/
methods.R
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/
methods.R
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print.MixMod <- function (x, digits = max(4, getOption("digits") - 4), ...) {
cat("\nCall:\n", printCall(x$call), "\n\n", sep = "")
cat("\nModel:")
user_defined <- is.null(x$family)
cat("\n family:", if (user_defined) "user-defined" else x$family$family)
cat("\n link:", if (user_defined) "user-defined" else x$family$link, "\n")
cat("\nRandom effects covariance matrix:\n")
D <- x$D
ncz <- nrow(D)
diag.D <- all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
sds <- sqrt(diag(D))
if (ncz > 1) {
if (diag.D) {
dat <- data.frame("StdDev" = round(sds, digits), row.names = rownames(D))
} else {
corrs <- cov2cor(D)
corrs[upper.tri(corrs, TRUE)] <- 0
mat <- round(cbind(sds, corrs[, -ncz]), digits)
mat <- apply(mat, 2, sprintf, fmt = "% .4f")
mat[mat == mat[1, 2]] <- ""
mat[1, -1] <- abbreviate(colnames(mat)[-1], 6)
colnames(mat) <- c(colnames(mat)[1], rep("", ncz - 1))
dat <- data.frame(mat, check.rows = FALSE, check.names = FALSE)
names(dat) <- c("StdDev", "Corr", if (ncz > 2) rep(" ", ncz - 2) else NULL)
row.names(dat) <- dimnames(D)[[1]]
}
} else {
dat <- data.frame("StdDev" = sds, row.names = rownames(D),
check.rows = FALSE, check.names = FALSE)
}
print(dat)
cat("\nFixed effects:\n")
print(x$coefficients)
if (!is.null(x$gammas)) {
cat("\nZero-part coefficients:\n")
print(x$gammas)
}
if (!is.null(x$phis)) {
if (x$family$family %in% c("negative binomial", "zero-inflated negative binomial")) {
cat("\ndispersion parameter:\n", exp(x$phis), "\n")
} else if (x$family$family %in% c("hurdle log-normal", "censored normal")) {
cat("\nResidual std. dev.:\n", exp(x$phis), "\n")
} else {
cat("\nphi parameters:\n", x$phis, "\n")
}
}
cat("\nlog-Lik:", x$logLik)
cat("\n\n")
invisible(x)
}
vcov.MixMod <- function (object, parm = c("all", "fixed-effects", "var-cov","extra",
"zero_part"), sandwich = FALSE, ...) {
parm <- match.arg(parm)
V <- solve(object$Hessian)
if (sandwich) {
meat <- object$score_vect_contributions
ind <- !names(meat) %in% "score.D" & !sapply(meat, is.null)
meat[ind] <- lapply(meat[ind], rowsum, group = object$id[[1]], reorder = FALSE)
meat <- do.call('cbind', meat)
meat <- Reduce("+", lapply(split(meat, row(meat)), function (x) x %o% x))
V <- V %*% meat %*% V
}
if (parm == "all") {
return(V)
}
if (parm == "fixed-effects") {
n_betas <- length(object$coefficients)
return(V[seq_len(n_betas), seq_len(n_betas), drop = FALSE])
}
if (parm == "var-cov") {
D <- object$D
diag_D <- ncol(D) > 1 && all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
include <- if (diag_D) {
unconstr_D <- log(diag(D))
n_betas <- length(object$coefficients)
seq(n_betas + 1, n_betas + length(unconstr_D))
} else {
unconstr_D <- chol_transf(D)
n_betas <- length(object$coefficients)
seq(n_betas + 1, n_betas + length(unconstr_D))
}
return(V[include, include, drop = FALSE])
}
if (parm == "extra") {
if (is.null(object$phis)) {
stop("the model behind 'object' contains no extra (phis) parameters.\n")
} else {
ind_phis <- grep("phi_", colnames(V), fixed = TRUE)
return(V[ind_phis, ind_phis, drop = FALSE])
}
}
if (parm == "zero_part") {
if (is.null(object$gammas)) {
stop("the fitted model does not have an extra zero part.")
} else {
gammas <- object$gammas
ind_gammas <- grep("zi_", colnames(V), fixed = TRUE)
return(V[ind_gammas, ind_gammas, drop = FALSE])
}
}
}
logLik.MixMod <- function (object, ...) {
out <- object$logLik
attr(out, "df") <- nrow(object$Hessian)
attr(out, "nobs") <- length(unique(object$id[[1]]))
class(out) <- "logLik"
out
}
coef.MixMod <- function (object, sub_model = c("main", "zero_part"), ...) {
sub_model <- match.arg(sub_model)
b <- ranef(object)
RE_zi <- grep("zi_", colnames(b), fixed = TRUE)
if (sub_model == "main") {
betas <- fixef(object, sub_model = "main")
if (length(RE_zi))
b <- b[, -RE_zi, drop = FALSE]
out <- matrix(betas, nrow = nrow(b), ncol = length(betas), byrow = TRUE)
colnames(out) <- names(betas)
rownames(out) <- rownames(b)
out[, colnames(b)] <- out[, colnames(b)] + b
out
} else {
gammas <- fixef(object, sub_model = "zero_part")
if (length(RE_zi)) {
b <- b[, RE_zi, drop = FALSE]
colnames(b) <- gsub("zi_", "", colnames(b), fixed = TRUE)
out <- matrix(gammas, nrow = nrow(b), ncol = length(gammas), byrow = TRUE)
colnames(out) <- names(gammas)
rownames(out) <- rownames(b)
out[, colnames(b)] <- out[, colnames(b)] + b
out
} else {
gammas
}
}
}
fixef.MixMod <- function(object, sub_model = c("main", "zero_part"), ...) {
sub_model <- match.arg(sub_model)
if (sub_model == "main") {
object$coefficients
} else {
if (!is.null(object$gammas))
object$gammas
else
stop("the fitted model does not have an extra zero-part.")
