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get_comparisons_data_factor <- function(
model,
newdata,
variable,
cross,
first_cross,
modeldata,
mfx,
...) {
if (is.factor(newdata[[variable$name]])) {
levs <- levels(newdata[[variable$name]])
convert_to_factor <- TRUE
} else if (check_variable_class(mfx, variable$name, "binary")) {
levs <- variable$value
convert_to_factor <- FALSE
} else {
if (isTRUE(getOption("marginaleffects_safe", default = TRUE))) {
msg <- "The `%s` variable is treated as a categorical (factor) variable, but the original data is of class %s. It is safer and faster to convert such variables to factor before fitting the model and calling a `marginaleffects` function."
msg <- sprintf(msg, variable$name, class(newdata[[variable$name]])[1])
warn_once(msg, "marginaleffects_warning_factor_on_the_fly_conversion")
}
if (is.factor(modeldata[[variable$name]])) {
levs <- levels(modeldata[[variable$name]])
convert_to_factor <- TRUE
} else {
levs <- sort(unique(modeldata[[variable$name]]))
convert_to_factor <- FALSE
}
}
# string shortcuts
flag <- checkmate::check_choice(
variable$value,
c(
"reference",
"revreference",
"pairwise",
"revpairwise",
"sequential",
"revsequential",
"all",
"minmax"
)
)
if (isTRUE(flag)) {
levs_idx <- contrast_categories_shortcuts(levs, variable, interaction)
# custom data frame or function
} else if (
isTRUE(checkmate::check_function(variable$value)) ||
isTRUE(checkmate::check_data_frame(variable$value))
) {
out <- contrast_categories_custom(variable, newdata)
return(out)
# vector of two values
} else if (isTRUE(checkmate::check_atomic_vector(variable$value, len = 2))) {
if (is.character(variable$value)) {
tmp <- modeldata[[variable$name]]
if (!all(variable$value %in% as.character(tmp))) {
msg <- "Some of the values supplied to the `variables` argument were not found in the dataset."
stop_sprintf(msg)
}
idx <- match(variable$value, as.character(tmp))
levs_idx <- data.table::data.table(lo = tmp[idx[1]], hi = tmp[idx[[2]]])
} else if (is.numeric(variable$value)) {
tmp <- newdata[[variable$name]]
if (convert_to_factor) {
levs_idx <- data.table::data.table(
lo = factor(as.character(variable$value[1]), levels = levels(tmp)),
hi = factor(as.character(variable$value[2]), levels = levels(tmp))
)
} else {
levs_idx <- data.table::data.table(
lo = variable$value[1],
hi = variable$value[2]
)
}
} else {
levs_idx <- data.table::data.table(
lo = variable$value[1],
hi = variable$value[2]
)
}
}
tmp <- contrast_categories_processing(
first_cross,
levs_idx,
levs,
variable,
newdata
)
lo <- tmp[[1]]
hi <- tmp[[2]]
original <- tmp[[3]]
if (is.factor(newdata[[variable$name]]) || isTRUE(convert_to_factor)) {
lo[[variable$name]] <- factor(
lo[["marginaleffects_contrast_lo"]],
levels = levs
)
hi[[variable$name]] <- factor(
hi[["marginaleffects_contrast_hi"]],
levels = levs
)
} else {
lo[[variable$name]] <- lo[["marginaleffects_contrast_lo"]]
hi[[variable$name]] <- hi[["marginaleffects_contrast_hi"]]
}
contrast_label <- hi$marginaleffects_contrast_label
contrast_null <- hi$marginaleffects_contrast_hi == hi$marginaleffects_contrast_lo
tmp <- !grepl("^marginaleffects_contrast", colnames(lo))
lo <- lo[, tmp, with = FALSE]
hi <- hi[, tmp, with = FALSE]
out <- list(
rowid = original$rowid,
lo = lo,
hi = hi,
original = original,
ter = rep(variable$name, nrow(lo)), # lo can be different dimension than newdata
lab = contrast_label,
contrast_null = contrast_null
)
return(out)
}
contrast_categories_shortcuts <- function(levs, variable, interaction) {
# index contrast orders based on variable$value
if (isTRUE(variable$value %in% c("reference", "revreference"))) {
# null contrasts are interesting with interactions
if (!isTRUE(interaction)) {
levs_idx <- data.table::data.table(
