Nothing
get_contrast_data_factor <- function(model,
newdata,
variable,
cross,
first_cross,
modeldata,
...) {
if (is.factor(newdata[[variable$name]])) {
levs <- levels(newdata[[variable$name]])
convert_to_factor <- TRUE
} else if (get_variable_class(newdata, variable$name, "binary")) {
levs <- 0:1
convert_to_factor <- FALSE
} else {
msg <- "The `%s` variable is treated as a categorical (factor) variable, but the original data is of class %s. It is safer and faster to convert such variables to factor before fitting the model and calling `slopes` functions."
msg <- sprintf(msg, variable$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
}
}
# 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)])
} else if (length(variable$value) == 2) {
if (is.character(variable$value)) {
tmp <- modeldata[[variable$name]]
if (any(!variable$value %in% as.character(tmp))) {
msg <- "Some of the values supplied to the `variables` argument were not found in the dataset."
insight::format_error(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$value[2])
}
}
if (isTRUE(variable$value %in% c("revreference", "revpairwise", "revsequential"))) {
levs_idx <- levs_idx[, .(lo = hi, hi = lo)]
}
# 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$marginaleffects_contrast_label == "custom")) {
levs_idx[, "marginaleffects_contrast_label" := paste0(marginaleffects_contrast_hi, ", ", marginaleffects_contrast_lo)]
}
lo <- hi <- cjdt(list(newdata, levs_idx))
original <- data.table::rbindlist(rep(list(newdata), nrow(levs_idx)))
if (is.factor(newdata[[variable$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)
}
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