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#' Plot method for check model for (non-)normality of residuals | ||
#' | ||
#' The `plot()` method for the `performance::check_residuals()` resp. | ||
#' `performance::simulate_residuals()` function. | ||
#' | ||
#' @param transform Function to transform the residuals. If `NULL` (default), | ||
#' no transformation is applied and uniformly distributed residuals are expected. | ||
#' See argument `quantileFuntion` in `?DHARMa:::residuals.DHARMa` for more details. | ||
#' | ||
#' @inheritParams plot.see_check_normality | ||
#' @inheritParams plot.see_check_model | ||
#' | ||
#' @return A ggplot2-object. | ||
#' | ||
#' @seealso See also the vignette about [`check_model()`](https://easystats.github.io/performance/articles/check_model.html). | ||
#' | ||
#' @examplesIf insight::check_if_installed("performance", "0.10.9.7") && require("glmmTMB") && require("qqplotr") && require("DHARMa") | ||
#' data(Salamanders, package = "glmmTMB") | ||
#' model <- glmmTMB::glmmTMB( | ||
#' count ~ mined + spp + (1 | site), | ||
#' family = poisson(), | ||
#' data = Salamanders | ||
#' ) | ||
#' simulated_residuals <- performance::simulate_residuals(model) | ||
#' plot(simulated_residuals) | ||
#' | ||
#' # or | ||
#' simulated_residuals <- performance::simulate_residuals(model) | ||
#' result <- performance::check_residuals(simulated_residuals) | ||
#' plot(result) | ||
#' | ||
#' @export | ||
plot.see_performance_simres <- function(x, | ||
size_line = 0.8, | ||
size_point = 1, | ||
alpha = 0.2, | ||
dot_alpha = 0.8, | ||
colors = c("#3aaf85", "#1b6ca8"), | ||
detrend = FALSE, | ||
transform = NULL, | ||
style = theme_lucid, | ||
...) { | ||
dp <- list(min = 0, max = 1, lower.tail = TRUE, log.p = FALSE) | ||
|
||
# need DHARMa to be installed | ||
insight::check_if_installed("DHARMa") | ||
|
||
# extract data, if from check_residuals | ||
if (inherits(x, "see_check_residuals")) { | ||
x <- attributes(x)$data | ||
} | ||
|
||
if (is.null(transform)) { | ||
res <- stats::residuals(x) | ||
} else { | ||
res <- stats::residuals(x, quantileFunction = transform) | ||
} | ||
|
||
# base plot information | ||
gg_init <- ggplot2::ggplot( | ||
data.frame(scaled_residuals = res), | ||
ggplot2::aes(sample = .data$scaled_residuals) | ||
) | ||
|
||
# when we have package qqplotr, we can add confidence bands | ||
if (requireNamespace("qqplotr", quietly = TRUE)) { | ||
qq_stuff <- list( | ||
qqplotr::stat_qq_band( | ||
distribution = "unif", | ||
dparams = list(min = 0, max = 1), | ||
alpha = alpha, | ||
detrend = detrend | ||
), | ||
qqplotr::stat_qq_line( | ||
distribution = "unif", | ||
dparams = dp, | ||
size = size_line, | ||
colour = colors[1], | ||
detrend = detrend | ||
), | ||
qqplotr::stat_qq_point( | ||
distribution = "unif", | ||
dparams = dp, | ||
size = size_point, | ||
alpha = dot_alpha, | ||
colour = colors[2], | ||
detrend = detrend | ||
) | ||
) | ||
if (detrend) { | ||
y_lab <- "Sample Quantile Deviations" | ||
} else { | ||
y_lab <- "Sample Quantiles" | ||
} | ||
} else { | ||
insight::format_alert("For confidence bands, please install `qqplotr`.") | ||
qq_stuff <- list( | ||
ggplot2::geom_qq( | ||
shape = 16, | ||
stroke = 0, | ||
distribution = stats::qunif, | ||
dparams = dp, | ||
size = size_point, | ||
colour = colors[2] | ||
), | ||
ggplot2::geom_qq_line( | ||
linewidth = size_line, | ||
colour = colors[1], | ||
na.rm = TRUE, | ||
distribution = stats::qunif, | ||
dparams = dp | ||
) | ||
) | ||
y_lab <- "Sample Quantiles" | ||
} | ||
|
||
gg_init + | ||
qq_stuff + | ||
ggplot2::labs( | ||
title = "Uniformity of Residuals", | ||
subtitle = "Dots should fall along the line", | ||
x = "Standard Uniform Distribution Quantiles", | ||
y = y_lab | ||
) + | ||
style( | ||
base_size = 10, | ||
plot.title.space = 3, | ||
axis.title.space = 5 | ||
) | ||
} | ||
|
||
#' @export | ||
plot.see_check_residuals <- plot.see_performance_simres |
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