#' Individual parameters distributions
#'
#' Plots the distributions of the selected individual parameters.
#'
#' @inheritParams plot_continuous_covariates_distributions
#' @inheritParams summarize_parameters_correlations
#'
#' @return A ggplot2 object.
#' @export
#'
#' @examples
#' EXAMPLERUN %>% plot_parameters_distributions()
#' EXAMPLERUN %>% plot_parameters_distributions(type = "density")
#' EXAMPLERUN %>% plot_parameters_distributions(type = "boxplot")
#' EXAMPLERUN %>% plot_parameters_distributions(type = "qq")
#'
#' EXAMPLERUN %>% plot_parameters_distributions(parameters = "eta")
#'
#' EXAMPLERUN %>%
#' plot_parameters_distributions(parameters = "ETCL",
#' histogram_empirical_density = TRUE,
#' histogram_reference_distribution = list(fun = dnorm,
#' args = list(mean = 0, sd = 1)))
plot_parameters_distributions <- function(run, parameters = NULL,
baseline_only = TRUE, type = "histogram", histogram_bins = 30L,
histogram_empirical_density = FALSE,
histogram_reference_distribution = list(fun = dnorm, args = list(mean = 0, sd = 1)),
qq_reference_distribution = list(fun = qnorm, args = list(mean = 0, sd = 1)),
boxplot_facets = TRUE, boxplot_drop_unused = FALSE,
facet_scales = "free", overlay_splits = TRUE, auto_legend = TRUE) {
indiv_parameters <- run$model$parameters %>% filter(type %in% c("eta", "individual") & !is.na(column))
if (is.null(parameters)) {
mixed_parameters <- indiv_parameters
parameters <- setNames(mixed_parameters$column, mixed_parameters$name)
} else if (length(parameters) == 1 && parameters %in% c("eta", "individual")) {
selected_parameters <- indiv_parameters %>% filter(type == parameters & !is.na(column))
parameters <- setNames(selected_parameters$column, selected_parameters$name)
} else {
parameters <- get_selected_parameters(indiv_parameters, parameters)
}
if (length(parameters) == 0) stop(simpleError("No parameter found."))
df <- run$tables$pmxploitab %>%
get_reduced_dataset(baseline_only = baseline_only)
if (nrow(df) == 0 & !is.null(attr(df, "filters"))) {
stop(simpleError("Data is empty after filtering."))
}
split_by <- NULL
if (!is.null(groups(df)) && length(groups(df)) > 0) {
split_by <- as.character(groups(df))
df <- ungroup(df)
}
if (!is.null(split_by)) {
split_cov <- subset(run$model$covariates, column %in% split_by | name %in% split_by)
if (nrow(split_cov) == 0) {
stop(simpleError(paste("Missing splitting column(s):", paste(split_by, collapse = ", "))))
}
split_cov <- split_cov %>%
mutate(matching_order = match(column, split_by)) %>%
mutate(matching_order = ifelse(is.na(matching_order), match(name, split_by), matching_order)) %>%
arrange(matching_order)
split_by <- setNames(split_cov$column, nm = split_cov$name)
}
cols <- c(as.character(parameters), split_by)
cols_names <- c(names(parameters), names(split_by))
if (!is.null(split_by)) {
for (i in seq_along(split_by)) {
current_split <- split_by[[i]]
if (current_split %in% colnames(df) & current_split %in% names(run$model$categorical_covariates_levels)) {
levels <- run$model$categorical_covariates_levels[[current_split]]
df[[current_split]] <- plyr::mapvalues(df[[current_split]],
from = levels,
to = names(levels)
)
}
}
}
df <- df %>%
select(ID, one_of(cols)) %>%
rename(!!!setNames(cols, cols_names))
if (!is.null(split_by)) {
g_df <- df %>% gather(parameter, value, -ID, -one_of(names(split_by)), factor_key = TRUE)
} else {
g_df <- df %>% gather(parameter, value, -ID, factor_key = TRUE)
}
if (!is.null(split_by)) {
wrap_formula <- (if (length(split_by) == 1) {
as.formula(~parameter)
} else {
safe_names <- names(split_by[-1]) %>% purrr::map(as.name)
as.formula(sprintf("parameter ~ %s", paste(safe_names, collapse = "+")))
})
}
corrected_split_names <- sprintf("`%s`", names(split_by))
if (type == "boxplot") {
# boxplot
if (!is.null(split_by)) {
if (!boxplot_facets) {
grouping_key <- paste(names(split_by), collapse = ".")