}
}
ranef.MixMod <- function(object, post_vars = FALSE, ...) {
out <- object$post_modes
if (post_vars)
attr(out, "post_vars") <- object$post_vars
out
}
summary.MixMod <- function (object, sandwich = FALSE, ...) {
betas <- fixef(object)
n_betas <- length(betas)
V <- vcov(object, sandwich = sandwich)
var_betas <- V[seq_len(n_betas), seq_len(n_betas), drop = FALSE]
ses <- sqrt(diag(var_betas))
D <- object$D
n_D <- length(D[lower.tri(D, TRUE)])
coef_table <- cbind("Estimate" = betas, "Std.Err" = ses, "z-value" = betas / ses,
"p-value" = 2 * pnorm(abs(betas / ses), lower.tail = FALSE))
if (!is.null(object$gammas)) {
gammas <- object$gammas
ind_gammas <- grep("zi_", colnames(V), fixed = TRUE)
ses <- sqrt(diag(V[ind_gammas, ind_gammas, drop = FALSE]))
coef_table_zi <- cbind("Estimate" = gammas, "Std.Err" = ses, "z-value" = gammas / ses,
"p-value" = 2 * pnorm(abs(gammas / ses), lower.tail = FALSE))
}
out <- list(coef_table = coef_table,
coef_table_zi = if (!is.null(object$gammas)) coef_table_zi,D = D,
logLik = logLik(object),
AIC = AIC(object), BIC = BIC(object), call = object$call,
N = length(object$id[[1]]))
if (!is.null(object$phis)) {
phis <- object$phis
ind_phis <- grep("phi_", colnames(V), fixed = TRUE)
var_phis <- V[ind_phis, ind_phis, drop = FALSE]
out$phis_table <- cbind("Estimate" = phis, "Std.Err" = sqrt(diag(var_phis)))
}
out$control <- object$control
out$family <- object$family
out$converged <- object$converged
class(out) <- 'summary.MixMod'
out
}
print.summary.MixMod <- function (x, digits = max(4, getOption("digits") - 4), ...) {
cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"),
"\n\n", sep = "")
cat("Data Descriptives:")
cat("\nNumber of Observations:", x$N)
cat("\nNumber of Groups:", attr(x$logLik, 'n'), "\n")
cat("\nModel:")
user_defined <- is.null(x$family)
cat("\n family:", if (user_defined) "user-defined" else x$family$family)
cat("\n link:", if (user_defined) "user-defined" else x$family$link, "\n")
cat("\nFit statistics:\n")
model.sum <- data.frame(log.Lik = x$logLik, AIC = x$AIC, BIC = x$BIC, row.names = "")
print(model.sum)
cat("\nRandom effects covariance matrix:\n")
D <- x$D
ncz <- nrow(D)
diag.D <- all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
sds <- sqrt(diag(D))
if (ncz > 1) {
if (diag.D) {
dat <- data.frame("StdDev" = round(sds, digits), row.names = rownames(D))
} else {
corrs <- cov2cor(D)
corrs[upper.tri(corrs, TRUE)] <- 0
mat <- round(cbind(sds, corrs[, -ncz]), digits)
mat <- apply(mat, 2, sprintf, fmt = "% .4f")
mat[mat == mat[1, 2]] <- ""
mat[1, -1] <- abbreviate(colnames(mat)[-1], 6)
colnames(mat) <- c(colnames(mat)[1], rep("", ncz - 1))
dat <- data.frame(mat, check.rows = FALSE, check.names = FALSE)
names(dat) <- c("StdDev", "Corr", if (ncz > 2) rep(" ", ncz - 2) else NULL)
row.names(dat) <- dimnames(D)[[1]]
}
} else {
dat <- data.frame("StdDev" = sds, row.names = rownames(D),
check.rows = FALSE, check.names = FALSE)
}
print(dat)
cat("\nFixed effects:\n")
coef_table <- as.data.frame(x[["coef_table"]])
coef_table[1:3] <- lapply(coef_table[1:3], round, digits = digits)
coef_table[["p-value"]] <- format.pval(coef_table[["p-value"]], eps = 1e-04)
print(coef_table)
if (!is.null(x[["coef_table_zi"]])) {
cat("\nZero-part coefficients:\n")
coef_table <- as.data.frame(x[["coef_table_zi"]])
coef_table[1:3] <- lapply(coef_table[1:3], round, digits = digits)
coef_table[["p-value"]] <- format.pval(coef_table[["p-value"]], eps = 1e-04)
print(coef_table)
}
if (!is.null(x$phis_table)) {
if (NB <- x$family$family %in% c("negative binomial", "zero-inflated negative binomial",
"hurdle negative binomial")) {
cat("\nlog(dispersion) parameter:\n")
} else if (NB <- x$family$family %in% c("hurdle log-normal", "censored normal")) {
cat("\nlog(residual std. dev.):\n")
} else {
cat("\nphi parameters:\n")
}
phis_table <- as.data.frame(x$phis_table)
if (NB)
row.names(phis_table) <- " "
phis_table[] <- lapply(phis_table, round, digits = digits)
print(phis_table)
}
cat("\nIntegration:")
cat("\nmethod: adaptive Gauss-Hermite quadrature rule")
cat("\nquadrature points:", x$control$nAGQ)
