# Copyright (C) 2017 Institute for Defense Analyses
#
# This file is part of ciTools.
#
# ciTools 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.
#
# ciTools is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ciTools. If not, see <http://www.gnu.org/licenses/>.
#' Confidence Intervals for Generalized Linear Mixed Model Predictions
#'
#' This function is one of the methods for \code{add_ci}, and is
#' called automatically when \code{add_ci} is used on a \code{fit} of
#' class \code{glmerMod}.
#'
#' The default and recommended method is bootstrap. The bootstrap
#' method can handle many types of models and we find it to be
#' generally reliable and robust as it is built on the \code{bootMer}
#' function from \code{lme4}. This function is experimental.
#'
#' @param df A data frame of new data.
#' @param fit An object of class \code{glmerMod}.
#' @param alpha A real number between 0 and 1. Controls the confidence
#' level of the interval estimates.
#' @param names \code{NULL} or character vector of length two. If
#' \code{NULL}, confidence bounds automatically will be named by
#' \code{add_ci}, otherwise, the lower confidence bound will be
#' named \code{names[1]} and the upper confidence bound will be
#' named \code{names[2]}.
#' @param type A string. If \code{type == "boot"} then bootstrap
#' intervals are formed. If \code{type == "parametric"} then
#' parametric intervals are formed. Currently only bootstrap
#' intervals are supported.
#' @param yhatName \code{NULL} or a string. Name of the predictions
#' vector. If \code{NULL}, the predictions will be named
#' \code{pred}.
#' @param response A logical. The default is \code{TRUE}. If
#' \code{TRUE}, the confidence intervals will be determined for the
#' expected response; if \code{FALSE}, confidence intervals will be
#' made on the scale of the linear predictor.
#' @param includeRanef A logical. Default is \code{TRUE}. Set whether
#' the predictions and intervals should be made conditional on the
#' random effects. If \code{FALSE}, random effects will not be
#' included.
#' @param nSims A positive integer. Controls the number of bootstrap
#' replicates if \code{type = "boot"}.
#' @param ... Additional arguments.
#' @return A dataframe, \code{df}, with predicted values, upper and lower
#' confidence bounds attached.
#'
#' @seealso \code{\link{add_pi.glmerMod}} for prediction intervals
#' of \code{glmerMod} objects, \code{\link{add_probs.glmerMod}} for
#' conditional probabilities of \code{glmerMod} objects, and
#' \code{\link{add_quantile.glmerMod}} for response quantiles of
#' \code{glmerMod} objects.
#'
#' @references For general information about GLMMs
#' http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
#'
#' @details If \code{IncludeRanef} is False, random slopes and intercepts are set to 0. Unlike in
#' `lmer` fits, settings random effects to 0 does not mean they are marginalized out. Consider
#' generalized estimating equations if this is desired.
#'
#' @examples
#' n <- 300
#' x <- runif(n)
#' f <- factor(sample(1:5, size = n, replace = TRUE))
#' y <- rpois(n, lambda = exp(1 - 0.05 * x * as.numeric(f) + 2 * as.numeric(f)))
#' df <- data.frame(x = x, f = f, y = y)
#' fit <- lme4::glmer(y ~ (1+x|f), data=df, family = "poisson")
#'
#' \dontrun{add_ci(df, fit, names = c("lcb", "ucb"), nSims = 300)}
#'
#' @export
add_ci.glmerMod <- function(df, fit,
alpha = 0.05, names = NULL, yhatName = "pred",
response = TRUE,
type = "boot", includeRanef = TRUE,
nSims = 500, ...){
if (!is.null(fit@optinfo$conv$lme4$code))
warning ("Coverage probabilities may be inaccurate if the model failed to converge")
if(!is.null(attr(fit@pp$X, "msgRankdrop")))
warning("Model matrix is rank deficient!")
if (is.null(names)){
names[1] <- paste("LCB", alpha/2, sep = "")
names[2] <- paste("UCB", 1 - alpha/2, sep = "")
}
if ((names[1] %in% colnames(df))) {
warning ("These CIs may have already been appended to your dataframe. Overwriting.")
}
if (type == "boot") {
bootstrap_ci_glmermod(df, fit, alpha, names, includeRanef, nSims, yhatName, response)
} else if (type == "parametric") {
stop("Parametric intervals give incorrect coverage")
##parametric_ci_glmermod(df, fit, alpha, names, includeRanef, yhatName, response)
} else {
stop("Incorrect type specified!")
}
}
parametric_ci_glmermod <- function(df, fit, alpha, names, includeRanef, yhatName, response){
if (length(fit@cnms[[1]]) != 1)
stop("parametric confidence intervals are currently only implemented for random intercept models.")
seFixed <- get_prediction_se_mermod(df, fit)
ranef_name <- names(fit@cnms)[1] ## just one random effect for now
seRandom <- arm::se.ranef(fit)[[1]][,1]
seRandom_vec <- rep(NA, length(df[[ranef_name]]))
seRandom_df <- data.frame(
group = names(seRandom),
seRandom = seRandom
)
names(seRandom_df)[names(seRandom_df) == 'group'] <- ranef_name
seRandom_vec <- dplyr::left_join(df, seRandom_df, by = ranef_name)[["seRandom"]]
rdf <- get_resid_df_mermod(fit)
if (fit@resp$family$family %in% c("binomial", "poisson")){
crit_val <- qnorm(p = 1 - alpha/2, mean = 0, sd = 1)
} else {
crit_val <- qt(p = 1 - alpha/2, df = rdf)
}
inverselink <- fit@resp$family$linkinv
if(includeRanef) {
re.form <- NULL
seGlobal <- sqrt(seFixed^2 + seRandom_vec^2)
} else {
re.form <- NA
seGlobal <- seFixed
}
out <- predict(fit, df, re.form = re.form)
pred <- out
upr <- out + crit_val * seGlobal
lwr <- out - crit_val * seGlobal
if (response == TRUE){
pred <- inverselink(pred)
upr <- inverselink(upr)
lwr <- inverselink(lwr)
}
if(fit@resp$family$link %in% c("inverse", "1/mu^2")){
upr1 <- lwr
lwr <- upr
upr <- upr1
}
df[[yhatName]] <- pred
df[[names[1]]] <- lwr
df[[names[2]]] <- upr
data.frame(df)
}
ciTools_data <- new.env(parent = emptyenv())
bootstrap_ci_glmermod <- function(df, fit, alpha, names, includeRanef, nSims, yhatName, response) {
ciTools_data$df_temp <- df
if (includeRanef) {
rform <- NULL
if (response) {
my_pred <- my_pred_full_glmer_response
lvl <- "response"
} else {
my_pred <- my_pred_full_glmer_linear
lvl <- "link"
}
} else {
rform <- NA
if (response) {
my_pred <- my_pred_fixed_glmer_response
lvl <- "response"
} else {
my_pred <- my_pred_fixed_glmer_linear
lvl <- "link"
}
}
boot_obj <- lme4::bootMer(fit, my_pred, nsim=nSims, type="parametric", re.form = rform)
ci_out <- boot_quants(boot_obj, alpha)
df[[yhatName]] <- predict(fit, ciTools_data$df_temp, re.form = rform, type = lvl)
df[[names[1]]] <- ci_out$lwr
df[[names[2]]] <- ci_out$upr
data.frame(df)
}
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