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(!is.function(transform)) { if (is.null(transform[[1]])) { return(x) } else { transform <- transform[[1]] } } checkmate::assert_data_frame(x) checkmate::assert_function(transform) cols <- intersect(colnames(x), c("estimate", "conf.low", "conf.high")) draws <- attr(x, "posterior_draws") if (!is.null(draws)) { dim_pre <- dim(draws) draws <- transform(draws) dim_post <- dim(draws) if (!identical(dim_pre, dim_post)) { insight::format_error("The `transform` function must return an object of the same class as its input: a matrix input must return a matrix of the same size, and a vector input must return a vector of the same length.") } } for (col in cols) { x[[col]] <- transform(x[[col]]) } for (col in c("std.error", "statistic")) { x[[col]] <- NULL } attr(x, "posterior_draws") <- draws return(x) } marginaleffects/R/get_contrast_data_logical.R0000644000176200001440000000153414631452335021120 0ustar liggesusersget_contrast_data_logical <- function(model, newdata, variable, ...) { # custom data frame or function if (isTRUE(checkmate::check_function(variable$value)) || isTRUE(checkmate::check_data_frame(variable$value))) { out <- contrast_categories_custom(variable, newdata) return(out) } lo <- hi <- newdata lo[[variable$name]] <- FALSE hi[[variable$name]] <- TRUE lab <- suppressWarnings(tryCatch( sprintf(variable$label, TRUE, FALSE), error = function(e) variable$label)) out <- list(rowid = seq_len(nrow(newdata)), lo = lo, hi = hi, original = newdata, ter = rep(variable$name, nrow(newdata)), lab = rep(lab, nrow(newdata))) return(out) } marginaleffects/R/plot.R0000644000176200001440000000210014631452335014705 0ustar liggesusers# The reason why I don't supply these functions is that there is too much ambiguity about which kind of plot people are expecting. The `plot_predictions()` function has a bunch of extra arguments, and makes assumptions based on the order of the user-supplied `condition` values, for example. Having another `plot.predictions()` method in addition would require me to add all those arguments, or to make opinionated decisions that would require a ton of `if/else` based on the original `predictions()` call's `by`, `newdata` grid, and others. This is hard, and almost certainly unsatisfactory. Also, it duplicates the plotting user-interface. It feels better to steer all users to a single point of entry for plots. #' @export plot.predictions <- function(x, ...) { insight::format_error("Please use the `plot_predictions()` function.") } #' @export plot.comparisons <- function(x, ...) { insight::format_error("Please use the `plot_comparisons()` function.") } #' @export plot.slopes <- function(x, ...) { insight::format_error("Please use the `plot_slopes()` function.") }marginaleffects/R/methods_fixest.R0000644000176200001440000000250714631452335016767 0ustar liggesusers#' @rdname get_predict #' @export get_predict.fixest <- function(model, newdata = insight::get_data(model), type = "response", ...) { insight::check_if_installed("fixest") if (is.null(type)) { type <- sanitize_type(model = model, type = type, calling_function = "predictions") } dots <- list(...) # some predict methods raise warnings on unused arguments unused <- c("normalize_dydx", "step_size", "numDeriv_method", "conf.int", "internal_call") dots <- dots[setdiff(names(dots), unused)] # fixest is super slow when using do call because of some `deparse()` call # issue #531: we don't want to waste time computing intervals or risk having # them as leftover columns in contrast computations pred <- try( stats::predict( object = model, newdata = newdata, type = type), silent = TRUE) if (inherits(pred, "try-error")) { return(pred) } if ("rowid" %in% colnames(newdata)) { out <- data.frame( rowid = newdata$rowid, estimate = as.numeric(pred)) } else { out <- data.frame( rowid = seq_len(nrow(newdata)), estimate = as.numeric(pred)) } return(out) } marginaleffects/R/get_modeldata.R0000644000176200001440000001574714631452335016545 0ustar liggesusersget_modeldata <- function(model, additional_variables = FALSE, modeldata = NULL, wts = FALSE, ...) { # mice if (inherits(model, c("mira", "amest"))) { return(modeldata) } # tidymodels: always require `newdata`, because sometimes there needs to be # some pre-processing, and we want to rely on the workflow to do that. # workflows are triggered on `stats::predict()` if (inherits(model, c("model_fit", "workflow"))) { return(NULL) } # otherwise, insight::get_data can sometimes return only the the outcome variable if (inherits(model, "bart")) { modeldata <- insight::get_data(model, additional_variables = TRUE) return(modeldata) } if (!is.null(modeldata)) { modeldata <- set_variable_class(modeldata, model = model) return(modeldata) } # often used to extract `by` if (isTRUE(checkmate::check_data_frame(additional_variables))) { additional_variables <- colnames(additional_variables) } # always get weights if (isTRUE(checkmate::check_string(wts))) { additional_variables <- c(additional_variables, wts) } # feols weights can be a formula if (inherits(model, "fixest")) { fwts <- tryCatch(all.vars(model$call$weights), error = function(e) NULL) additional_variables <- c(additional_variables, fwts) } # after by if (isTRUE(checkmate::check_flag(additional_variables))) { out <- hush(insight::get_data( model, additional_variables = additional_variables, verbose = FALSE) ) out <- set_variable_class(out, model = model) return(out) } # always extract offset variable if available off <- hush(insight::find_offset(model)) if (isTRUE(checkmate::check_formula(off))) { additional_variables <- c(additional_variables, hush(all.vars(off))) } else if (isTRUE(checkmate::check_character(off, max.len = 4))) { if (isTRUE(grepl("~", off))) { additional_variables <- c(additional_variables, hush(all.vars(stats::as.formula(off)))) } else { additional_variables <- c(additional_variables, off) } } # always extract weights variable if available wtsvname <- hush(insight::find_weights(model)) if (isTRUE(checkmate::check_formula(wtsvname))) { additional_variables <- c(additional_variables, hush(all.vars(wtsvname))) } else if (isTRUE(checkmate::check_character(wtsvname, max.len = 4))) { if (isTRUE(grepl("~", wtsvname))) { additional_variables <- c(additional_variables, hush(all.vars(stats::as.formula(wtsvname)))) } else { additional_variables <- c(additional_variables, wtsvname) } } out <- hush(insight::get_data(model, verbose = FALSE, additional_variables = additional_variables)) if (isTRUE(wts)) { tmp <- insight::get_weights(model) if (is.numeric(tmp) && length(tmp) == nrow(out)) { out[["marginaleffects_wts_internal"]] <- tmp } } # iv_robust and some others if (is.null(out)) { out <- evalup(model[["call"]][["data"]]) } if (is.null(out)) { out <- evalup(attr(model, "call")$data) } out <- as.data.frame(out) out <- set_variable_class(out, model = model) return(out) } set_variable_class <- function(modeldata, model = NULL) { if (is.null(modeldata)) return(modeldata) # this can be costly on large datasets, when only a portion of # variables are used in the model variables <- NULL if (is.null(model)) { variables <- tryCatch( unlist(insight::find_variables(model, flatten = TRUE), use.names = FALSE), error = function(e) NULL) } if (is.null(variables)) variables <- colnames(modeldata) out <- modeldata cl <- NULL for (col in variables) { if (is.logical(out[[col]])) { cl[col] <- "logical" } else if (is.character(out[[col]])) { cl[col] <- "character" } else if (is.factor(out[[col]])) { cl[col] <- "factor" } else if (inherits(out[[col]], "Surv")) { # is numeric but breaks the %in% 0:1 check cl[col] <- "other" } else if (is.numeric(out[[col]])) { if (is_binary(out[[col]])) { cl[col] <- "binary" } else { cl[col] <- "numeric" } } else { cl[col] <- "other" } } if (is.null(model)) { attr(out, "marginaleffects_variable_class") <- cl return(out) } te <- hush(insight::find_terms(model, flatten = TRUE)) # in-formula factor regex <- "^(^as\\.factor|^factor)\\((.*)\\)" idx <- gsub( regex, "\\2", Filter(function(x) grepl(regex, x), te)) cl[names(cl) %in% idx] <- "factor" # in-formula categoricals regex <- "^(^mo|^strata)\\((.*)\\)" idx <- gsub( regex, "\\2", Filter(function(x) grepl(regex, x), te)) cl[names(cl) %in% idx] <- "strata" # in-formula numeric regex <- "^numeric\\((.*)\\)$|^as.numeric\\((.*)\\)$" idx <- gsub( regex, "\\1", Filter(function(x) grepl(regex, x), te)) cl[names(cl) %in% idx] <- "numeric" # in-formula logical regex <- "^logical\\((.