cat("\n\nOptimization:")
methd <- if (x$control$iter_EM == 0) "quasi-Newton"
else if (isTRUE(attr(x$converged, "during_EM"))) "EM"
else "hybrid EM and quasi-Newton"
cat("\nmethod:", methd)
cat("\nconverged:", as.logical(x$converged), "\n")
invisible(x)
}
coef.summary.MixMod <- function (object, ...) {
object$coef_table
}
confint.MixMod <- function (object, parm = c("fixed-effects", "var-cov","extra",
"zero_part"),
level = 0.95, sandwich = FALSE, ...) {
parm <- match.arg(parm)
V <- vcov(object, sandwich = sandwich)
if (parm == "fixed-effects") {
betas <- fixef(object)
n_betas <- length(betas)
ses_betas <- sqrt(diag(V[seq_len(n_betas), seq_len(n_betas), drop = FALSE]))
out <- cbind(betas + qnorm((1 - level) / 2) * ses_betas, betas,
betas + qnorm((1 + level) / 2) * ses_betas)
} else if (parm == "var-cov") {
D <- object$D
diag_D <- ncol(D) > 1 && all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
if (diag_D) {
unconstr_D <- log(diag(D))
n_betas <- length(object$coefficients)
include <- seq(n_betas + 1, n_betas + length(unconstr_D))
ses_unconstr_D <- sqrt(diag(V[include, include, drop = FALSE]))
out <- cbind(unconstr_D + qnorm((1 - level) / 2) * ses_unconstr_D,
unconstr_D,
unconstr_D + qnorm((1 + level) / 2) * ses_unconstr_D)
out <- exp(out)
rownames(out) <- paste0("var.", rownames(D))
} else {
unconstr_D <- chol_transf(D)
n_betas <- length(object$coefficients)
include <- seq(n_betas + 1, n_betas + length(unconstr_D))
ses_unconstr_D <- sqrt(diag(V[include, include, drop = FALSE]))
out <- cbind(unconstr_D + qnorm((1 - level) / 2) * ses_unconstr_D,
unconstr_D,
unconstr_D + qnorm((1 + level) / 2) * ses_unconstr_D)
ind <- lower.tri(D, TRUE)
out[, 1] <- chol_transf(out[, 1])[ind]
out[, 2] <- chol_transf(out[, 2])[ind]
out[, 3] <- chol_transf(out[, 3])[ind]
nams <- rownames(D)
rownames(out) <- apply(which(ind, arr.ind = TRUE), 1, function (k) {
if (k[1L] == k[2L]) paste0("var.", nams[k[1]]) else {
paste0("cov.", abbreviate(nams[k[2]], 5), "_", abbreviate(nams[k[1]], 5))
}
})
}
} else if (parm == "extra") {
if (is.null(object$phis)) {
stop("the model behind 'object' contains no extra (phis) parameters.\n")
} else {
phis <- object$phis
ind_phis <- grep("phi_", colnames(V), fixed = TRUE)
ses_phis <- sqrt(diag(V[ind_phis, ind_phis, drop = FALSE]))
out <- cbind(phis + qnorm((1 - level) / 2) * ses_phis, phis,
phis + qnorm((1 + level) / 2) * ses_phis)
if (object$family$family %in% c("negative binomial",
"zero-inflated negative binomial"))
out <- exp(out)
}
} else {
if (is.null(object$gammas)) {
stop("the fitted model does not have an extra zero part.")
} else {
gammas <- object$gammas
ind_gammas <- grep("zi_", colnames(V), fixed = TRUE)
ses_gammas <- sqrt(diag(V[ind_gammas, ind_gammas, drop = FALSE]))
out <- cbind(gammas + qnorm((1 - level) / 2) * ses_gammas, gammas,
gammas + qnorm((1 + level) / 2) * ses_gammas)
}
}
colnames(out) <- c(paste(round(100 * c((1 - level) / 2,
(1 + level) / 2), 1), "%"), "Estimate")[c(1,3,2)]
out
}
anova.MixMod <- function (object, object2, test = TRUE, L = NULL,
sandwich = FALSE, ...) {
if (missing(object2) && is.null(L))
stop("either argument 'object2' or argument 'L' needs to be specified.\n")
if (!missing(object2)) {
if (!object$converged)
warning("it seems that '", deparse(substitute(object)),
"' has not converged.\n")
if (!object2$converged)
warning("it seems that '", deparse(substitute(object2)),
"' has not converged.")
L0 <- logLik(object)
L1 <- logLik(object2)
nb0 <- attr(L0, "df")
nb1 <- attr(L1, "df")
df <- abs(nb1 - nb0)
if (test && df == 0) {
test <- FALSE
warning("the two objects represent models with the same number of parameters;",
" argument 'test' is set to FALSE.")
}
fam <- object$family$family
fam2 <- object2$family$family
if (test && (fam != fam2 && ((fam != "poisson" & fam2 != "negative binomial") &&
(fam != "zero-inflated poisson" & fam2 != "zero-inflated negative binomial")))) {
warning("it seems that the two objects represent model with different families;",
" are the models nested? If not, you should set 'test' to FALSE.")
