# R/plot_comparisons.R In marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests

#### Documented in plot_comparisons

```#' Plot Conditional or Marginal Comparisons
#'
#' @description
#' Plot comparisons on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets).
#'
#' The `by` argument is used to plot marginal comparisons, that is, comparisons made on the original data, but averaged by subgroups. This is analogous to using the `by` argument in the `comparisons()` function.
#'
#' The `condition` argument is used to plot conditional comparisons, that is, comparisons made on a user-specified grid. This is analogous to using the `newdata` argument and `datagrid()` function in a `comparisons()` call. All variables whose values are not specified explicitly are treated as usual by `datagrid()`, that is, they are held at their mean or mode (or rounded mean for integers). This includes grouping variables in mixed-effects models, so analysts who fit such models may want to specify the groups of interest using the `condition` argument, or supply model-specific arguments to compute population-level estimates. See details below.
#'
#' See the "Plots" vignette and website for tutorials and information on how to customize plots:
#'
#' * https://marginaleffects.com/vignettes/plot.html
#' * https://marginaleffects.com
#'
#' @param variables Name of the variable whose contrast we want to plot on the y-axis.
#' @param draw `TRUE` returns a `ggplot2` plot. `FALSE` returns a `data.frame` of the underlying data.
#' @inheritParams comparisons
#' @param newdata When `newdata` is `NULL`, the grid is determined by the `condition` argument. When `newdata` is not `NULL`, the argument behaves in the same way as in the `comparisons()` function.
#' @inheritParams plot_slopes
#' @inheritParams slopes
#' @template model_specific_arguments
#' @return A `ggplot2` object
#' @export
#' @examples
#' mod <- lm(mpg ~ hp * drat * factor(am), data = mtcars)
#'
#' plot_comparisons(mod, variables = "hp", condition = "drat")
#'
#' plot_comparisons(mod, variables = "hp", condition = c("drat", "am"))
#'
#' plot_comparisons(mod, variables = "hp", condition = list("am", "drat" = 3:5))
#'
#' plot_comparisons(mod, variables = "am", condition = list("hp", "drat" = range))
#'
#' plot_comparisons(mod, variables = "am", condition = list("hp", "drat" = "threenum"))
plot_comparisons <- function(model,
variables = NULL,
condition = NULL,
by = NULL,
newdata = NULL,
type = "response",
vcov = NULL,
conf_level = 0.95,
wts = FALSE,
comparison = "difference",
transform = NULL,
rug = FALSE,
gray = FALSE,
draw = TRUE,
...) {

dots <- list(...)
if ("effect" %in% names(dots)) {
if (is.null(variables)) {
variables <- dots[["effect"]]
} else {
insight::format_error("The `effect` argument has been renamed to `variables`.")
}
}
if ("transform_post" %in% names(dots)) { # backward compatibility
transform <- dots[["transform_post"]]
}

if (inherits(model, "mira") && is.null(newdata)) {
msg <- "Please supply a data frame to the `newdata` argument explicitly."
insight::format_error(msg)
}

# order of the first few paragraphs is important
scall <- rlang::enquo(newdata)
newdata <- sanitize_newdata_call(scall, newdata, model, by = by)
if (!isFALSE(wts) && is.null(by)) {
insight::format_error("The `wts` argument requires a `by` argument.")
}

checkmate::assert_character(by, null.ok = TRUE, max.len = 3, min.len = 1, names = "unnamed")
if ((!is.null(condition) && !is.null(by)) || (is.null(condition) && is.null(by))) {
msg <- "One of the `condition` and `by` arguments must be supplied, but not both."
insight::format_error(msg)
}

# sanity check
checkmate::assert(
checkmate::check_character(variables, names = "unnamed"),
checkmate::check_list(variables, names = "unique"),
.var.name = "variables")

modeldata <- get_modeldata(
model,
wts = wts)

# mlr3 and tidymodels
if (is.null(modeldata) || nrow(modeldata) == 0) {
modeldata <- newdata
}

# conditional
if (!is.null(condition)) {
condition <- sanitize_condition(model, condition, variables, modeldata = modeldata)
v_x <- condition\$condition1
v_color <- condition\$condition2
v_facet_1 <- condition\$condition3
v_facet_2 <- condition\$condition4
datplot <- comparisons(
model,
newdata = condition\$newdata,
type = type,
vcov = vcov,
conf_level = conf_level,
by = FALSE,
wts = wts,
variables = variables,
comparison = comparison,
transform = transform,
cross = FALSE,
modeldata = modeldata,
...)
}

# marginal
if (!is.null(by)) {
newdata <- sanitize_newdata(
model = model,
newdata = newdata,
modeldata = modeldata,
by = by,
wts = wts)
datplot <- comparisons(
model,
by = by,
newdata = newdata,
type = type,
vcov = vcov,
conf_level = conf_level,
variables = variables,
wts = wts,
comparison = comparison,
transform = transform,
cross = FALSE,
modeldata = modeldata,
...)
v_x <- by[[1]]
v_color <- hush(by[[2]])
v_facet_1 <- hush(by[[3]])
v_facet_2 <- hush(by[[4]])
}

datplot <- plot_preprocess(datplot, v_x = v_x, v_color = v_color, v_facet_1 = v_facet_1, v_facet_2 = v_facet_2, condition = condition, modeldata = modeldata)

# return immediately if the user doesn't want a plot
if (isFALSE(draw)) {
out <- as.data.frame(datplot)
attr(out, "posterior_draws") <- attr(datplot, "posterior_draws")
return(out)
}

# ggplot2
insight::check_if_installed("ggplot2")
p <- plot_build(datplot, v_x = v_x, v_color = v_color, v_facet_1 = v_facet_1, v_facet_2 = v_facet_2, gray = gray, rug = rug, modeldata = modeldata)
p <- p + ggplot2::labs(x = v_x, y = sprintf("Comparison"))

return(p)
}
```

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marginaleffects documentation built on May 29, 2024, 4:03 a.m.