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#' @title Check overdispersion of GL(M)M's
#' @name check_overdispersion
#'
#' @description `check_overdispersion()` checks generalized linear (mixed)
#' models for overdispersion.
#'
#' @param x Fitted model of class `merMod`, `glmmTMB`, `glm`, or `glm.nb`
#' (package **MASS**).
#' @param ... Currently not used.
#'
#' @return A list with results from the overdispersion test, like chi-squared
#' statistics, p-value or dispersion ratio.
#'
#' @details Overdispersion occurs when the observed variance is higher than the
#' variance of a theoretical model. For Poisson models, variance increases
#' with the mean and, therefore, variance usually (roughly) equals the mean
#' value. If the variance is much higher, the data are "overdispersed".
#'
#' @section Interpretation of the Dispersion Ratio:
#' If the dispersion ratio is close to one, a Poisson model fits well to the
#' data. Dispersion ratios larger than one indicate overdispersion, thus a
#' negative binomial model or similar might fit better to the data. A p-value <
#' .05 indicates overdispersion.
#'
#' @section Overdispersion in Poisson Models:
#' For Poisson models, the overdispersion test is based on the code from
#' _Gelman and Hill (2007), page 115_.
#'
#' @section Overdispersion in Mixed Models:
#' For `merMod`- and `glmmTMB`-objects, `check_overdispersion()`
#' is based on the code in the
#' [GLMM FAQ](http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html),
#' section *How can I deal with overdispersion in GLMMs?*. Note that this
#' function only returns an *approximate* estimate of an overdispersion
#' parameter, and is probably inaccurate for zero-inflated mixed models (fitted
#' with `glmmTMB`).
#'
#' @section How to fix Overdispersion:
#' Overdispersion can be fixed by either modeling the dispersion parameter, or
#' by choosing a different distributional family (like Quasi-Poisson, or
#' negative binomial, see _Gelman and Hill (2007), pages 115-116_).
#'
#' @family functions to check model assumptions and and assess model quality
#'
#' @references
#' - Bolker B et al. (2017):
#' [GLMM FAQ.](http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html)
#'
#' - Gelman, A., and Hill, J. (2007). Data analysis using regression and
#' multilevel/hierarchical models. Cambridge; New York: Cambridge University
#' Press.
#'
#' @examplesIf getRversion() >= "4.0.0" && require("glmmTMB", quietly = TRUE)
#'
#' library(glmmTMB)
#' data(Salamanders)
#' m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
#' check_overdispersion(m)
#'
#' m <- glmmTMB(
#' count ~ mined + spp + (1 | site),
#' family = poisson,
#' data = Salamanders
#' )
#' check_overdispersion(m)
#' @export
check_overdispersion <- function(x, ...) {
UseMethod("check_overdispersion")
}
# default -----------------------
#' @export
check_overdispersion.default <- function(x, ...) {
# check for valid input
.is_model_valid(x)
insight::format_error(
paste0("`check_overdisperion()` not yet implemented for models of class `", class(x)[1], "`.")
)
}
# Methods -----------------------------
#' @export
plot.check_overdisp <- function(x, ...) {
insight::check_if_installed(c("see", "graphics"), "for overdispersion plots")
obj_name <- attr(x, "object_name", exact = TRUE)
model <- NULL
if (!is.null(obj_name)) {
model <- .safe(get(obj_name, envir = parent.frame()))
if (is.null(model)) {
# second try, global env
model <- .safe(get(obj_name, envir = globalenv()))
}
}
if (!is.null(model)) {
x <- .diag_overdispersion(model)
class(x) <- c("see_check_overdisp", "data.frame")
attr(x, "colors") <- list(...)$colors
attr(x, "line_size") <- list(...)$size_line
attr(x, "overdisp_type") <- list(...)$plot_type
graphics::plot(x, ...)