}
out <- list(nam0 = deparse(substitute(object)), L0 = L0,
aic0 = AIC(object), bic0 = BIC(object),
nam1 = deparse(substitute(object2)), L1 = L1, aic1 = AIC(object2),
bic1 = BIC(object2), df = df, test = test)
if (test) {
LRT <- abs(- 2 * (L0 - L1))
attributes(LRT) <- NULL
out$LRT <- LRT
out$p.value <- pchisq(LRT, df, lower.tail = FALSE)
}
} else {
betas <- fixef(object)
n_betas <- length(betas)
if (!is.numeric(L) || ncol(L) != n_betas)
stop("L matrix not of appropriate dimensions. ",
"It should have ", n_betas, " columns.\n")
colnames(L) <- abbreviate(names(betas), 6)
rownames(L) <- rep("", nrow(L))
V <- vcov(object, sandwich = sandwich)
var_betas <- V[seq_len(n_betas), seq_len(n_betas)]
Lbetas <- c(L %*% betas)
LVtL <- L %*% tcrossprod(var_betas, L)
stat <- c(crossprod(Lbetas, solve(LVtL, Lbetas)))
pval <- pchisq(stat, nrow(L), lower.tail = FALSE)
res <- data.frame(Chisq = stat, df = nrow(L),
"Pr(>|Chi|)" = pval, check.names = FALSE, row.names = " ")
out <- list(aovTab.L = res, L = L)
}
class(out) <- "aov.MixMod"
out
}
print.aov.MixMod <- function (x, ...) {
if (is.null(x$L0)) {
f <- function (dat) {
dat[] <- lapply(dat, function (x)
round(unlist(x), 4))
dat$'Pr(>|Chi|)' <- format.pval(dat$'Pr(>|Chi|)', eps = 1e-04)
dat
}
cat("\nMarginal Wald Tests Table\n")
if (!is.null(x$aovTab.L)) {
cat("\nUser-defined contrasts matrix:\n")
print(x$L)
cat("\n")
print(f(x$aovTab.L))
}
cat("\n")
} else {
dat <- if (x$test) {
p.val <- round(x$p.value, 4)
p.val <- if (p.val < 0.0001) "<0.0001" else p.val
data.frame(AIC = round(c(x$aic0, x$aic1), 2),
BIC = round(c(x$bic0, x$bic1), 2),
log.Lik = round(c(x$L0, x$L1), 2),
LRT = c(" ", round(x$LRT, 2)), df = c("", x$df),
p.value = c("", p.val), row.names = c(x$nam0, x$nam1))
} else {
data.frame(AIC = round(c(x$aic0, x$aic1), 2),
BIC = round(c(x$bic0, x$bic1), 2),
log.Lik = round(c(x$L0, x$L1), 2), df = c("", x$df),
row.names = c(x$nam0, x$nam1))
}
cat("\n")
print(dat)
cat("\n")
}
invisible(x)
}
fitted.MixMod <- function (object, type = c("mean_subject", "subject_specific", "marginal"),
link_fun = NULL, ...) {
type <- match.arg(type)
X <- model.matrix(object$Terms$termsX, object$model_frames$mfX)
if (type == "mean_subject") {
betas <- fixef(object)
eta <- c(X %*% betas)
} else if (type == "subject_specific") {
betas <- fixef(object)
b <- ranef(object)
RE_zi <- grep("zi_", colnames(b), fixed = TRUE)
if (length(RE_zi))
b <- b[, -RE_zi, drop = FALSE]
id <- match(object$id[[1L]], unique(object$id[[1L]]))
Z <- mapply(constructor_Z, object$Terms$termsZ, object$model_frames$mfZ,
MoreArgs = list(id = id), SIMPLIFY = FALSE)
Z <- do.call("cbind", Z)
eta <- c(X %*% betas) + rowSums(Z * b[id, , drop = FALSE])
} else {
betas <- marginal_coefs(object, link_fun = link_fun)$betas
eta <- c(X %*% betas)
}
if (!is.null(object$offset))
eta <- eta + object$offset
mu <- object$Funs$mu_fun(eta)
if (!is.null(object$gammas)) {
X_zi <- model.matrix(object$Terms$termsX_zi, object$model_frames$mfX_zi)
offset_zi <- model.offset(object$model_frames$mfX_zi)
gammas <- fixef(object, "zero_part")
eta_zi <- c(X_zi %*% gammas)
if (type == "subject_specific" && !is.null(object$Terms$termsZ_zi)) {
b <- ranef(object)
RE_zi <- grep("zi_", colnames(b), fixed = TRUE)
if (length(RE_zi))
b <- b[, RE_zi, drop = FALSE]
id <- match(object$id[[1]], unique(object$id[[1]]))
Z_zi <- mapply(constructor_Z, object$Terms$termsZ_zi, object$model_frames$mfZ_zi,
MoreArgs = list (id = id), SIMPLIFY = FALSE)
Z_zi <- do.call("cbind", Z_zi)
eta_zi <- eta_zi + rowSums(Z_zi * b[id, , drop = FALSE])
}
if (!is.null(offset_zi))
eta_zi <- eta_zi + offset_zi
mu <- plogis(eta_zi, lower.tail = FALSE) * mu
}
names(mu) <- rownames(X)
mu
}
residuals.MixMod <- function (object, type = c("mean_subject", "subject_specific",
"marginal"), link_fun = NULL,
tasnf_y = function (x) x, ...) {
type <- match.arg(type)
fits <- fitted(object, type = type, link_fun = link_fun)
y <- model.response(object$model_frames$mfX)
if (is.factor(y)) {
y <- as.numeric(y != levels(y)[1L])
}
tasnf_y(y) - fits
}
marginal_coefs <- function (object, ...) UseMethod("marginal_coefs")
marginal_coefs.MixMod <- function (object, std_errors = FALSE, link_fun = NULL,
M = 3000L, K = 100L,
seed = 1L,
cores = max(parallel::detectCores() - 1, 1),
sandwich = FALSE, ...) {
offset <- object$offset
X <- model.matrix(object$Terms$termsX, object$model_frames$mfX)
id <- match(object$id[[1L]], unique(object$id[[1L]]))
Z <- mapply(constructor_Z, object$Terms$termsZ, object$model_frames$mfZ,
MoreArgs = list(id = id), SIMPLIFY = FALSE)
Z <- do.call("cbind", Z)
betas <- fixef(object)
D <- object$D
if (!is.null(object$gammas)) {
offset_zi <- model.offset(object$model_frames$mfX_zi)
X_zi <- model.matrix(object$Terms$termsX_zi, object$model_frames$mfX_zi)
if (!is.null(object$Terms$termsZ_zi)) {
Z_zi <- mapply(constructor_Z, object$Terms$termsZ_zi, object$model_frames$mfZ_zi,
MoreArgs = list (id = id), SIMPLIFY = FALSE)
Z_zi <- do.call("cbind", Z_zi)
} else {
Z_zi <- NULL
}
gammas <- fixef(object, "zero_part")