lo = levs[1],
hi = levs[2:length(levs)]
)
} else {
levs_idx <- data.table::data.table(lo = levs[1], hi = levs)
}
} else if (isTRUE(variable$value %in% c("pairwise", "revpairwise"))) {
levs_idx <- CJ(lo = levs, hi = levs, sorted = FALSE)
# null contrasts are interesting with interactions
if (!isTRUE(interaction)) {
levs_idx <- levs_idx[levs_idx$hi != levs_idx$lo, ]
levs_idx <- levs_idx[
match(levs_idx$lo, levs) < match(levs_idx$hi, levs),
]
}
} else if (isTRUE(variable$value %in% c("sequential", "revsequential"))) {
levs_idx <- data.table::data.table(
lo = levs[1:(length(levs) - 1)],
hi = levs[2:length(levs)]
)
} else if (isTRUE(variable$value == "all")) {
levs_idx <- CJ(lo = levs, hi = levs, sorted = FALSE)
} else if (isTRUE(variable$value == "minmax")) {
levs_idx <- data.table::data.table(lo = levs[1], hi = levs[length(levs)])
}
if (
isTRUE(
variable$value %in% c("revreference", "revpairwise", "revsequential")
)
) {
levs_idx <- levs_idx[, .(lo = hi, hi = lo)]
}
return(levs_idx)
}
contrast_categories_df <- function(variable) {
# manual data frame
if (all(c("low", "high") %in% colnames(variable$value))) {
low <- variable$value$low
high <- variable$value$high
} else if (all(c("lo", "hi") %in% colnames(variable$value))) {
low <- variable$value$low
high <- variable$value$high
} else {
low <- variable$value[[1]]
high <- variable$value[[2]]
}
levs_idx <- data.table::data.table(
lo = low,
hi = high
)
return(levs_idx)
}
contrast_categories_processing <- function(
first_cross,
levs_idx,
levs,
variable,
newdata) {
# internal option applied to the first of several contrasts when
# interaction=TRUE to avoid duplication. when only the first contrast
# flips, we get a negative sign, but if first increases and second
# decreases, we get different total effects.
if (isTRUE(first_cross)) {
idx <- match(levs_idx$hi, levs) >= match(levs_idx$lo, levs)
if (sum(idx) > 0) {
levs_idx <- levs_idx[idx, , drop = FALSE]
}
}
levs_idx$isNULL <- levs_idx$hi == levs_idx$lo
levs_idx$label <- suppressWarnings(tryCatch(
sprintf(variable$label, levs_idx$hi, levs_idx$lo),
error = function(e) variable$label
))
levs_idx <- stats::setNames(
levs_idx,
paste0("marginaleffects_contrast_", colnames(levs_idx))
)
if (
!"marginaleffects_contrast_label" %in% colnames(levs_idx) ||
all(levs_idx$marginaleffects_contrast_label == "custom")
) {
levs_idx[
,
"marginaleffects_contrast_label" := paste0(
marginaleffects_contrast_hi,
", ",
marginaleffects_contrast_lo
)
]
}
lo <- hi <- cjdt(list(newdata, levs_idx))
original <- data.table::rbindlist(rep(list(newdata), nrow(levs_idx)))
return(list(lo, hi, original))
}
contrast_categories_custom <- function(variable, newdata) {
original <- newdata
if (!"rowid" %in% colnames(original)) {
original$rowid <- seq_len(nrow(original))
}
hi <- lo <- original
if (isTRUE(checkmate::check_function(variable$value))) {
variables_df <- variable$value(newdata[[variable$name]])
} else if (isTRUE(checkmate::check_data_frame(variable$value))) {
variables_df <- variable$value
}
checkmate::assert_data_frame(variables_df, nrows = nrow(original))
if (all(c("low", "high") %in% colnames(variables_df))) {
lo[[variable$name]] <- variables_df[["low"]]
hi[[variable$name]] <- variables_df[["high"]]
} else if (all(c("lo", "hi") %in% colnames(variables_df))) {
lo[[variable$name]] <- variables_df[["lo"]]
hi[[variable$name]] <- variables_df[["hi"]]
} else {
lo[[variable$name]] <- variables_df[[1]]
hi[[variable$name]] <- variables_df[[2]]
}
out <- list(
rowid = original$rowid,
lo = lo,
hi = hi,
original = original,
ter = rep(variable$name, nrow(lo)), # lo can be different dimension than newdata
lab = "custom",
contrast_null = rep(FALSE, nrow(lo))
)
return(out)
}
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