}
}
#' @export
print.check_overdisp <- function(x, digits = 3, ...) {
orig_x <- x
x$dispersion_ratio <- sprintf("%.*f", digits, x$dispersion_ratio)
x$chisq_statistic <- sprintf("%.*f", digits, x$chisq_statistic)
x$p_value <- pval <- round(x$p_value, digits = digits)
if (x$p_value < 0.001) x$p_value <- "< 0.001"
maxlen <- max(
nchar(x$dispersion_ratio),
nchar(x$chisq_statistic),
nchar(x$p_value)
)
insight::print_color("# Overdispersion test\n\n", "blue")
cat(sprintf(" dispersion ratio = %s\n", format(x$dispersion_ratio, justify = "right", width = maxlen)))
cat(sprintf(" Pearson's Chi-Squared = %s\n", format(x$chisq_statistic, justify = "right", width = maxlen)))
cat(sprintf(" p-value = %s\n\n", format(x$p_value, justify = "right", width = maxlen)))
if (pval > 0.05) {
message("No overdispersion detected.")
} else {
message("Overdispersion detected.")
}
invisible(orig_x)
}
# Overdispersion for classical models -----------------------------
#' @export
check_overdispersion.glm <- function(x, verbose = TRUE, ...) {
# check if we have poisson
info <- insight::model_info(x)
if (!info$is_count && !info$is_binomial) {
insight::format_error(
"Overdispersion checks can only be used for models from Poisson families or binomial families with trials > 1."
)
}
# check for Bernoulli
if (info$is_bernoulli) {
insight::format_error("Overdispersion checks cannot be used for Bernoulli models.")
}
if (info$is_binomial) {
return(check_overdispersion.merMod(x, verbose = verbose, ...))
}
yhat <- stats::fitted(x)
n <- stats::nobs(x)
k <- length(insight::find_parameters(x, effects = "fixed", flatten = TRUE))
zi <- (insight::get_response(x, verbose = FALSE) - yhat) / sqrt(yhat)
chisq <- sum(zi^2)
ratio <- chisq / (n - k)
p.value <- stats::pchisq(chisq, df = n - k, lower.tail = FALSE)
out <- list(
chisq_statistic = chisq,
dispersion_ratio = ratio,
residual_df = n - k,
p_value = p.value
)
class(out) <- c("check_overdisp", "see_check_overdisp")
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(x))
out
}
#' @export
check_overdispersion.fixest <- check_overdispersion.glm
#' @export
check_overdispersion.fixest_multi <- function(x, verbose = TRUE, ...) {
lapply(x, check_overdispersion.fixest)
}
#' @export
check_overdispersion.glmx <- check_overdispersion.glm
# mfx models ------------------------------
#' @export
check_overdispersion.poissonmfx <- function(x, ...) {
check_overdispersion(x$fit, ...)
}
#' @export
check_overdispersion.poissonirr <- check_overdispersion.poissonmfx
#' @export
check_overdispersion.negbinirr <- check_overdispersion.poissonmfx
#' @export
check_overdispersion.negbinmfx <- check_overdispersion.poissonmfx
#' @export
check_overdispersion.model_fit <- check_overdispersion.poissonmfx
# Overdispersion for mixed models ---------------------------
#' @export
check_overdispersion.merMod <- function(x, verbose = TRUE, ...) {
# check if we have poisson or binomial
info <- insight::model_info(x)
if (!info$is_count && !info$is_binomial) {
insight::format_error(
"Overdispersion checks can only be used for models from Poisson families or binomial families with trials > 1."
)
}
# check for Bernoulli
if (info$is_bernoulli) {
insight::format_error("Overdispersion checks cannot be used for Bernoulli models.")
}
rdf <- stats::df.residual(x)
rp <- insight::get_residuals(x, type = "pearson")
if (insight::is_empty_object(rp)) {
Pearson.chisq <- NA
prat <- NA
pval <- NA
rp <- NA
if (isTRUE(verbose)) {
insight::format_alert(
"Cannot test for overdispersion, because pearson residuals are not implemented for models with zero-inflation or variable dispersion.",
"Only the visual inspection using `plot(check_overdispersion(model))` is possible."
)
}
} else {
Pearson.chisq <- sum(rp^2)
prat <- Pearson.chisq / rdf
pval <- stats::pchisq(Pearson.chisq, df = rdf, lower.tail = FALSE)
}
out <- list(
chisq_statistic = Pearson.chisq,
dispersion_ratio = prat,
residual_df = rdf,
p_value = pval
)
class(out) <- c("check_overdisp", "see_check_overdisp")
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(x))
out
}
#' @export
check_overdispersion.negbin <- check_overdispersion.merMod
#' @export
check_overdispersion.glmmTMB <- check_overdispersion.merMod
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