} else {
X_zi <- Z_zi <- gammas <- NULL
}
compute_marg_coefs <- function (object, X, betas, Z, X_zi, gammas, Z_zi, D, M,
link_fun, seed) {
if (!exists(".Random.seed", envir = .GlobalEnv))
runif(1L)
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
mu_fun <- object$Funs$mu_fun
if (is.null(link_fun)) {
link_fun <- object$family$linkfun
}
if (is.null(link_fun)) {
stop("you must specify the 'link_fun' argument.\n")
}
Xbetas <- c(X %*% betas)
if (!is.null(offset)) {
Xbetas <- Xbetas + offset
}
if (!is.null(gammas)) {
eta_zi <- c(X_zi %*% gammas)
if (!is.null(offset_zi)) {
eta_zi <- eta_zi + offset_zi
}
}
id <- match(object$id[[1]], unique(object$id[[1]]))
nRE <- ncol(D)
N <- nrow(X)
n <- length(unique(id))
eS <- eigen(D, symmetric = TRUE)
ev <- eS$values
V <- eS$vectors %*% diag(sqrt(pmax(ev, 0)), nRE)
marg_inv_mu <- numeric(N)
for (i in seq_len(n)) {
set.seed(seed + i)
id_i <- id == i
b <- V %*% matrix(rnorm(M * nRE), nRE, M)
Zb <- Z[id_i, , drop = FALSE] %*% b[seq_len(ncol(Z)), , drop = FALSE]
mu <- mu_fun(Xbetas[id_i] + Zb)
if (!is.null(gammas)) {
eta_zi_id_i <- eta_zi[id_i]
if (!is.null(object$Terms$termsZ_zi)) {
eta_zi_id_i <- eta_zi_id_i + Z_zi[id_i, , drop = FALSE] %*%
b[-seq_len(ncol(Z)), , drop = FALSE]
}
mu <- plogis(eta_zi_id_i, lower.tail = FALSE) * mu
}
marg_inv_mu[id_i] <- link_fun(rowMeans(mu))
}
res <- c(solve(crossprod(X), crossprod(X, marg_inv_mu)))
names(res) <- names(betas)
res
}
out <- list(betas = compute_marg_coefs(object, X, betas, Z, X_zi, gammas, Z_zi, D, M,
link_fun, seed))
if (std_errors) {
blocks <- split(seq_len(K), rep(seq_len(cores), each = ceiling(K / cores),
length.out = K))
D <- object$D
diag_D <- ncol(D) > 1 && all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
list_thetas <- list(betas = betas, D = if (diag_D) log(diag(D)) else chol_transf(D))
if (!is.null(object$phis)) {
list_thetas <- c(list_thetas, list(phis = object$phis))
}
if (!is.null(gammas)) {
list_thetas <- c(list_thetas, list(gammas = gammas))
}
tht <- unlist(as.relistable(list_thetas))
V <- vcov(object, sandwich = sandwich)
cluster_compute_marg_coefs <- function (block, tht, list_thetas, V, XX, Z, X_zi,
Z_zi, M, compute_marg_coefs, chol_transf,
object, link_fun, seed) {
if (!exists(".Random.seed", envir = .GlobalEnv))
runif(1L)
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
n_block <- length(block)
m_betas <- matrix(0.0, n_block, length(list_thetas[["betas"]]))
for (b in seq_along(block)) {
seed. <- seed + block[b]
set.seed(seed.)
new_tht <- relist(MASS::mvrnorm(1, tht, V), skeleton = list_thetas)
new_betas <- new_tht$betas
new_D <- if (diag_D) diag(exp(new_tht$D), length(new_tht$D)) else chol_transf(new_tht$D)
new_gammas <- new_tht$gammas
m_betas[b, ] <- compute_marg_coefs(object, XX, new_betas, Z, X_zi,
new_gammas, Z_zi, new_D, M, link_fun,
seed = seed.)
}
m_betas
}
if (cores > 1L) {
cl <- parallel::makeCluster(cores)
parallel::clusterSetRNGStream(cl = cl, iseed = seed)
res <- parallel::parLapply(cl, blocks, cluster_compute_marg_coefs, tht = tht,
list_thetas = list_thetas, V = V, XX = X, Z = Z,
X_zi = X_zi, Z_zi = Z_zi, M = M,
object = object, compute_marg_coefs = compute_marg_coefs,
chol_transf = chol_transf, link_fun = link_fun, seed = seed)
parallel::stopCluster(cl)
} else {
res <- lapply(blocks, cluster_compute_marg_coefs, tht = tht,
list_thetas = list_thetas, V = V, XX = X, Z = Z,
X_zi = X_zi, Z_zi = Z_zi, M = M,
object = object, compute_marg_coefs = compute_marg_coefs,
chol_transf = chol_transf, link_fun = link_fun, seed = seed)
}
out$var_betas <- var(do.call("rbind", res))
dimnames(out$var_betas) <- list(names(out$betas), names(out$betas))
ses <- sqrt(diag(out$var_betas))
coef_table <- cbind("Estimate" = out$betas, "Std.Err" = ses,
"z-value" = out$betas / ses,
"p-value" = 2 * pnorm(abs(out$betas / ses), lower.tail = FALSE))
out$coef_table <- coef_table
}
class(out) <- "m_coefs"
out
}
print.m_coefs <- function (x, digits = max(4, getOption("digits") - 4), ...) {
if (is.null(x$coef_table)) {
print(round(x$betas, digits = digits))
} else {
coef_table <- as.data.frame(x$coef_table)
coef_table[1:3] <- lapply(coef_table[1:3], round, digits = digits)
coef_table[["p-value"]] <- format.pval(coef_table[["p-value"]], eps = 1e-04)
print(coef_table)
cat("\n")
}
invisible(x)
}
coef.m_coefs <- function (object, ...) {
object$betas
}
vcov.m_coefs <- function (object, ...) {
if (is.null(object$coef_table)) {
NULL
} else {
object$var_betas
}
}
effectPlotData <- function (object, newdata, level, ...) UseMethod("effectPlotData")
effectPlotData.MixMod <- function (object, newdata, level = 0.95, marginal = FALSE,
CR_cohort_varname = NULL, direction = NULL,
K = 200, seed = 1, sandwich = FALSE, ...) {
termsX <- delete.response(object$Terms$termsX)
mfX <- model.frame(termsX, newdata,
xlev = .getXlevels(termsX, object$model_frames$mfX))
X <- model.matrix(termsX, mfX)
if (is.null(object$gammas)) {
if (marginal) {
mcoefs <- marginal_coefs(object, std_errors = TRUE, seed = seed, ...)
