#' Plot parameters correlations
#'
#' Returns selected parameters correlations in the form of either a matrix, a
#' data frame or a plot.
#'
#' @param parameters character vector of parameters names. Default is \code{NULL},
#' returning all individual parameters (random and post-hoc).
#' @param type character. Type of plot. One of \code{heatmap} or \code{scatterplot}.
#' @param smoothing_method character. If \code{type = "scatterplot"}, corresponds
#' to a \code{ggplot2} smoothing method.
#' @param fixed_ratio logical. If \code{type = "heatmap"}, plot scaled to a
#' 1:1 ratio. Default is \code{TRUE}.
#'
#' @inheritParams summarize_covariates_correlations
#' @inheritParams plot_dv_vs_predictions
#'
#' @return A ggplot2 object.
#' @export
#'
#' @examples
#' EXAMPLERUN %>% plot_parameters_correlations()
#'
#' EXAMPLERUN %>% plot_parameters_correlations(type = "heatmap")
#' EXAMPLERUN %>% plot_parameters_correlations(type = "heatmap", auto_order = FALSE)
#'
#' EXAMPLERUN %>% plot_parameters_correlations(type = "scatterplot")
#' EXAMPLERUN %>% plot_parameters_correlations(type = "scatterplot", smoothing_method = "lm")
#' EXAMPLERUN %>% plot_parameters_correlations(type = "scatterplot", smoothing_method = "loess")
#' EXAMPLERUN %>% group_by(STUD) %>% plot_parameters_correlations(type = "scatterplot")
plot_parameters_correlations <-
function(run,
parameters = NULL,
type = "heatmap",
correlation_method = NULL,
baseline_only = TRUE,
auto_order = TRUE,
smoothing_method = NULL,
smoothing_se = TRUE,
fixed_ratio = TRUE,
auto_legend = TRUE) {
stopifnot(type %in% c("heatmap", "scatterplot"))
indiv_parameters <-
run$model$parameters %>%
filter(type %in% c("eta", "individual") &
!is.na(column))
if (is.null(parameters)) {
mixed_parameters <-
indiv_parameters %>%
filter(type == "individual" & !is.na(column))
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)
}
keep_cols <- c(parameters, split_by)
df <- df %>%
select(ID, one_of(keep_cols))
fixed_parameters <- df %>%
summarise_at(vars(one_of(parameters)), funs(length(unique(.)))) %>%
gather(Parameter, N_unique) %>%
filter(N_unique == 1)
if (nrow(fixed_parameters) > 0) {
if (nrow(fixed_parameters) == length(parameters)) {
stop(simpleError("No parameter have more than one unique value, correlations cannot be computed."))
}
removed_params <-
indiv_parameters %>%
filter(column %in% fixed_parameters$Parameter)
message(
sprintf(
"Correlations are not computed for parameters(s) with one unique value: %s\n",
paste(removed_params$name, collapse = ", ")
)
)
parameters <- parameters[parameters %in%
setdiff(parameters, unique(c(
as.character(removed_params$id), removed_params$column
)))]
df <- df %>%
select(-one_of(fixed_parameters$Parameter))
}
df <- df %>%
rename(!!!setNames(parameters, names(parameters)))
named_group <- split_by
if (!is.null(split_by) &&
split_by %in% run$model$covariates$column &&
is.null(names(named_group))) {
named_group <-
setNames(
split_by,
filter(run$model$covariates, column == split_by)$name
)
levels <- run$model$categorical_covariates_levels[[split_by]]
df[[split_by]] <-
plyr::mapvalues(df[[split_by]], from = levels, to = names(levels))
}
if (!is.null(named_group)) {
df <- df %>% rename(!!!named_group)
}
if (type == "scatterplot") {
if (length(parameters) <= 2) {
safe_p1 <- as.name(names(parameters)[[1]])
safe_p2 <- as.name(names(parameters)[[length(parameters)]])
safe_color <- NULL
if (!is.null(names(named_group))) {
safe_color <- as.name(names(named_group))
}
g <- ggplot(df, aes_string(
x = safe_p1,
y = safe_p2,
colour = safe_color
)) +
geom_point()
} else {
g <- GGally::ggscatmat(as.data.frame(select(df, -ID)))
}
if (!is.null(smoothing_method)) {
g <-
g + geom_smooth(method = smoothing_method, aes(colour = NULL), se = smoothing_se)
}
g
} else {
cor.matrix <-
cor(select(df, one_of(names(parameters))), method = correlation_method, use = "pairwise.complete.obs")
if (auto_order & ncol(cor.matrix) >= 2) {
abs_matrix <- abs(cor.matrix)
dd <- dist((1 - abs_matrix) / 2)
hc <- hclust(dd)
cor.matrix <- cor.matrix[hc$order, hc$order]
}
cor.matrix[lower.tri(cor.matrix)] <- NA
# corr_table <-
# reshape2::melt(cor.matrix, varnames = c("parameter1", "parameter2"))
corr_table <- crossing(
parameter1 = rownames(cor.matrix),
parameter2 = colnames(cor.matrix)
) %>%
mutate(value = map2_dbl(parameter1, parameter2, ~cor.matrix[.x, .y]))
corr_table <- corr_table %>%
rename(correlation = value) %>%
filter(!is.na(correlation)) %>%
group_by(parameter2) %>%
mutate(N = dplyr::n()) %>%
ungroup() %>%
arrange(desc(N)) %>%
select(-N)
corr_table$parameter2 <-
factor(corr_table$parameter2, levels = unique(as.character(corr_table$parameter2)))
if (all(colnames(cor.matrix) %in% rownames(cor.matrix))) {
corr_table$parameter1 <-
factor(corr_table$parameter1, levels = rev(levels(corr_table$parameter2)))
} else {
corr_table$parameter1 <-
factor(corr_table$parameter1, levels = unique(as.character(corr_table$parameter1)))
}
g <-
ggplot(corr_table, aes(parameter1, parameter2, fill = (correlation))) +
geom_tile()
g <- g +
geom_text(
aes(
parameter1,
parameter2,
label = round(correlation, digits = getOption("pmxploit.correlationplot.digits"))
),
color = getOption("pmxploit.correlationplot.text_color"),
size = 4
) +
scale_fill_gradient2(
name = bquote(rho ~ plain("coefficient")),
# sprintf("%s coefficient", correlation_method)
low = getOption("pmxploit.correlationplot.dark_color2"),
mid = "white",
high = getOption("pmxploit.correlationplot.dark_color")
)
if (fixed_ratio) {
g <- g + coord_fixed()
}
if (auto_legend) {
g <- g +
guides(fill = guide_colorbar(barwidth = getOption(
"pmxploit.correlationplot.bandwidth"
))) +
labs(x = NULL, y = NULL, caption = str_c("Path: ", run$info$path))
}
g
}
}
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