*)\\)$|^as.logical\\((.*)\\)$" idx <- gsub( regex, "\\1", Filter(function(x) grepl(regex, x), te)) cl[names(cl) %in% idx] <- "logical" # in-formula: fixest::i() fi <- NULL idx <- grepl("^i\\(.*\\)$", te) if (sum(idx) > 0) { arg1 <- lapply(te[idx], function(x) hush(as.character(str2lang(x)[[2]]))) arg2 <- lapply(te[idx], function(x) hush(as.character(str2lang(x)[[3]]))) arg1 <- unlist(arg1, recursive = TRUE) arg2 <- unlist(arg2, recursive = TRUE) arg2 <- gsub("^i\\.", "", arg2[grepl("^i\\.", arg2)]) fi <- unique(c(arg1, arg2)) } for (f in fi) { cl[f] <- "cluster" } # attributes attr(out, "marginaleffects_variable_class") <- cl return(out) } get_variable_class <- function(newdata, variable = NULL, compare = NULL) { if ("marginaleffects_variable_class" %in% names(attributes(newdata))) { cl <- attributes(newdata)$marginaleffects_variable_class } else { newdata <- set_variable_class(newdata) cl <- attributes(newdata)$marginaleffects_variable_class } if (is.null(compare) && is.null(variable)) { out <- cl } else if (is.null(compare)) { out <- cl[variable] } else if (is.null(variable)) { if (isTRUE(compare == "categorical")) { out <- cl[cl %in% c("factor", "character", "logical", "strata", "cluster", "binary")] } else { out <- cl[cl %in% compare] } } else { if (isTRUE(compare == "categorical")) { out <- cl[variable] %in% c("factor", "character", "logical", "strata", "cluster", "binary") } else { out <- cl[variable] %in% compare } } return(out) } marginaleffects/R/methods_glmx.R0000644000176200001440000000041214631452335016425 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.glmx <- function(model, coefs, ...) { out <- model out$coefficients$glm <- coefs[names(out$coefficients$glm)] out$coefficients$extra <- coefs[names(out$coefficients$extra)] return(out) } marginaleffects/R/methods_stats.R0000644000176200001440000000545114631452335016624 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.glm <- function(model, coefs, ...) { model[["coefficients"]] <- sub_named_vector( model[["coefficients"]], coefs ) ## But, there's an edge case!! When `predict(model, se.fit = TRUE)` is called without `newdata`, `predict.lm()` isn't called. ## Instead `model$linear.predictors` is returned directly if `type = "link"` and ## `model$fitted.values` is returned directly if `type = "response"`. ## `marginal_effects()` for "glm" is always called with `newdata`, so we won't hit this. model } #' @rdname set_coef #' @export set_coef.lm <- function(model, coefs, ...) { model[["coefficients"]] <- sub_named_vector( model[["coefficients"]], coefs ) model } #' @rdname get_predict #' @export get_predict.lm <- function(model, newdata = insight::get_data(model), type = "response", ...) { MM <- attr(newdata, "marginaleffects_model_matrix") beta <- get_coef(model) if (!isTRUE(checkmate::check_matrix(MM)) || ncol(MM) != length(beta)) { out <- get_predict.default(model = model, newdata = newdata, type = type, ...) return(out) } p <- model$rank p1 <- seq_len(p) piv <- if (p) qr(model)$pivot[p1] if (!all(seq_len(ncol(MM)) %in% piv)) { MM <- MM[, piv, drop = FALSE] beta <- beta[piv] } if (getOption("marginaleffects_linalg", default = "RcppEigen") == "RcppEigen") { pred <- eigenMatMult(MM, beta) } else { pred <- drop(MM %*% beta) } # `pred` is a secret argument which re-uses the default get_predict to format a vector a data frame using correct `rowid` out <- get_predict.default(model = model, newdata = newdata, type = type, pred = pred, ...) return(out) } #' @rdname get_predict #' @export get_predict.glm <- function(model, newdata = insight::get_data(model), type = "response", ...) { out <- NULL MM <- attr(newdata, "marginaleffects_model_matrix") if (isTRUE(checkmate::check_matrix(MM))) { if (isTRUE(checkmate::check_choice(type, c("link")))) { out <- get_predict.lm(model = model, newdata = newdata, ...) } else if (isTRUE(checkmate::check_choice(type, "response")) || is.null(type)) { out <- get_predict.lm(model = model, newdata = newdata, ...) out$estimate <- stats::family(model)$linkinv(out$estimate) } } if (is.null(out)) { out <- get_predict.default(model = model, newdata = newdata, type = type, ...) } return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.nls <- function(model, coefs, ...) { out <- model out$m$setPars(coefs) return(out) } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.nls <- function(model, ...) { model$m$getPars() } marginaleffects/R/methods_betareg.R0000644000176200001440000000400614631452335017072 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.betareg <- function(model, coefs, ...) { # coefs are split between mean coefs (which can be length 0) and precision coefs # (which must be length > 0 and always start with "(phi)_" due to get_coef.betareg(), # to match with get_varcov(., component = "all") output). In betareg object, these # are stored as two elements in a list, with precision coefs lacking the "(phi)_" # prefix, so we remove it. mean_coefs <- coefs[names(coefs) != "(phi)" & !startsWith(names(coefs), "(phi)_")] precision_coefs <- coefs[names(coefs) == "(phi)" | startsWith(names(coefs), "(phi)_")] names(precision_coefs) <- sub("(phi)_", "", names(precision_coefs), fixed = TRUE) if (length(mean_coefs) > 0) { model[["coefficients"]]$mean[names(mean_coefs)] <- mean_coefs } model[["coefficients"]]$precision[names(precision_coefs)] <- precision_coefs model } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.betareg <- function(model, ...) { mean_coefs <- model$coefficients$mean precision_coefs <- model$coefficients$precision # precision coefs have "(phi)_" appended to their names in covariance matrix; mean coefficients # never have this, so no risk of duplicate names, and precision coefs are always determined. # Mimicking coef.betareg(., "full"). if (!identical(names(precision_coefs), "(phi)")) { names(precision_coefs) <- paste0("(phi)_", names(precision_coefs)) } c(mean_coefs, precision_coefs) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.betareg <- function(model, newdata, ...) { out <- stats::predict(model, newdata = newdata) out <- data.frame(rowid = seq_len(nrow(newdata)), estimate = out) return(out) } #' @rdname sanitize_model_specific sanitize_model_specific.betareg <- function(model, ...) { insight::check_if_installed("insight", minimum_version = "0.17.1") return(model) } marginaleffects/R/sanitize_type.R0000644000176200001440000000274214631452335016632 0ustar liggesusers#' check type sanity #' #' @param model model object #' @param type character vector #' @noRd sanitize_type <- function(model, type, by = FALSE, calling_function = "raw") { # mlr3 if (inherits(model, "Learner")) { if (is.null(type)) type <- "response" valid <- setdiff(model$predict_types, "se") checkmate::assert_choice(type, choices = valid, null.ok = TRUE) return(type) } checkmate::assert_character(type, len = 1, null.ok = TRUE) if (inherits(model, "model_fit")) { cl <- "model_fit" } else { cl <- class(model)[1] } if (!cl %in% type_dictionary$class) { cl <- "other" } dict <- type_dictionary # raw is often invoked by `get_predict()`, which is required for {clarify} and others. # we only allow invlink(link) in predictions() and marginal_means(), which are handled by {marginaleffects} if (!calling_function %in% "predictions") {# || !isFALSE(by)) { dict <- dict[dict$type != "invlink(link)", , drop = FALSE] } # fixest: invlink(link) only supported for glm model if (inherits(model, "fixest")) { if (!isTRUE(hush(model[["method_type"]]) %in% c("feglm"))) { dict <- dict[dict$type != "invlink(link)", , drop = FALSE] } } dict <- dict[dict$class == cl, , drop = FALSE] checkmate::assert_choice(type, choices = dict$type, null.ok = TRUE) if (is.null(type)) { type <- dict$type[1] } return(type) }marginaleffects/R/RcppExports.R0000644000176200001440000000036114632674171016234 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 eigenMatMult <- function(A, B) { .Call('_marginaleffects_eigenMatMult', PACKAGE = 'marginaleffects', A, B) } marginaleffects/R/print.R0000644000176200001440000002725214631452335015102 0ustar liggesusers#' Print a marginaleffects object in knitr #' #' @keywords internal #' @return A string with class `knit_asis` to be printed in Rmarkdown or Quarto documents. #' @exportS3Method knitr::knit_print knit_print.marginaleffects <- function(x, ...) { if (isTRUE(getOption("marginaleffects_print_style") == "tinytable")) { insight::check_if_installed("tinytable") x <- print(x, "tinytable") printfun <- utils::getFromNamespace("knit_print.tinytable", "tinytable") printfun(x) } else { print(x) } } #' Print `marginaleffects` objects #' #' @description #' This function controls the text which is printed to the console when one of the core `marginalefffects` functions is called and the object is returned: `predictions()`, `comparisons()`, `slopes()`, `hypotheses()`, `avg_predictions()`, `avg_comparisons()`, `avg_slopes()`. #' #' All of those functions return standard data frames. Columns can be extracted by name, `predictions(model)$estimate`, and all the usual data manipulation functions work out-of-the-box: `colnames()`, `head()`, `subset()`, `dplyr::filter()`, `dplyr::arrange()`, etc. #' #' Some of the data columns are not printed by default. You can disable pretty printing and print the full results as a standard data frame using the `style` argument or by applying `as.data.frame()` on the object. See examples below. #' #' @param x An object produced by one of the `marginaleffects` package functions. #' @param style "summary", "data.frame", or "tinytable" #' @param digits The number of digits to display. #' @param p_eps p values smaller than this number are printed in "<0.001" style. #' @param topn The number of rows to be printed from the beginning and end of tables with more than `nrows` rows. #' @param nrows The number of rows which will be printed before truncation. #' @param ncols The maximum number of column names to display at the bottom of the printed output. #' @param type boolean: should the type be printed? #' @param column_names boolean: should the column names be printed? #' @param ... Other arguments are currently ignored. #' @export #' @examples #' library(marginaleffects) #' mod <- lm(mpg ~ hp + am + factor(gear), data = mtcars) #' p <- predictions(mod, by = c("am", "gear")) #' p #' #' subset(p, am == 1) #' #' print(p, style = "data.frame") #' #' data.frame(p) #' print.marginaleffects <- function(x, style = getOption("marginaleffects_print_style", default = "summary"), digits = getOption("marginaleffects_print_digits", default = 3), p_eps = getOption("marginaleffects_print_p_eps", default = 0.001), topn = getOption("marginaleffects_print_topn", default = 5), nrows = getOption("marginaleffects_print_nrows", default = 30), ncols = getOption("marginaleffects_print_ncols", default = 30), type = getOption("marginaleffects_print_type", default = TRUE), column_names = getOption("marginaleffects_print_column_names", default = TRUE), ...) { checkmate::assert_number(digits) checkmate::assert_number(topn) checkmate::assert_number(nrows) checkmate::assert_choice( style, choices = c("data.frame", "summary", "tinytable", "html", "latex", "markdown", "typst") ) if (isTRUE(style == "data.frame")) { print(as.data.frame(x)) return(invisible(x)) } out <- x nrows <- max(nrows, 2 * topn) if ("group" %in% colnames(out) && all(out$group == "main_marginaleffects")) { out$group <- NULL } # subset before rounding so that digits match top and bottom rows if (nrow(out) > nrows) { out <- rbind(utils::head(out, topn), utils::tail(out, topn)) splitprint <- TRUE } else { splitprint <- FALSE } # round and replace NAs ps <- c("p.value", "p.value.nonsup", "p.value.noninf", "p.value.equiv") for (i in seq_along(out)) { if (colnames(out)[i] %in% ps) { out[[i]] <- format.pval(out[[i]], digits = digits, eps = p_eps) } else if (isTRUE("s.value" == colnames(out)[i])) { out[[i]] <- sprintf("%.1f", out[[i]]) } else { out[[i]] <- format(out[[i]], digits = digits) } } if (is.null(attr(x, "conf_level"))) { alpha <- NULL } else { alpha <- 100 * (1 - attr(x, "conf_level")) } # contrast is sometimes useless if ("contrast" %in% colnames(out) && all(out$contrast == "")) { out$contrast <- NULL } statistic_label <- attr(x, "statistic_label") if (is.null(statistic_label)) { if (any(out[["df"]] < Inf)) { statistic_label <- "t" } else { statistic_label <- "z" } } # rename dict <- c( "group" = "Group", "term" = "Term", "contrast" = "Contrast", "hypothesis" = "Hypothesis", "value" = "Value", "by" = "By", "estimate" = "Estimate", "std.error" = "Std. Error", "statistic" = statistic_label, "p.value" = sprintf("Pr(>|%s|)", statistic_label), "s.value" = "S", "conf.low" = ifelse(is.null(alpha), "CI low", sprintf("%.1f %%", alpha / 2)), "conf.high" = ifelse(is.null(alpha), "CI high", sprintf("%.1f %%", 100 - alpha / 2)), "pred.low" = ifelse(is.null(alpha), "Pred low", sprintf("Pred. %.1f %%", alpha / 2)), "pred.high" = ifelse(is.null(alpha), "Pred high", sprintf("Pred. %.1f %%", 100 - alpha / 2)), "pred.set" = ifelse(is.null(alpha), "Pred Set", sprintf("Pred Set %.1f %%", 100 - alpha / 2)), "p.value.nonsup" = "p (NonSup)", "p.value.noninf" = "p (NonInf)", "p.value.equiv" = "p (Equiv)", "df" = "Df", "df1" = "Df 1", "df2" = "Df 2" ) if (inherits(x, "marginalmeans")) { dict["estimate"] <- "Mean" } # Subset columns idx <- c( names(dict), grep("^contrast_", colnames(x), value = TRUE)) # explicitly given by user in `datagrid()` or `by` or `newdata` nd <- attr(x, "newdata") if (is.null(nd)) { nd <- attr(x, "newdata_newdata") } tmp <- c("by", attr(x, "hypothesis_by"), attr(nd, "variables_datagrid"), attr(nd, "newdata_variables_datagrid"), attr(x, "variables_datagrid"), attr(x, "newdata_variables_datagrid") ) if (isTRUE(checkmate::check_character(attr(x, "by")))) { tmp <- c(tmp, attr(x, "by")) } idx <- c(idx[1:grep("by", idx)], tmp, idx[(grep("by", idx) + 1):length(idx)]) if (isTRUE(attr(nd, "newdata_newdata_explicit")) || isTRUE(attr(nd, "newdata_explicit"))) { idx <- c(idx, colnames(nd)) } # drop useless columns: rowid useless <- c("rowid", "rowidcf") # drop useless columns: dv dv <- tryCatch( unlist(insight::find_response(attr(x, "model"), combine = TRUE), use.names = FALSE), error = function(e) NULL) useless <- c(useless, dv) # selection style data.table::setDT(out) # drop useless columns idx <- setdiff(unique(idx), useless) idx <- intersect(idx, colnames(out)) out <- out[, ..idx, drop = FALSE] if ("term" %in% colnames(out) && all(out$term == "cross")) { out[["term"]] <- NULL colnames(out) <- gsub("^contrast_", "C: ", colnames(out)) } for (i in seq_along(dict)) { colnames(out)[colnames(out) == names(dict)[i]] <- dict[i] } # avoid infinite recursion by stripping marginaleffect.summary class data.table::setDF(out) # recommend avg_*() rec <- "" if (isFALSE(attr(x, "by"))) { if (inherits(x, "predictions")) { rec <- "?avg_predictions and " } else if (inherits(x, "comparisons")) { rec <- "?avg_comparisons and " } else if (inherits(x, "slopes")) { rec <- "?avg_slopes and " } } print_columns_text <- print_type_text <- NULL if (ncol(x) <= ncols && isTRUE(column_names)) { print_columns_text <- paste("Columns:", paste(colnames(x), collapse = ", "), "\n") } if (isTRUE(type) && !is.null(attr(x, "type"))) { print_type_text <- paste("Type: ", attr(x, "type"), "\n") } if (style %in% c("tinytable", "html", "latex", "typst", "markdown")) { insight::check_if_installed("tinytable") tab <- as.data.frame(out) if (isTRUE(splitprint)) { tab <- rbind(utils::head(tab, topn), utils::tail(tab, topn)) } # at <- attributes(tab) # attributes(tab) <- at[names(at) %in% c("row.names", "names", "class")] args <- list(x = tab) notes <- c(print_type_text, print_columns_text) if (!is.null(notes)) args$notes <- notes tab <- do.call(tinytable::tt, args) tab <- tinytable::format_tt(tab, escape = TRUE) if (isTRUE(splitprint)) { msg <- "%s rows omitted" msg <- sprintf(msg, nrow(x) - 2 * topn) msg <- stats::setNames(list(topn + 1), msg) tab <- tinytable::group_tt(tab, i = msg) tab <- tinytable::style_tt(tab, i = topn + 1, align = "c") } tab@output <- style if (style == "tinytable") { return(tab) } print(tab) return(invisible(tab)) } # head cat("\n") print_head <- attr(x, "print_head") if (!is.null(print_head)) { cat(print_head, "\n") } # some commands do not generate average contrasts/mfx. E.g., `lnro` with `by` if (splitprint) { print(utils::head(out, n = topn), row.names = FALSE) msg <- "--- %s rows omitted. See %s?print.marginaleffects ---" msg <- sprintf(msg, nrow(x) - 2 * topn, rec) cat(msg, "\n") # remove colnames tmp <- utils::capture.output(print(utils::tail(out, n = topn), row.names = FALSE)) tmp <- paste(tmp[-1], collapse = "\n") cat(tmp) } else { print(out, row.names = FALSE) } cat("\n") cat(print_columns_text) cat(print_type_text) cat("\n") ## This is tricky to extract nicely when transform_* are passed from avg_comparisons to comparisons. I could certainly figure it out, but at the same time, I don't think the print method should return information