if(length(split_by) > 1){
g_df <- g_df %>%
mutate(interaction_group = interaction(!!!syms(names(split_by)))) %>%
rename(!!!setNames(nm = grouping_key, "interaction_group"))
}
g <- ggplot(g_df, aes_string(x = "parameter", y = "value", fill = as.name(grouping_key))) +
geom_boxplot()
} else {
g <- ggplot(g_df, aes_string(x = corrected_split_names[1], y = "value", fill = corrected_split_names[1])) +
geom_boxplot() +
facet_wrap(wrap_formula, scales = facet_scales)
}
} else {
g <- ggplot(g_df, aes(x = parameter, y = value)) +
geom_boxplot()
if (boxplot_facets) {
g <- g + facet_wrap(~parameter, scales = facet_scales)
}
}
} else if (type == "qq") {
qq_dist <- qq_reference_distribution
if (!is.null(split_by)) {
grps <- map(c("parameter", names(split_by)), as.name)
intsl <- g_df %>% group_by(!!!grps)
} else {
intsl <- g_df %>% group_by(parameter)
}
# compute theoretical qq int/slope
intsl <- intsl %>% summarise(
q25 = quantile(value, 0.25),
q75 = quantile(value, 0.75),
theo25 = do.call(qq_dist$fun,
args = c(
p = 0.25,
qq_dist$args
)
),
theo75 = do.call(qq_dist$fun,
args = c(
p = 0.75,
qq_dist$args
)
),
slope = (q25 - q75) / (theo25 - theo75),
int = q25 - slope * theo25, n = dplyr::n()
) # %>%
if (!is.null(split_by)) {
if (overlay_splits) {
g <- ggplot(g_df) +
geom_qq(aes_string(sample = "value", colour = corrected_split_names[1]), distribution = qq_dist$fun) +
geom_abline(data = intsl, aes_string(intercept = "int", slope = "slope", colour = corrected_split_names[1])) +
facet_wrap(wrap_formula, scales = facet_scales)
} else {
wrap_formula <- as.formula(sprintf("parameter ~ %s", paste(corrected_split_names[1], collapse = "+")))
g <- ggplot(g_df) +
geom_qq(aes(sample = value), distribution = qq_dist$fun) +
geom_abline(data = intsl, aes(intercept = int, slope = slope), colour = "blue") +
facet_wrap(wrap_formula, scales = facet_scales)
}
} else {
g <- ggplot(g_df) +
geom_qq(aes(sample = value), distribution = qq_dist$fun) +
geom_abline(data = intsl, aes(intercept = int, slope = slope), colour = "blue") +
facet_wrap(~parameter, scales = facet_scales)
}
# g <- g + geom_abline(linetype = "dashed", colour = "red")
} else {
dist_geom <- switch(type,
"histogram" = {
geom_histogram(bins = histogram_bins)
},
"density" = {
geom_density(alpha = 0.5)
}, {
stop(simpleError(paste("Unknow plot type:", type)))
}
)
if (!is.null(split_by)) {
if (overlay_splits) {
g <- ggplot(g_df, aes_string(x = "value", fill = corrected_split_names[1])) +
dist_geom +
facet_wrap(wrap_formula, scales = facet_scales)
} else {
wrap_formula <- as.formula(sprintf("parameter ~ %s", paste(corrected_split_names[1], collapse = "+")))
g <- ggplot(g_df, aes(x = value)) +
dist_geom +
facet_wrap(wrap_formula, scales = facet_scales)
}
} else {
g <- ggplot(g_df, aes(x = value)) +
dist_geom +
facet_wrap(~parameter, scales = facet_scales)
}
if (histogram_empirical_density) {
g$mapping$y <- as.name(quote(..density..))
g <- g + stat_density(geom = "line", mapping = aes(colour = "Empirical", linetype = "Empirical"))
if (!is.null(histogram_reference_distribution) &&
all(c("fun", "args") %in% names(histogram_reference_distribution))) {
g <- g + stat_function(aes(colour = "Reference", linetype = "Reference"),
inherit.aes = FALSE,
fun = histogram_reference_distribution$fun,
args = histogram_reference_distribution$args
)
}
g <- g + scale_colour_manual("Distributions",
values = c(
"Empirical" = getOption("pmxploit.parametersdistributionsplot.empiricaldistribution.colour"),
"Reference" = getOption("pmxploit.parametersdistributionsplot.referencedistribution.colour")
)
) +
scale_linetype_manual("Distributions",
values = c(
"Empirical" = getOption("pmxploit.parametersdistributionsplot.empiricaldistribution.linetype"),
"Reference" = getOption("pmxploit.parametersdistributionsplot.referencedistribution.linetype")
)
)
}
}
if (auto_legend) {
g <- g + labs(caption = str_c("Path: ", run$info$path))
if (!is.null(split_by)) {
if (type == "qq") {
g <- g + scale_colour_discrete(name = paste(names(split_by), collapse = "."))
} else {
g <- g + scale_fill_discrete(name = paste(names(split_by), collapse = "."))
if (type == "histogram") {
g <- g + labs(y = ifelse(histogram_empirical_density, "Density", "Count"))
}
if (type == "boxplot") {
g <- g + scale_x_discrete(drop = boxplot_drop_unused)
}
}
}
}
g
}
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