betas <- mcoefs$betas
var_betas <- mcoefs$var_betas
} else {
betas <- fixef(object)
var_betas <- vcov(object, parm = "fixed-effects", sandwich = sandwich)
}
if (is.null(CR_cohort_varname)) {
pred <- c(X %*% betas)
ses <- sqrt(diag(X %*% var_betas %*% t(X)))
newdata$pred <- pred
newdata$low <- pred + qnorm((1 - level) / 2) * ses
newdata$upp <- pred + qnorm((1 + level) / 2) * ses
} else {
if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
runif(1L)
R.seed <- get(".Random.seed", envir = .GlobalEnv)
set.seed(seed)
RNGstate <- structure(seed, kind = as.list(RNGkind()))
on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
##
cohort_var <- newdata[[CR_cohort_varname]]
nlvs <- nlevels(cohort_var)
eta <- do.call("cbind", split(c(X %*% betas), cohort_var))
logit_marg_probs <- qlogis(cr_marg_probs(eta, direction = direction))
new_betas <- MASS::mvrnorm(K, betas, var_betas)
sim_marg_probs <- array(0.0, dim = c(dim(logit_marg_probs), K))
for (k in seq_len(K)) {
eta_k <- do.call("cbind", split(c(X %*% new_betas[k, ]), cohort_var))
sim_marg_probs[, , k] <- qlogis(cr_marg_probs(eta_k, direction = direction))
}
logit_marg_probs_low <- apply(sim_marg_probs, c(1, 2), quantile,
probs = (1 - level) / 2)
logit_marg_probs_upp <- apply(sim_marg_probs, c(1, 2), quantile,
probs = (1 + level) / 2)
newdata <- newdata[cohort_var == levels(cohort_var)[1L],
names(newdata) != CR_cohort_varname, drop = FALSE]
newdata <- do.call("rbind", rep(list(newdata), nlvs + 1)); row.names(newdata) <- NULL
newdata[["ordinal_response"]] <- rep(seq_len(nlvs + 1), each = nrow(newdata) / (nlvs + 1))
newdata$pred <- c(logit_marg_probs)
newdata$low <- c(logit_marg_probs_low)
newdata$upp <- c(logit_marg_probs_upp)
}
} else {
if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
runif(1)
R.seed <- get(".Random.seed", envir = .GlobalEnv)
set.seed(seed)
RNGstate <- structure(seed, kind = as.list(RNGkind()))
on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
mu_fun <- object$Funs$mu_fun
link_fun <- object$family$linkfun
betas <- fixef(object)
gammas <- fixef(object, sub_model = "zero_part")
termsX_zi <- object$Terms$termsX_zi
mfX_zi <- model.frame(termsX_zi, newdata,
xlev = .getXlevels(termsX_zi, object$model_frames$mfX_zi))
X_zi <- model.matrix(termsX_zi, mfX_zi)
list_thetas <- list(betas = betas, gammas = gammas)
tht <- unlist(as.relistable(list_thetas))
V <- vcov(object, sandwich = sandwich)
ind <- c(seq_len(length(betas)), grep("zi_", colnames(V), fixed = TRUE))
V <- V[ind, ind, drop = FALSE]
new_tht <- MASS::mvrnorm(K, tht, V)
if (marginal) {
mcoefs <- marginal_coefs(object, std_errors = TRUE, seed = seed,
...)
betas <- mcoefs$betas
var_betas <- mcoefs$var_betas
pred <- c(X %*% betas)
ses <- sqrt(diag(X %*% var_betas %*% t(X)))
newdata$pred <- pred
newdata$low <- pred + qnorm((1 - level) / 2) * ses
newdata$upp <- pred + qnorm((1 + level) / 2) * ses
} else {
eta_y <- c(X %*% betas)
eta_zi <- c(X_zi %*% gammas)
pred <- link_fun(mu_fun(eta_y) / (1 + exp(eta_zi)))
Preds <- matrix(0.0, length(pred), K)
for (k in seq_len(K)) {
thetas_k <- relist(new_tht[k, ], skeleton = list_thetas)
betas_k <- thetas_k$betas
gammas_k <- thetas_k$gammas
eta_y <- c(X %*% betas_k)
eta_zi <- c(X_zi %*% gammas_k)
Preds[, k] <- link_fun(mu_fun(eta_y) / (1 + exp(eta_zi)))
}
newdata$pred <- pred
Qs <- apply(Preds, 1, quantile, probs = c((1 - level) / 2, (1 + level) / 2))
newdata$low <- Qs[1, ]
newdata$upp <- Qs[2, ]
}
}
newdata
}
create_lists <- function (object, newdata) {
if (!inherits(object, "MixMod")) {
stop("only works for 'MixMod' objects.")
