#' Plot ranked specification curve
#'
#' @description `r lifecycle::badge("deprecated")`
#' This function is deprecated because the new version of specr uses a new analytic framework.
#' In this framework, you can plot a similar figure simply by using the generic \code{plot()} function and
#' adding the argument \code{type = "curve"}.
#' This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the upper panel in \code{plot_specs()}.
#'
#' @param df a data frame resulting from \code{run_specs()}.
#' @param var which variable should be evaluated? Defaults to estimate (the effect sizes computed by [run_specs()]).
#' @param group Should the arrangement of the curve be grouped by a particular choice?
#' Defaults to NULL, but can be any of the present choices (e.g., x, y, controls...)
#' @param desc logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE.
#' @param ci logical value indicating whether confidence intervals should be plotted.
#' @param ribbon logical value indicating whether a ribbon instead should be plotted.
#' @param legend logical value indicating whether the legend should be plotted Defaults to FALSE.
#' @param null Indicate what value represents the null hypothesis (Defaults to zero)
#'
#' @return a \link[ggplot2]{ggplot} object.
#' @export
#'
#' @examples
#' # load additional library
#' library(ggplot2) # for further customization of the plots
#'
#' # Run specification curve analysis
#' results <- run_specs(df = example_data,
#' y = c("y1", "y2"),
#' x = c("x1", "x2"),
#' model = c("lm"),
#' controls = c("c1", "c2"),
#' subsets = list(group1 = unique(example_data$group1),
#' group2 = unique(example_data$group2)))
#'
#' # Plot simple specification curve
#' plot_curve(results)
#'
#' # Ribbon instead of CIs and customize further
#' plot_curve(results, ci = FALSE, ribbon = TRUE) +
#' geom_hline(yintercept = 0) +
#' geom_hline(yintercept = median(results$estimate),
#' linetype = "dashed") +
#' theme_linedraw()
plot_curve <- function(df,
var = .data$estimate,
group = NULL,
desc = FALSE,
ci = TRUE,
ribbon = FALSE,
legend = FALSE,
null = 0){
# Deprecation warning
lifecycle::deprecate_warn("1.0.0", "plot_curve()", "plot.specr.object()")
var <- enquo(var)
group <- enquo(group)
# Create basic plot
plot <- df %>%
format_results(var = var, group = group, null = null, desc = desc) %>%
ggplot(aes(x = .data$specifications,
y = !! var,
ymin = .data$conf.low,
ymax = .data$conf.high,
color = .data$color)) +
geom_point(aes(color = .data$color),
size = 1) +
theme_minimal() +
scale_color_identity() +
theme(strip.text = element_blank(),
axis.line = element_line("black", size = .5),
legend.position = "none",
panel.spacing = unit(.75, "lines"),
axis.text = element_text(colour = "black")) +
labs(x = "")
# add legends if necessary
if (isFALSE(legend)) {
plot <- plot +
theme(legend.position = "none")
}
# add CIs if necessary
if (isTRUE(ci)) {
plot <- plot +
geom_pointrange(alpha = 0.5,
size = .6,
fatten = 1)
}
# add ribbon if necessary
if (isTRUE(ribbon)) {
plot <- plot +
geom_ribbon(aes(ymin = .data$conf.low,
ymax = .data$conf.high,
color = "lightgrey"),
alpha = 0.25)
}
return(plot)
}
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