that is immediately visible from the call. This is different from `type`, where users often rely on the default value, which can change from model to model, so printing it is often # if (!is.null(attr(x, "comparison_label"))) { # cat("Pre-transformation: ", paste(attr(x, "comparison_label"), collapse = ""), "\n") # } # if (!is.null(attr(x, "transform_label"))) { # cat("Post-transformation: ", paste(attr(x, "transform_label"), collapse = ""), "\n") # } print_tail <- attr(x, "print_tail") if (!is.null(print_tail)) { cat(print_tail, "\n") } return(invisible(x)) } #' @noRd #' @export print.hypotheses <- print.marginaleffects #' @noRd #' @export print.predictions <- print.marginaleffects #' @noRd #' @export print.comparisons <- print.marginaleffects #' @noRd #' @export print.slopes <- print.marginaleffects #' @noRd #' @exportS3Method knitr::knit_print knit_print.hypotheses <- knit_print.marginaleffects #' @noRd #' @exportS3Method knitr::knit_print knit_print.predictions <- knit_print.marginaleffects #' @noRd #' @exportS3Method knitr::knit_print knit_print.comparisons <- knit_print.marginaleffects #' @noRd #' @exportS3Method knitr::knit_print knit_print.slopes <- knit_print.marginaleffects marginaleffects/R/equivalence.R0000644000176200001440000000304214631452335016236 0ustar liggesusersequivalence <- function(x, equivalence = NULL, df = Inf, ...) { if (is.null(equivalence)) { return(x) } if (!is.null(equivalence) && any(!c("estimate", "std.error") %in% colnames(x))) { msg <- "The `equivalence` argument is not supported with models for which `marginaleffects` does not estimate a standard error (e.g., bayesian)." insight::format_error(msg) } checkmate::assert_numeric(equivalence, min.len = 1, max.len = 2) if (length(equivalence) == 1) { equivalence <- c(equivalence, equivalence) } delta <- abs(diff(equivalence)) / 2 null <- min(equivalence) + delta # definitions from `emmeans`, with a different user interface based on symmetric "equivalence" x$statistic.noninf <- (x$estimate - equivalence[1]) / x$std.error x$statistic.nonsup <- (x$estimate - equivalence[2]) / x$std.error ## keep this in case we return to the emmeans-style user interface # x$statistic.inf <- (x$estimate - null + delta) / x$std.error # x$statistic.sup <- (x$estimate - null - delta) / x$std.error if (is.infinite(df)) { x$p.value.noninf <- stats::pnorm(x$statistic.noninf, lower.tail = FALSE) x$p.value.nonsup <- stats::pnorm(x$statistic.nonsup, lower.tail = TRUE) } else { x$p.value.noninf <- stats::pt(x$statistic.noninf, lower.tail = FALSE, df = x[["df"]]) x$p.value.nonsup <- stats::pt(x$statistic.nonsup, lower.tail = TRUE, df = x[["df"]]) } x$p.value.equiv <- pmax(x$p.value.nonsup, x$p.value.noninf) return(x) }marginaleffects/R/recall.R0000644000176200001440000000427714631452335015212 0ustar liggesusers# fancy way to catch the call so that get_averages(slopes()) does not evaluate twice # and is fast recall <- function(x, ...) { funs <- c("comparisons", "slopes", "predictions", "marginalmeans", "hypotheses", "avg_predictions", "avg_comparisons", "avg_slopes") # 2-step estimation with already evaluated & assigned call if (!is.call(x)) { # unsupported evaluated object: return `NULL` if (!inherits(x, funs)) { return(NULL) } # retrieve call mc <- attr(x, "call") if (!is.call(mc)) { msg <- sprintf("Call could not be retrieved from object of class %s.", class(x)[1]) insight::format_error(msg) } # unsupported call: return `NULL` } else { if (!as.character(x[1]) %in% funs) { return(NULL) } mc <- x } dots <- list(...) # don't overwrite certain arguments if ("hypothesis" %in% names(mc) && "hypothesis" %in% names(dots)) { if (is.null(dots[["hypothesis"]])) { dots[["hypothesis"]] <- NULL } } # safe to work with original objects when available objs <- c("newdata", "model") for (obj in objs) { if (!is.null(attr(x, "call")[[obj]])) { dots[[obj]] <- attr(x, "call")[[obj]] } } # overwrite previous arguments for (n in names(dots)) { # named NULL should not remove the corresponding argument from the call if (is.null(dots[[n]])) { mc[n] <- list(NULL) } else { mc[[n]] <- dots[[n]] } } ## old `rlang` convenience. I don't think the current version is toooo unsafe. # FUN <- rlang::call_modify # args <- c(list(".call" = quote(mc)), dots) # # evaluate call # mc <- do.call("FUN", args) # # expand user-supplied arguments (don't think this is necessary) # funs <- list( # "predictions" = predictions, # "comparisons" = comparisons, # "slopes" = slopes, # "hypotheses" = hypotheses, # "marginalmeans" = marginalmeans) # mc <- match.call( # definition = funs[[as.character(mc)[1]]], # call = mc) out <- eval(mc) return(out) } marginaleffects/R/unpack_matrix_cols.R0000644000176200001440000000351714631452335017631 0ustar liggesusersunpack_matrix_cols <- function(x) { # what data types are we working with dcs <- sapply(x, function(x) class(x)[1]) # do something if we have matrix columns if (any(mcols <- dcs == "matrix")) { # assume that any matrix columns are of the same number of cols nc <- ncol(x[mcols][[1L]]) out <- lapply(x[mcols], as.vector) other <- lapply(x[!mcols], function(y, nc) rep(y, times = nc), nc = nc) out <- as.data.frame(out) other <- as.data.frame(other) out <- cbind(out, other) # put order back as it was out <- out[names(x)] } else { return(x) } out } # The content of this file was adapted from the `gratia` package # https://github.com/gavinsimpson/gratia # The MIT License (MIT) # # Copyright (c) 2013-2020 Gavin L. Simpson # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # marginaleffects/R/methods_mlm.R0000644000176200001440000000110714631452335016245 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.mlm <- function(model, coefs, ...) { model$coefficients[] <- coefs return(model) } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.mlm <- function(model, ...) { out <- insight::get_parameters(model, ...) out <- stats::setNames( out$Estimate, sprintf("%s:%s", out$Response, out$Parameter)) return(out) } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.mlm <- function(model, ...) { resp <- insight::get_response(model) return(names(resp)) } marginaleffects/R/methods_MASS.R0000644000176200001440000000441414631452335016227 0ustar liggesusers#' @include get_coef.R #' @rdname get_coef #' @export get_coef.polr <- function(model, ...) { out <- insight::get_parameters(model) out <- stats::setNames(out$Estimate, out$Parameter) names(out) <- gsub("Intercept: ", "", names(out)) return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.polr <- function(model, coefs, ...) { # in basic model classes coefficients are named vector idx <- match(names(model$coefficients), names(coefs)) model[["coefficients"]] <- coefs[idx] idx <- match(names(model$zeta), names(coefs)) model[["zeta"]] <- coefs[idx] model } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.polr <- function(model, ...) { resp <- insight::get_response(model) if (is.factor(resp)) { out <- levels(resp) } else { out <- unique(resp) } return(out) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.polr <- function(model, newdata = insight::get_data(model), type = "probs", ...) { type <- sanitize_type(model, type, calling_function = "predictions") # hack: 1-row newdata returns a vector, so get_predict.default does not learn about groups if (nrow(newdata) == 1) { hack <- TRUE newdata <- newdata[c(1, 1), , drop = FALSE] newdata$rowid[1] <- -Inf } else { hack <- FALSE } out <- get_predict.default(model, newdata = newdata, type = type, ...) # hack out <- out[out$rowid != -Inf, ] return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.glmmPQL <- function(model, coefs, ...) { model[["coefficients"]][["fixed"]][names(coefs)] <- coefs model } #' @rdname get_predict #' @export get_predict.glmmPQL <- function(model, newdata = insight::get_data(model), type = "response", ...) { out <- stats::predict(model, newdata = newdata, type = type, ...) out <- data.frame( rowid = seq_len(nrow(newdata)), estimate = out) return(out) } marginaleffects/R/sanitize_condition.R0000644000176200001440000001567014631452335017643 0ustar liggesuserscondition_shortcuts <- function(x, tr, shortcuts) { if (identical(tr, "threenum")) { m <- mean(x, na.rm = TRUE) s <- stats::sd(x, na.rm = TRUE) out <- c(m - s, m, m + s) } else if (identical(tr, "fivenum")) { out <- stats::fivenum(x, na.rm = TRUE) } else if (identical(tr, "minmax")) { out <- c( min(x, na.rm = TRUE), max(x, na.rm = TRUE)) } else if (identical(tr, "quartile")) { out <- stats::quantile(x, probs = c(.25, .5, .75), na.rm = TRUE) } return(out) } sanitize_condition <- function(model, condition, variables = NULL, modeldata = NULL) { # allow multiple conditions and/or effects checkmate::assert( checkmate::check_character(condition, min.len = 1, max.len = 4), checkmate::check_list(condition, min.len = 1, max.len = 4)) # c("a", "b") or list("a", "b") -> named list of NULLs flag1 <- isTRUE(checkmate::check_character(condition)) flag2 <- isTRUE(checkmate::check_list(condition, names = "unnamed")) && all(sapply(condition, function(x) isTRUE(checkmate::check_string(x)))) if (flag1 || flag2) { condition <- stats::setNames(rep(list(NULL), length(condition)), unlist(condition)) } # validity of the list for (i in seq_along(condition)) { if (identical(names(condition)[i], "")) { if (!isTRUE(checkmate::check_character(condition[[i]], len = 1))) { msg <- "The `condition` argument must be a character vector or a named list." insight::format_error(msg) } else { names(condition)[i] <- condition[[i]] tmp <- stats::setNames(list(NULL), names(condition)[i]) condition <- utils::modifyList(condition, tmp, keep.null = TRUE) } } } # get data to know over what range of values we should plot if (is.null(modeldata) && isTRUE(checkmate::check_character(condition))) { dat <- get_modeldata(model, additional_variables = condition) } else if (is.null(modeldata) && isTRUE(checkmate::check_list(condition))) { dat <- get_modeldata(model, additional_variables = names(condition)) } else { dat <- modeldata } resp <- insight::get_response(model) respname <- insight::find_response(model) flag <- checkmate::check_true(all(names(condition) %in% c(colnames(dat), "group"))) if (!isTRUE(flag)) { msg <- sprintf("Entries in the `condition` argument must be element of: %s", paste(colnames(dat), collapse = ", ")) insight::format_error(msg) } # condition names condition1 <- names(condition)[[1]] condition2 <- hush(names(condition)[[2]]) condition3 <- hush(names(condition)[[3]]) condition4 <- hush(names(condition)[[4]]) # build typical dataset with a sequence of values over "condition" range at_list <- list() shortcuts <- c("threenum", "fivenum", "minmax", "quartile") # condition 1: x-axis if (is.null(condition[[1]])) { if (get_variable_class(dat, condition1, "binary")) { at_list[[condition1]] <- 0:1 } else if (is.numeric(dat[[condition1]]) && !get_variable_class(dat, condition1, "categorical")) { at_list[[condition1]] <- seq( min(dat[[condition1]], na.rm = TRUE), max(dat[[condition1]], na.rm = TRUE), length.out = 50) } else { at_list[[condition1]] <- factor(unique(dat[[condition1]])) } } else { if (isTRUE(checkmate::check_choice(condition[[1]], shortcuts))) { at_list[[condition1]] <- condition_shortcuts(dat[[condition1]], condition[[1]], shortcuts) } else { at_list[[condition1]] <- condition[[1]] } } # condition 2: color if (length(condition) > 1) { if (is.null(condition[[2]])) { if (is.numeric(dat[[condition2]])) { at_list[[condition2]] <- stats::fivenum(dat[[condition2]]) } else { at_list[[condition2]] <- unique(dat[[condition2]]) } } else { if (isTRUE(checkmate::check_choice(condition[[2]], shortcuts))) { at_list[[condition2]] <- condition_shortcuts(dat[[condition2]], condition[[2]], shortcuts) } else { at_list[[condition2]] <- condition[[2]] } } } # condition 3: facet_1 if (length(condition) > 2) { if (is.null(condition[[3]])) { if (is.numeric(dat[[condition3]])) { at_list[[condition3]] <- stats::fivenum(dat[[condition3]]) } else { at_list[[condition3]] <- unique(dat[[condition3]]) } } else { if (isTRUE(checkmate::check_choice(condition[[3]], shortcuts))) { at_list[[condition3]] <- condition_shortcuts(dat[[condition3]], condition[[3]], shortcuts) } else { at_list[[condition3]] <- condition[[3]] } } } # condition 4: facet_2 if (length(condition) > 3) { if (is.null(condition[[4]])) { if (is.numeric(dat[[condition4]])) { at_list[[condition4]] <- stats::fivenum(dat[[condition4]]) } else { at_list[[condition4]] <- unique(dat[[condition4]]) } } else { if (isTRUE(checkmate::check_choice(condition[[4]], shortcuts))) { at_list[[condition4]] <- condition_shortcuts(dat[[condition4]], condition[[4]], shortcuts) } else { at_list[[condition4]] <- condition[[4]] } } } at_list[["model"]] <- model at_list[["newdata"]] <- dat if (isTRUE(checkmate::check_list(variables))) { flag <- all(names(variables) %in% names(condition)) } else { flag <- all(variables %in% names(condition)) } if (!flag) { # sometimes we use the same condition as effect (e.g., GAM vignette), # but otherwise we don't want it at all if (isTRUE(checkmate::check_character(variables))) { dups <- setdiff(variables, names(condition)) for (d in dups) { at_list[[d]] <- NULL } } else { at_list[[names(variables)]] <- NULL } } # mlr3 and tidymodels are not supported by `insight::find_variables()`, so we need to create a grid based on all the variables supplied in `newdata` if (inherits(at_list$model, "Learner") || inherits(at_list$model, "model_fit") || inherits(at_list$model, "workflow") ) { at_list$model <- NULL } # create data nd <- do.call("datagrid", at_list) out <- list( "modeldata" = dat, "newdata" = nd, "resp" = resp, "respname" = respname, "condition" = condition, "condition1" = condition1, "condition2" = condition2, "condition3" = condition3, "condition4" = condition4) return(out) }marginaleffects/R/methods_rstanarm.R0000644000176200001440000000014714631452335017312 0ustar liggesusers#' @include get_predict.R #' @rdname get_predict #' @export get_predict.stanreg <- get_predict.brmsfit marginaleffects/R/methods_nnet.R0000644000176200001440000000707514631452335016436 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.multinom <- function(model, coefs, ...) { # internally, coefficients are held in the `wts` vector, with 0s # interspersed. When transforming that vector to a matrix, we see that the # first row and first column are all zeros. # NOTE: must use `newdata` in predict otherwise returns stored object. b_original <- get_coef(model) model$wts[match(b_original, model$wts)] <- coefs return(model) } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.multinom <- function(model, ...) { out <- insight::get_parameters(model, ...) out <- stats::setNames( out$Estimate, sprintf("%s:%s", out$Response, out$Parameter)) return(out) } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.multinom <- function(model, ...) { resp <- insight::get_response(model) if (is.factor(resp)) { out <- levels(resp) } else { out <- unique(resp) } return(out[2:length(out)]) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.multinom <- function(model, newdata = insight::get_data(model), type = "probs", ...) { type <- sanitize_type(model, type, calling_function = "predictions") is_latent <- is_mclogit <- is_nnet <- FALSE if (isTRUE(type == "latent") && inherits(model, c("mblogit", "mclogit"))) { is_latent <- TRUE is_mclogit <- TRUE type <- "link" } else if (isTRUE(type == "latent") && inherits(model, "multinom")) { is_latent <- TRUE is_nnet <- TRUE type <- "probs" } # needed because `predict.multinom` uses `data` rather than `newdata` pred <- stats::predict(model, newdata = newdata, type = type, ...) # atomic vector means there is only one row in `newdata` # two levels DV returns a vector if (isTRUE(checkmate::check_atomic_vector(pred))) { y_original <- sort(unique(insight::get_response(model))) two_levels <- length(y_original) == 2 if (isTRUE(two_levels)) { pred <- matrix(pred) colnames(pred) <- as.character(y_original[2]) } else { pred <- matrix(pred, nrow = 1, dimnames = list(NULL, names(pred))) } } if (is_latent && is_mclogit) { missing_level <- as.character(unique(insight::get_response(model))) missing_level <- setdiff(missing_level, colnames(pred)) if (length(missing_level == 1)) { pred <- cbind(0, pred) colnames(pred)[1] <- missing_level pred <- pred - rowMeans(pred) } else { insight::format_error("Unable to compute predictions on the latent scale.") } } else if (is_latent && is_nnet) { inverse_softMax <- function(mu) { log_mu <- log(mu) return(sweep(log_mu, 1, STATS = rowMeans(log_mu), FUN = "-")) } pred <- inverse_softMax(pred) } # matrix with outcome levels as columns out <- data.frame( group = rep(colnames(pred), each = nrow(pred)), estimate = c(pred)) out$group <- group_to_factor(out$group, model) # usually when `newdata` is supplied by `comparisons` if ("rowid" %in% colnames(newdata)) { out$rowid <- rep(newdata$rowid, times = ncol(pred)) } else { out$rowid <- rep(seq_len(nrow(pred)), times = ncol(pred)) } return(out) } marginaleffects/R/plot_predictions.R0000644000176200001440000001666714631452335017337 0ustar liggesusers#' Plot Conditional or Marginal Predictions #' #' @description #' Plot predictions 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 predictions, that is, predictions made on the original data, but averaged by subgroups. This is analogous to using the `by` argument in the `predictions()` function. #' #' The `condition` argument is used to plot conditional predictions, that is, predictions made on a user-specified grid. This is analogous to using the `newdata` argument and `datagrid()` function in a `predictions()` 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 condition Conditional predictions #' + Character vector (max length 4): Names of the predictors to display. #' + Named list (max length 4): List names correspond to predictors. List elements can be: #' - Numeric vector #' - Function which returns a numeric vector or a set of unique categorical values #' - Shortcut strings for common reference values: "minmax", "quartile", "threenum" #' + 1: x-axis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid). #' + Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers `?stats::fivenum` #' @param by Marginal predictions #' + Character vector (max length 3): Names of the categorical predictors to marginalize across. #' + 1: x-axis. 2: color. 3: facets. #' @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 `predictions()` function. #' @param points Number between 0 and 1 which controls the transparency of raw data points. 0 (default) does not display any points. #' @param draw `TRUE` returns a `ggplot2` plot. `FALSE` returns a `data.frame` of the underlying data. #' @inheritParams plot_slopes #' @inheritParams predictions #' @template model_specific_arguments #' @template type #' @return A `ggplot2` object or data frame (if `draw=FALSE`) #' @export #' @examples #' mod <- lm(mpg ~ hp + wt, data = mtcars) #' plot_predictions(mod, condition = "wt") #' #' mod <- lm(mpg ~ hp * wt * am, data = mtcars) #' plot_predictions(mod, condition = c("hp", "wt")) #' #' plot_predictions(mod, condition = list("hp", wt = "threenum")) #' #' plot_predictions(mod, condition = list("hp", wt = range)) #' plot_predictions <- function(model, condition = NULL, by = NULL, newdata = NULL, type = NULL, vcov = NULL, conf_level = 0.95, wts = FALSE, transform = NULL, points = 0, rug = FALSE, gray = FALSE, draw = TRUE, ...) { dots <- list(...) checkmate::assert_number(points, lower = 0, upper = 1) if ("variables" %in% names(dots)) { insight::format_error("The `variables` argument is not supported by this function.") } if ("effect" %in% names(dots)) { insight::format_error("The `effect` argument is not supported by this function.") } 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) # sanity check checkmate::assert_character(by, null.ok = TRUE, max.len = 4, 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) } modeldata <- get_modeldata( model, additional_variables = c(names(condition), by), 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 = NULL, modeldata = modeldata) v_x <- condition$condition1 v_color <- condition$condition2 v_facet_1 <- condition$condition3 v_facet_2 <- condition$condition4 datplot <- predictions( model, newdata = condition$newdata, type = type, vcov = vcov, conf_level = conf_level, transform = transform, modeldata = modeldata, wts = wts, ...) } # marginal if (!isFALSE(by) && !is.null(by)) { # switched from NULL above condition <- NULL newdata <- sanitize_newdata( model = model, newdata = newdata, modeldata = modeldata, by = by, wts = wts) # tidymodels & mlr3 if (is.null(modeldata)) { modeldata <- newdata } datplot <- predictions( model, by = by, type = type, vcov = vcov, conf_level = conf_level, wts = wts, transform = transform, newdata = newdata, modeldata = modeldata, ...) v_x <- by[[1]] v_color <- hush(by[[2]]) v_facet_1 <- hush(by[[3]]) v_facet_2 <- hush(by[[4]]) } dv <- unlist(insight::find_response(model, combine = TRUE), use.names = FALSE)[1] 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, points = points, modeldata = modeldata, dv = dv, rug = rug, gray = gray) p <- p + ggplot2::labs( x = v_x, y = dv, color = v_color, fill = v_color, linetype = v_color) # attach model data for each of use attr(p, "modeldata") <- modeldata return(p) } marginaleffects/R/methods_quantreg.R0000644000176200001440000000402714631452335017312 0ustar liggesusers#' @rdname get_predict #' @export get_predict.rq <- function(model, newdata = insight::get_data(model), type = NULL, ...) { # type argument of the method is used to specify confidence interval type insight::check_if_installed("quantreg") if (isTRUE(getOption("marginaleffects_linalg", default = "RcppEigen") == "RcppEigen")) { MM <- attr(newdata, "marginaleffects_model_matrix") if (isTRUE(checkmate::check_matrix(MM))) { beta <- get_coef(model) out <- hush(eigenMatMult(MM, beta)) if (isTRUE(checkmate::check_numeric(out, len = nrow(newdata)))) { out <- data.frame(rowid = newdata$rowid, estimate = out) return(out) } else { out = data.frame(rowid = seq_len(length(out)), estimate = out) } } } out <- quantreg::predict.rq(model, newdata = newdata, ...) if (isTRUE(checkmate::check_numeric(out, len = nrow(newdata)))) { out <- data.frame(rowid = newdata$rowid, estimate = out) } else { out = data.frame(rowid = seq_len(length(out)), estimate = out) } return(out) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @keywords internal sanitize_model_specific.rqs <- function(model, ...) { stop("`marginaleffects` only supports `quantreg::rq` models with a single `tau` value.", call. = FALSE) } # #' @rdname get_model_matrix # #' @keywords internal # #' @export # get_model_matrix.rq <- function(object, newdata) { # tt <- terms(object) # Terms <- delete.response(tt) # m <- model.frame(Terms, newdata, na.action = na.pass, xlev = object$xlevels) # if (!is.null(cl <- attr(Terms, "dataClasses"))) # stats::.checkMFClasses(cl, m) # X <- model.matrix(Terms, m, contrasts.arg = object$contrasts) # if (!isTRUE(nrow(X) == nrow(newdata))) { # return(NULL) # } else { # return(X) # } # }marginaleffects/R/get_term_labels.R0000644000176200001440000000132314631452335017065 0ustar liggesusersget_term_labels <- function(x, idx = NULL) { if (is.data.frame(x)) { if ("term" %in% names(x) && length(unique(x$term)) == nrow(x)) { return(unique(x$term)) } else if (any(grepl("^contrast", names(x)))) { tmp <- grep("^term$|^contrast", names(x)) out <- x[, tmp, drop = FALSE] if (length(unique(out[["term"]])) == 1) { out[["term"]] <- NULL } out <- do.call(paste, c(out, sep = " ")) } else { out <- paste0("b", seq_len(nrow(x))) } } else if (is.vector(x)) { if (!is.null(names(x))) { out <- names(x) } else { out <- paste0("b", seq_along(x)) } } else { return(NULL) } if (!is.null(idx)) out <- out[idx] return(out) }marginaleffects/R/github_issue.R0000644000176200001440000000040114631452335016423 0ustar liggesusersgithub_issue <- function() { msg <- insight::format_string( "Please report this error along with a minimal reproducible example using publicly available data on Github: https://github.com/vincentarelbundock/marginaleffects/issues ") } marginaleffects/R/methods_brglm2.R0000644000176200001440000000114014631452335016642 0ustar liggesusers#' @rdname get_coef #' @export get_coef.brmultinom <- function(model, ...) { out <- insight::get_parameters(model) out <- stats::setNames(out$Estimate, sprintf("%s:%s", out$Response, out$Parameter)) return(out) } #' @include methods_nnet.R #' @rdname get_predict #' @export get_predict.brmultinom <- get_predict.multinom #' @include get_group_names.R #' @include methods_nnet.R #' @rdname get_group_names #' @export get_group_names.bracl <- get_group_names.multinom #' @rdname get_coef #' @export get_coef.bracl <- function(model, ...) { stats::coef(model) } marginaleffects/R/methods_mclogit.R0000644000176200001440000000333314631452335017121 0ustar liggesusers#' @rdname get_group_names #' @export get_group_names.mblogit <- function(model, type, ...) { out <- get_predict(model, type = type) if ("group" %in% colnames(out)) { out <- unique(out$group) } else { out <- "main_marginaleffects" } return(out) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @export sanitize_model_specific.mblogit <- function(model, calling_function = "marginaleffects", ...) { if (calling_function == "marginaleffects") { variables <- insight::find_variables(model, flatten = TRUE) dat <- insight::get_data(model) dat <- dat[, intersect(variables, colnames(dat))] flag <- any(sapply(dat, is.character)) if (isTRUE(flag)) { stop("Cannot compute marginal effects for models of class `mblogit` when the data includes character variables. Please convert character variables to factors in the dataset before fitting the model, and call `marginaleffects` again.", call. = FALSE) } } return(model) } #' @rdname get_predict #' @export get_predict.mblogit <- function(model, newdata = insight::get_data(model), type = "response", ...) { out <- suppressMessages( get_predict.multinom(model = model, newdata = newdata, type = type, ...)) return(out) } #' @rdname get_coef #' @export get_coef.mblogit <- function(model, ...) { # get_coef.default uses `insight::get_parameters`, but does not combine # Response and Parameter columns. This also matches naming scheme from # `vcov` stats::coef(model) }marginaleffects/R/deprecated.R0000644000176200001440000003471014631452335016043 0ustar liggesusers#' Deprecated function #' #' @keywords internal #' @export deltamethod <- function(...) { .Deprecated("hypotheses()") } #' Deprecated function #' #' @keywords internal #' @export marginaleffects <- function(...) { .Deprecated("slopes()") slopes(...) } #' Deprecated function #' #' @keywords internal #' @export meffects <- marginaleffects #' Deprecated function #' #' @keywords internal #' @export datagridcf <- function(...) { .Deprecated('datagrid(x = 1:2, grid_type = "counterfactual")') datagridcf_internal(...) } #' Deprecated function #' #' @keywords internal #' @export marginal_means <- function(model, variables = NULL, newdata = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, transform = NULL, cross = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, wts = "equal", by = NULL, numderiv = "fdforward", ...) { .Deprecated(' predictions(mod, by = "x", newdata = datagrid(grid_type = \"balanced\")) ') # deprecation and backward compatibility dots <- list(...) sanity_equivalence_p_adjust(equivalence, p_adjust) if ("transform_post" %in% names(dots)) transform <- dots[["transform_post"]] if ("variables_grid" %in% names(dots)) { if (!is.null(newdata)) { insight::format_error("The `variables_grid` argument and has been replaced by `newdata`. These two arguments cannot be used simultaneously.") } newdata <- dots[["variables_grid"]] } if (!is.null(equivalence) && !is.null(p_adjust)) { insight::format_error("The `equivalence` and `p_adjust` arguments cannot be used together.") } numderiv = sanitize_numderiv(numderiv) # build call: match.call() doesn't work well in *apply() call_attr <- c(list( name = "marginal_means", model = model, newdata = newdata, variables = variables, type = type, vcov = vcov, by = by, conf_level = conf_level, transform = transform, wts = wts, hypothesis = hypothesis, equivalence = equivalence, p_adjust = p_adjust, df = df), list(...)) call_attr <- do.call("call", call_attr) # multiple imputation if (inherits(model, c("mira", "amest"))) { out <- process_imputation(model, call_attr, marginal_means = TRUE) return(out) } # if type is NULL, we backtransform if relevant type_string <- sanitize_type(model = model, type = type, calling_function = "marginal_means") if (type_string == "invlink(link)") { if (is.null(hypothesis)) { type_call <- "link" } else { type_call <- "response" type_string <- "response" insight::format_warning('The `type="invlink"` argument is not available unless `hypothesis` is `NULL` or a single number. The value of the `type` argument was changed to "response" automatically. To suppress this warning, use `type="response"` explicitly in your function call.') } } else { type_call <- type_string } modeldata <- get_modeldata(model, additional_variables = FALSE, wts = wts) checkmate::assert_flag(cross) transform <- sanitize_transform(transform) conf_level <- sanitize_conf_level(conf_level, ...) model <- sanitize_model(model, vcov = vcov, calling_function = "marginalmeans") checkmate::assert_choice(wts, choices = c("equal", "cells", "proportional")) if (wts != "equal" && is.data.frame(newdata)) { insight::format_error('The `wts` argument must be "equal" when `newdata` is a data frame.') } tmp <- sanitize_hypothesis(hypothesis, ...) hypothesis <- tmp$hypothesis hypothesis_null <- tmp$hypothesis_null sanity_dots(model = model, ...) if (inherits(model, c("brmsfit", "bart"))) { insight::format_error("This model object type is not yet supported by the `marginal_means` function.") } # fancy vcov processing to allow strings like "HC3" vcov_false <- isTRUE(vcov == FALSE) vcov <- get_vcov(model, vcov = vcov, type = type, ...) # focal categorical variables checkmate::assert_character(variables, min.len = 1, null.ok = TRUE) if (any(variables %in% insight::find_response(model))) { insight::format_error("The `variables` vector cannot include the response.") } if (is.null(variables)) { variables <- insight::find_predictors(model, flatten = TRUE) } idx <- vapply( variables, FUN = get_variable_class, newdata = modeldata, FUN.VALUE = logical(1), compare = c("logical", "character", "factor")) focal <- variables[idx] if (length(focal) == 0) { insight::format_error("No categorical predictor was found in the model data or `variables` argument.") } # non-focal categorical variables checkmate::assert( checkmate::check_null(newdata), checkmate::check_character(newdata), checkmate::check_data_frame(newdata)) if (is.null(newdata)) { nonfocal <- insight::find_predictors(model, flatten = TRUE) nonfocal <- setdiff(nonfocal, focal) } else if (is.character(newdata)) { if (!all(newdata %in% colnames(modeldata))) { insight::format_error("Some of the variables in `newdata` are missing from the data used to fit the model.") } nonfocal <- setdiff(newdata, focal) } else if (is.data.frame(newdata)) { nonfocal <- colnames(newdata) } idx <- vapply( nonfocal, FUN = get_variable_class, newdata = modeldata, FUN.VALUE = logical(1), compare = c("logical", "character", "factor")) nonfocal <- nonfocal[idx] # grid args <- list(model = model) if (is.data.frame(newdata)) { for (v in focal) { args[[v]] <- unique(modeldata[[v]]) } newgrid <- do.call(datagridcf, args) } else { for (v in c(focal, nonfocal)) { args[[v]] <- unique(modeldata[[v]]) } newgrid <- do.call(datagrid, args) } # by: usual tests + only data frames in `marginal_means()` # after newgrid checkmate::assert_data_frame(by, null.ok = TRUE) sanity_by(by, newgrid) # weights if (identical(wts, "equal")) { newgrid[["wts"]] <- 1 } else if (identical(wts, "proportional")) { wtsgrid <- copy(data.table(modeldata)[, ..nonfocal]) idx <- nonfocal wtsgrid[, N := .N] wtsgrid[, "wts" := .N / N, by = idx] # sometimes datagrid() converts to factors when there is a transformation # in the model formula, so we need to standardize the data for (v in colnames(newgrid)) { if (v %in% colnames(wtsgrid) && is.factor(newgrid[[v]])) { wtsgrid[[v]] <- factor(wtsgrid[[v]], levels = levels(newgrid[[v]])) } } wtsgrid <- unique(wtsgrid) newgrid <- merge(newgrid, wtsgrid, all.x = TRUE) newgrid[["wts"]][is.na(newgrid[["wts"]])] <- 0 } else if (identical(wts, "cells")) { # https://stackoverflow.com/questions/66748520/what-is-the-difference-between-weights-cell-and-weights-proportional-in-r-pa idx <- c(focal, nonfocal) wtsgrid <- copy(data.table(modeldata)[, ..idx]) if (length(idx) == 0) { newgrid[["wts"]] <- 1 return(newgrid) } else { wtsgrid <- data.table(modeldata)[ , .