}
id_nam <- object$id_name
id <- newdata[[id_nam]]
id <- match(id, unique(id))
id_unq <- unique(id)
# terms & model frames
termsX <- object$Terms$termsX
mfX <- model.frame(termsX, newdata,
xlev = .getXlevels(termsX, object$model_frames$mfX))
termsZ <- object$Terms$termsZ
mfZ <- mapply(model.frame.default, formula = termsZ,
xlev = mapply(.getXlevels, termsZ, object$model_frames$mfZ, SIMPLIFY = FALSE),
MoreArgs = list(data = newdata), SIMPLIFY = FALSE)
if (!is.null(object$gammas)) {
termsX_zi <- delete.response(object$Terms$termsX_zi)
mfX_zi <- model.frame(termsX_zi, newdata,
xlev = .getXlevels(termsX_zi, object$model_frames$mfX_zi))
if (!is.null(object$Terms$termsZ_zi)) {
termsZ_zi <- object$Terms$termsZ_zi
mfZ_zi <- mapply(model.frame.default, formula = termsZ_zi,
xlev = mapply(.getXlevels, termsZ_zi, object$model_frames$mfZ_zi,
SIMPLIFY = FALSE),
MoreArgs = list(data = newdata), SIMPLIFY = FALSE)
}
}
# delete missing data
complete_cases <- cbind(complete.cases(mfX), sapply(mfZ, complete.cases))
if (!is.null(object$gammas))
complete_cases <- cbind(complete_cases, complete.cases(mfX_zi))
if (!is.null(object$Terms$termsZ_zi))
complete_cases <- cbind(complete_cases, complete.cases(mfZ_zi))
keep <- apply(complete_cases, 1, all)
mfX <- mfX[keep, , drop = FALSE]
mfZ[] <- lapply(mfZ, function (mf) mf[keep, , drop = FALSE])
if (!is.null(object$gammas))
mfX_zi <- mfX_zi[keep, , drop = FALSE]
if (!is.null(object$Terms$termsZ_zi))
mfZ_zi[] <- lapply(mfZ_zi, function (mf) mf[keep, , drop = FALSE])
# response vector and design matrices
y <- model.response(mfX)
if (is.null(y)) {
stop("the outcome variable does seem to exist in 'newdata'; do you perhaps want ",
"'mean_subject' predictions?\n")
}
if (is.factor(y)) {
y <- as.numeric(y != levels(y)[1L])
}
offset <- model.offset(mfX)
X <- model.matrix(termsX, mfX)
Z <- mapply(constructor_Z, termsZ, mfZ, MoreArgs = list(id = id), SIMPLIFY = FALSE)
Z <- do.call("cbind", Z)
if (!is.null(object$gammas)) {
offset_zi <- model.offset(mfX_zi)
X_zi <- model.matrix(termsX_zi, mfX_zi)
if (!is.null(object$Terms$termsZ_zi)) {
Z_zi <- mapply(constructor_Z, termsZ_zi, mfZ_zi, MoreArgs = list(id = id),
SIMPLIFY = FALSE)
Z_zi <- do.call("cbind", Z_zi)
} else {
Z_zi <- NULL
}
} else {
X_zi <- offset_zi <- Z_zi <- NULL
}
y_lis <- if (is.matrix(y)) lapply(id_unq, function (i) y[id == i, , drop = FALSE]) else split(y, id)
N <- if (NCOL(y) == 2) y[, 1] + y[, 2]
N_lis <- if (NCOL(y) == 2) split(N, id)
X_lis <- lapply(id_unq, function (i) X[id == i, , drop = FALSE])
Z_lis <- lapply(id_unq, function (i) Z[id == i, , drop = FALSE])
offset_lis <- if (!is.null(offset)) split(offset, id)
Zty_fun <- function (z, y) {
if (NCOL(y) == 2) crossprod(z, y[, 1]) else crossprod(z, y)
}
Zty_lis <- lapply(mapply(Zty_fun, Z_lis, y_lis, SIMPLIFY = FALSE), drop)
Xty <- drop(if (NCOL(y) == 2) crossprod(X, y[, 1]) else crossprod(X, y))
X_zi_lis <- if (!is.null(X_zi)) lapply(id_unq, function (i) X_zi[id == i, , drop = FALSE])
Z_zi_lis <- if (!is.null(Z_zi)) lapply(id_unq, function (i) Z_zi[id == i, , drop = FALSE])
offset_zi_lis <- if (!is.null(offset_zi)) split(offset_zi, id)
log_dens <- object$Funs$log_dens
mu_fun <- object$Funs$mu_fun
var_fun <- object$Funs$var_fun
mu.eta_fun <- object$Funs$mu.eta_fun
score_eta_fun <- object$Funs$score_eta_fun
score_phis_fun <- object$Funs$score_phis_fun
score_eta_zi_fun <- object$Funs$score_eta_zi_fun
family <- object$family
canonical <- !is.null(family$family) &&
((family$family == "binomial" && family$link == "logit") ||
(family$family == "poisson" && family$link == "log"))
known_families <- c("binomial", "poisson")
user_defined <- !family$family %in% known_families
numer_deriv <- if (object$control$numeric_deriv == "fd") fd else cd
numer_deriv_vec <- if (object$control$numeric_deriv == "fd") fd_vec else cd_vec
nRE <- if (!is.null(Z_zi)) ncol(Z) + ncol(Z_zi) else ncol(Z)
start <- matrix(0.0, length(y_lis), nRE)
betas <- fixef(object)
invD <- solve(object$D)
phis <- object$phis
gammas <- object$gammas
list(y_lis = y_lis, N_lis = N_lis, X_lis = X_lis, Z_lis = Z_lis, Z = Z, Z_zi = Z_zi,
X_zi_lis = X_zi_lis, Z_zi_lis = Z_zi_lis, offset_zi_lis = offset_zi_lis,
id = id, id_nam = id_nam, offset_lis = offset_lis, betas = betas, invD = invD,
phis = phis, gammas = gammas, start = start, canonical = canonical,
user_defined = user_defined, Zty_lis = Zty_lis, log_dens = log_dens,
mu_fun = mu_fun, var_fun = var_fun,
mu.eta_fun = mu.eta_fun, score_eta_fun = score_eta_fun, termsZ = termsZ,
score_phis_fun = score_phis_fun, score_eta_zi_fun = score_eta_zi_fun)
}
predict.MixMod <- function (object, newdata, newdata2 = NULL,
type_pred = c("response", "link"),
type = c("mean_subject", "subject_specific", "marginal", "zero_part"),
se.fit = FALSE, M = 300, df = 10, scale = 0.3, level = 0.95,
seed = 1, return_newdata = FALSE, sandwich = FALSE, ...) {
type_pred <- match.arg(type_pred)
type <- match.arg(type)
if (!is.null(object$gammas) && type != "zero_part" && type_pred == "link") {
warning("for model with an extra zero-part only predictions at the level of the ",
"response variable are returned;\n'type_pred' is set to 'response'.")
