE) * matrix(qw_ls, N, K, byrow = TRUE)
Lijk <- lapply(theta_ls, function(theta_k) exp(loglik_ltm(alpha = alpha, beta = beta, rep(theta_k, N)))) # K-list
Lik <- vapply(Lijk, compose(exp, partial(rowSums, na.rm = TRUE), log), double(N))
Li <- rowSums(Lik * pik)
# log likelihood
log_Lik <- sum(log(Li))
# outer product of gradients
environment(dalpha_ltm) <- environment(sj_ab_ltm) <- environment(si_gamma) <- environment(si_lambda) <- environment()
dalpha <- dalpha_ltm(alpha, beta) # K*J matrix
s_ab <- unname(Reduce(cbind, lapply(1:J, sj_ab_ltm)))
s_gamma <- vapply(1:N, si_gamma, double(p))
s_lambda <- vapply(1:N, si_lambda, double(q))
s_all <- rbind(t(s_ab)[-c(1L, ncol(s_ab)), , drop = FALSE], s_gamma, s_lambda)
s_all[is.na(s_all)] <- 0
covmat <- tryCatch(solve(tcrossprod(s_all)),
error = function(e) {warning("The information matrix is singular; SE calculation failed.");
matrix(NA, nrow(s_all), nrow(s_all))})
se_all <- sqrt(diag(covmat))
# reorganize se_all
sH <- 2 * J
gamma_indices <- (sH - 1):(sH + p - 2)
lambda_indices <- (sH + p - 1):(sH + p + q - 2)
se_all <- c(NA, se_all[1:(sH-2)], NA, se_all[gamma_indices], se_all[lambda_indices])
# name se_all and covmat
names_ab <- paste(rep(names(alpha), each = 2), c("Diff", "Dscrmn"))
names(se_all) <- c(names_ab, names(gamma), names(lambda))
rownames(covmat) <- colnames(covmat) <- c(names_ab[-c(1L, length(names_ab))], names(gamma), names(lambda))
# item coefficients
coefs_item <- Map(function(a, b) c(Diff = a, Dscrmn = b), alpha, beta)
# all coefficients
coef_all <- c(unlist(coefs_item), gamma, lambda)
coefs <- data.frame(Estimate = coef_all, Std_Error = se_all, z_value = coef_all/se_all,
p_value = 2 * (1 - pnorm(abs(coef_all/se_all))))
rownames(coefs) <- names(se_all)
# item constraints
if (constr == "items"){
gamma0_prev <- gamma[1L]
# location constraint
alpha_sum <- sum(alpha)
beta_sum <- sum(beta)
c1 <- alpha_sum/beta_sum
gamma[1L] <- gamma[1L] + c1 # adjust gamma0
alpha <- unlist(Map(function(x, y) x - c1 * y, alpha, beta))
# scale constraint
c2 <- 2 * mean(log(abs(beta)))
gamma <- gamma * exp(c2/2)
lambda[1L] <- lambda[1L] + c2
beta <- beta / exp(c2/2)
# fitted means and variances
fitted_mean <- as.double(x %*% gamma)
fitted_var <- exp(as.double(z %*% lambda))
# theta_eap and theta_vap
theta_eap <- (theta_eap - gamma0_prev) * exp(c2/2) + gamma[1L]
theta_vap <- theta_vap * (exp(c2/2))^2
# covmat for new parameterization
tmp_fun <- function(d) {
mat <- diag(d)
mat[d, d] <- exp(-c2/2)
mat[1:(d-1), d] <- rep(-c1, d-1)
mat
}
A <- Reduce(Matrix::bdiag, lapply(H, tmp_fun))
A2 <- A[seq(2, nrow(A)-1), seq(2, ncol(A)-1)]
B <- Matrix::bdiag(exp(c2/2) * diag(p), diag(q))
C <- Matrix::bdiag(A2, B)
covmat <- C %*% Matrix::tcrossprod(covmat, C)
se_all <- sqrt(Matrix::diag(covmat))
# reorganize se_all
sH <- 2 * J
lambda_indices <- gamma_indices <- NULL
gamma_indices <- (sH - 1):(sH + p - 2)
lambda_indices <- (sH + p - 1):(sH + p + q - 2)
se_all <- c(NA, se_all[1:(sH-2)], NA, se_all[gamma_indices], se_all[lambda_indices])
# name se_all and covmat
names_ab <- paste(rep(names(alpha), each = 2), c("Diff", "Dscrmn"))
names(se_all) <- c(names_ab, names(gamma), names(lambda))
rownames(covmat) <- colnames(covmat) <- c(names_ab[-c(1L, length(names_ab))], names(gamma), names(lambda))
# item coefficients
coefs_item <- Map(function(a, b) c(Diff = a, Dscrmn = b), alpha, beta)
# all coefficients
coef_all <- c(unlist(coefs_item), gamma, lambda)
coefs <- data.frame(Estimate = coef_all, Std_Error = se_all, z_value = coef_all/se_all,
p_value = 2 * (1 - pnorm(abs(coef_all/se_all))))
rownames(coefs) <- names(se_all)
}
# ability parameter estimates
theta <- data.frame(post_mean = theta_eap, post_sd = sqrt(theta_vap),
prior_mean = fitted_mean, prior_sd = sqrt(fitted_var))
# output
out <- list(coefficients = coefs, scores = theta, vcov = covmat, log_Lik = log_Lik, constr = constr,
N = N, J = J, H = H, ylevels = ylevels, p = p, q = q, control = con, call = cl)
class(out) <- c("hltm", "hIRT")
out
}
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