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#' Plot all effect estimates against p values
#'
#' \code{all_plot()} generates a scatter plot with effect estimates of all possible models
#' again p values.
#' @export
#' @param data \emph{Object} from \code{all_cox}, \code{all_glm}, \code{all_speedglm}, or \code{all_glm}, including all effect estimate values.
#' @param xlabels \emph{Numeric vector} x-axis tick labels. Default is
#' \code{"c(0, 0.001, 0.01, 0.05, 0.2, 0.5, 1)"}.
#' @param xlim \emph{Vector} of 2 numeric values for x-axis limits. Default is \code{"c(0, 1)"}.
#' @param xlab \emph{Character} string for x-axis name. Default is \code{"P value"}.
#' @param ylim \emph{Vector} of 2 numeric values for y-axis limits.
#' @param ylab \emph{Character} string for y-axis name. Default depends on original model types.
#' @param yscale_log \emph{TRUE or FALSE} to re-scale y-axis to "log10". Default is \code{"FALSE"}.
#' @param title \emph{Character} for plot title. Default is \code{"NULL"}.
#' @return A \pkg{ggplot2} object: scatter plot
#' @examples
#' vlist <- c("Age", "Sex", "Married", "BMI", "Education", "Income")
#' results <- all_cox(crude = "Surv(t0, t1, Endpoint) ~ Diabetes", xlist = vlist, data = diab_df)
#' all_plot(results)
#' @name all_plot
all_plot <- function(data,
xlabels = c(0, 0.001, 0.01, 0.05, 0.2, 0.5, 1),
xlim = c(0, 1),
xlab = "P value",
ylim = NULL,
ylab = NULL,
yscale_log = FALSE,
title = NULL) {
xbreaks <- sapply(xlabels, function(x) x^(log(0.5) / (log(0.05))))
result_df <- data$estimate
if (is.null(ylab)) {
if (data$fun == "all_cox") {
ylab <- "Hazard ratio"
} else if (data$fun == "all_lm") {
ylab <- "Coefficient"
} else if (data$family == "poisson") {
ylab <- "Rate ratio"
} else if (data$family == "binomial") {
ylab <- "Odds ratio"
} else {
ylab <- "Effect estimates"
}
}
hline <- ifelse(data$fun == "all_lm", 0, 1)
df_scatter <- result_df %>%
dplyr::mutate(p_value = p^(log(0.5) / log(0.05)))
if (is.null(ylim) & data$fun != "all_lm") {
maxv <- max(max(1 / result_df$estimate), max(result_df$estimate))
ylim <- c(1 / maxv, maxv)
} else if (is.null(ylim) & data$fun == "all_lm") {
ylim <- c(-(max(abs(result_df$estimate))), max(abs(result_df$estimate)))
}
if (yscale_log) {
my_yscale <- scale_y_log10(limits = ylim)
} else {
my_yscale <- scale_y_continuous(limits = ylim)
}
ggplot(data = df_scatter, aes(x = p_value, y = estimate)) +
geom_point(shape = 1) +
scale_x_continuous(breaks = xbreaks, labels = xlabels, limits = xlim) +
my_yscale +
labs(x = xlab, y = ylab, title = title) +
theme_bw() +
geom_vline(xintercept = 0.5, linetype = 2) +
geom_hline(yintercept = hline, linetype = 2)
}
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