type_pred <- "response"
}
if (missing(newdata)) {
newdata <- object$model_frames$mfX
combine <- function (mf, newdata) {
mf <- object$model_frames$mfX_zi
cond <- length(mf) >= 1 && any(ind <- !names(mf) %in% names(newdata))
if (cond) cbind(newdata, mf[ind]) else newdata
}
newdata <- combine(object$model_frames$mfZ, newdata)
newdata <- combine(object$model_frames$mfX_zi, newdata)
newdata <- combine(object$model_frames$mfZ_zi, newdata)
newdata[[object$id_name]] <- object[["id"]]
}
termsX <- delete.response(object$Terms$termsX)
mfX <- model.frame(termsX, newdata,
xlev = .getXlevels(termsX, object$model_frames$mfX))
X <- model.matrix(termsX, mfX)
offset <- model.offset(mfX)
if (!is.null(object$gammas)) {
termsX_zi <- delete.response(object$Terms$termsX_zi)
mfX_zi <- model.frame(termsX_zi, newdata,
xlev = .getXlevels(termsX_zi, object$model_frames$mfX_zi))
X_zi <- model.matrix(termsX_zi, mfX_zi)
offset_zi <- model.offset(mfX_zi)
gammas <- fixef(object, sub_model = "zero_part")
eta_zi <- c(X_zi %*% gammas)
if (!is.null(offset_zi)) {
eta_zi <- eta_zi + offset_zi
}
}
if (type %in% c("mean_subject", "marginal")) {
if (type == "mean_subject") {
betas <- fixef(object)
var_betas <- vcov(object, parm = "fixed-effects", sandwich = sandwich)
eta_y <- c(X %*% betas)
if (!is.null(offset)) {
eta_y <- eta_y + offset
}
pred <- if (type_pred == "link") eta_y else object$family$linkinv(eta_y)
if (!is.null(object$gammas)) {
if (object$family$family == "hurdle log-normal") {
pred <- exp(pred + 0.5 * exp(object$phis)^2)
}
pred <- plogis(eta_zi, lower.tail = FALSE) * pred
}
names(pred) <- row.names(newdata)
se_fit <- if (se.fit && is.null(object$gammas)) sqrt(diag(X %*% var_betas %*% t(X)))
} else {
if (!is.null(object$gammas)) {
stop("the predict() method is not yet implemented for models with an extra zero-part.")
}
mcoefs <- marginal_coefs(object, std_errors = TRUE, ...)
betas <- coef(mcoefs)
var_betas <- mcoefs$var_betas
pred <- if (type_pred == "link") c(X %*% betas) else object$family$linkinv(c(X %*% betas))
names(pred) <- row.names(newdata)
se_fit <- if (se.fit) sqrt(diag(X %*% var_betas %*% t(X)))
}
} else if (type == "zero_part") {
if (is.null(object$gammas))
stop("the fitted model does not have an extra zero part.")
pred <- if (type_pred == "link") eta_zi else plogis(eta_zi)
names(pred) <- row.names(newdata)
var_gammas <- vcov(object, parm = "zero_part", sandwich = sandwich)
se_fit <- if (se.fit) sqrt(diag(X_zi %*% var_gammas %*% t(X_zi)))
} else {
Lists <- create_lists(object, newdata)
id <- Lists[["id"]]
betas <- Lists[["betas"]]
gammas <- Lists[["gammas"]]
phis <- Lists[["phis"]]
Z <- Lists[["Z"]]
ncz <- ncol(Z)
Z_zi <- Lists[["Z_zi"]]
EBs <- find_modes(b = Lists$start, y_lis = Lists[["y_lis"]],
N_lis = Lists[["N_lis"]], X_lis = Lists[["X_lis"]],
Z_lis = Lists[["Z_lis"]], offset_lis = Lists[["offset_lis"]],
X_zi_lis = Lists[["X_zi_lis"]], Z_zi_lis = Lists[["Z_zi_lis"]],
offset_zi_lis = Lists[["offset_zi_lis"]],
betas = betas, invD = Lists[["invD"]], phis = Lists[["phis"]],
gammas = gammas, canonical = Lists[["canonical"]],
user_defined = Lists[["user_defined"]],
Zty_lis = Lists[["Zty_lis"]], log_dens = Lists[["log_dens"]],
mu_fun = Lists[["mu_fun"]], var_fun = Lists[["var_fun"]],
mu.eta_fun = Lists[["mu.eta_fun"]],
score_eta_fun = Lists[["score_eta_fun"]],
score_phis_fun = Lists[["score_phis_fun"]],
score_eta_zi_fun = Lists[["score_eta_zi_fun"]])
eta <- c(X %*% betas) + rowSums(Z * EBs$post_modes[id, seq_len(ncz), drop = FALSE])
if (!is.null(offset))
eta <- eta + offset
if (!is.null(object$Terms$termsZ_zi)) {