(wts = .N), by = idx][ , wts := wts / sum(wts)] # sometimes datagrid() converts to factors when there is a transformation # in the model formula, so we need to standardize the data for (v in colnames(newgrid)) { if (v %in% colnames(wtsgrid) && is.factor(newgrid[[v]])) { wtsgrid[[v]] <- factor(wtsgrid[[v]], levels = levels(newgrid[[v]])) } } wtsgrid <- unique(wtsgrid) newgrid <- merge(newgrid, wtsgrid, all.x = TRUE) newgrid[["wts"]][is.na(newgrid[["wts"]])] <- 0 } } # `equivalence` should not be passed to predictions() at this stage args <- list( model = model, newdata = newgrid, type = type_call, variables = focal, cross = cross, hypothesis = hypothesis, by = by, modeldata = modeldata) args <- c(args, list(...)) args[["equivalence"]] <- NULL mm <- do.call(get_marginalmeans, args) # we want consistent output, regardless of whether `data.table` is installed/used or not out <- as.data.frame(mm) # standard errors via delta method if (!vcov_false) { args <- list( model, vcov = vcov, type = type_call, FUN = get_se_delta_marginalmeans, index = NULL, variables = focal, newdata = newgrid, cross = cross, modeldata = modeldata, hypothesis = hypothesis, by = by, numderiv = numderiv) args <- c(args, list(...)) args[["equivalence"]] <- NULL se <- do.call(get_se_delta, args) # get rid of attributes in column out[["std.error"]] <- as.numeric(se) J <- attr(se, "jacobian") } else { J <- NULL } out <- get_ci( out, conf_level = conf_level, vcov = vcov, null_hypothesis = hypothesis_null, df = df, p_adjust = p_adjust, model = model, ...) # equivalence tests out <- equivalence(out, equivalence = equivalence, df = df, ...) # after assign draws if (identical(type_string, "invlink(link)")) { linv <- tryCatch(insight::link_inverse(model), error = function(e) identity) out <- backtransform(out, transform = linv) } out <- backtransform(out, transform) # column order cols <- c("rowid", "group", colnames(by), "term", "hypothesis", "value", variables, "estimate", "std.error", "statistic", "p.value", "s.value", "conf.low", "conf.high", sort(colnames(out))) cols <- unique(cols) cols <- intersect(cols, colnames(out)) out <- out[, cols, drop = FALSE] # attributes attr(out, "model") <- model attr(out, "jacobian") <- J attr(out, "type") <- type_string attr(out, "model_type") <- class(model)[1] attr(out, "variables") <- variables attr(out, "call") <- call_attr attr(out, "conf_level") <- conf_level attr(out, "transform_label") <- names(transform)[1] if (isTRUE(cross)) { attr(out, "variables_grid") <- setdiff(nonfocal, variables) } else { attr(out, "variables_grid") <- unique(c(nonfocal, variables)) } if (inherits(model, "brmsfit")) { insight::check_if_installed("brms") attr(out, "nchains") <- brms::nchains(model) } class(out) <- c("marginalmeans", class(out)) return(out) } get_marginalmeans <- function(model, newdata, type, variables, cross, modeldata, hypothesis = NULL, by = NULL, ...) { if ("wts" %in% colnames(newdata)) { wts <- "wts" } else { wts <- NULL } # predictions for each cell of all categorical data, but not the response if (isTRUE(cross) || length(variables) == 1) { out <- predictions( model = model, newdata = newdata, type = type, vcov = FALSE, modeldata = modeldata, wts = wts, by = c("group", variables), ...) if (length(variables) == 1) { out$term <- variables out$value <- out[[variables]] } # predictions for each variable individual, then bind } else { pred_list <- draw_list <- list() for (v in variables) { tmp <- predictions( model = model, newdata = newdata, type = type, vcov = FALSE, modeldata = modeldata, wts = wts, by = c("group", v), ...) tmp$rowid <- NULL draw_list[[v]] <- attr(tmp, "posterior_draws") tmp$term <- v data.table::setnames(tmp, old = v, new = "value") pred_list[[v]] <- tmp } # try to preserve term-value class, but convert to character if needed to bind classes <- sapply(pred_list, function(x) class(x$value)[1]) if (length(unique(classes)) > 1) { for (i in seq_along(pred_list)) { pred_list[[i]]$value <- as.character(pred_list[[i]]$value) } } out <- rbindlist(pred_list) } data.table::setDT(out) if (isTRUE(checkmate::check_data_frame(by))) { # warnings for factor vs numeric vs character. merge.data.table usually still works. bycols <- intersect(colnames(out), colnames(by)) if (length(bycols) == 0) { msg <- "There is no common columns in `by` and in the output of `marginal_means()`. Make sure one of the entries in the `variables` argument corresponds to one of the columns in `by`." insight::format_error(msg) } for (b in bycols) { if (is.factor(out[[b]]) && is.numeric(by[[b]])) { out[[b]] <- as.numeric(as.character(out[[b]])) } else if (is.numeric(out[[b]]) && is.factor(by[[b]])) { by[[b]] <- as.numeric(as.character(by[[b]])) } else if (is.factor(out[[b]]) && is.character(by[[b]])) { out[[b]] <- as.character(out[[b]]) } else if (is.character(out[[b]]) && is.factor(by[[b]])) { by[[b]] <- as.character(by[[b]]) } } out <- merge(out, by, sort = FALSE) out <- out[, .(estimate = mean(estimate)), by = "by"] } if (!is.null(hypothesis)) { out <- get_hypothesis(out, hypothesis, by = by) } return(out) } #' Deprecated function #' #' @keywords internal #' @export marginalmeans <- marginal_means marginaleffects/R/methods_afex.R0000644000176200001440000000244314631452335016407 0ustar liggesusers #' @include set_coef.R #' @rdname set_coef #' @export set_coef.afex_aov <- function(model, coefs, ...) { if (isTRUE(checkmate::check_matrix(model$lm$coefficients))) { mat <- matrix(coefs, ncol = ncol(model$lm$coefficients)) dimnames(mat) <- dimnames(model$lm$coefficients) model$lm$coefficients <- mat } else { model$lm$coefficients <- coefs } return(model) } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.afex_aov <- function(model, ...) { b <- insight::get_parameters(model) b <- stats::setNames(b$Estimate, paste(b$Parameter, b$Response, sep = ":")) return(b) } #' @include get_vcov.R #' @rdname get_vcov #' @export get_vcov.afex_aov <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !is.logical(vcov)) { stop("The `vcov` argument is not supported for models of this class.") } insight::get_varcov(model) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.afex_aov <- function(model, newdata = NULL, ...) { out <- stats::predict(model, newdata = newdata) out <- data.frame(estimate = out) if (isTRUE("rowid" %in% colnames(newdata))) { out[["rowid"]] <- newdata[["rowid"]] } else { out[["rowid"]] <- seq_len(nrow(out)) } return(out) } marginaleffects/R/mean_or_mode.R0000644000176200001440000000303314631452335016361 0ustar liggesusers#' find mode, preserve type, and pick an arbitrary value when multi-modal #' https://stackoverflow.com/a/8189441/342331 #' @noRd get_mode <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } #################################################################### # The functions below were copied from the `prediction` package. ## # Copyright: Thomas J. Leeper 2016-2018 ## # MIT License ## #################################################################### #' Compute the mode or mean of `x` #' @param x extract the mean or the mode of vector or data.frame x depending on its type #' @keywords internal #' @noRd #' @return numeric vector get_mean_or_mode <- function(x) { UseMethod("get_mean_or_mode") } #' @export get_mean_or_mode.default <- function(x) { mean(x) } #' @export get_mean_or_mode.character <- function(x) { get_mode(x) } #' @export get_mean_or_mode.factor <- function(x) { get_mode(x) } #' @export get_mean_or_mode.logical <- function(x) { get_mode(x) } #' @export get_mean_or_mode.data.frame <- function(x) { out <- list() for (n in names(x)) { # variables transformed to factor in formula are assigned a "factor" # TRUE attribute by insight::get_data if (isTRUE(attributes(x)$marginaleffects_variable_class[[n]] == "factor")) { out[[n]] <- get_mean_or_mode.factor(x[[n]]) } else { out[[n]] <- get_mean_or_mode(x[[n]]) } } return(out) } marginaleffects/R/set_coef.R0000644000176200001440000000252514631452335015531 0ustar liggesusers#' Internal function to set coefficients #' #' Set the coefficients in a model to different values and return the modified object (internal function) #' #' @rdname set_coef #' @param model object to modify #' @param coefs vector of coefficients to insert in the model object #' @export #' @keywords internal #' @return Model object of the same class as the `model` argument, but with #' different stored coefficients. #' @details To compute the variance of marginal effects we need to take the #' Jacobian with # respect to the model coefficients. These functions manipulate model objects # to change the coefficients stored internally, which changes the output of the # `predict()` function. set_coef <- function(model, coefs, ...) { UseMethod("set_coef") } #' @rdname set_coef #' @export set_coef.default <- function(model, coefs, ...) { # in basic model classes coefficients are named vector # in ordinal::clm models, there are sometimes duplicates, so name matching doesn't work # sometimes names are backticked, others not Issue #1005 a <- gsub("`", "", names(coefs)) b <- gsub("`", "", names(model$coefficients)) flag <- length(model[["coefficients"]]) == length(coefs) && all(a == b) if (flag) { model[["coefficients"]] <- coefs } else { model[["coefficients"]][names(coefs)] <- coefs } model } marginaleffects/R/by.R0000644000176200001440000000522314631452335014352 0ustar liggesusersget_by <- function( estimates, draws, newdata, by, byfun = NULL, verbose = TRUE, ...) { if (is.null(by) || isFALSE(by)) { out <- estimates attr(out, "posterior_draws") <- draws return(out) } missing <- setdiff(setdiff(colnames(by), "by"), colnames(estimates)) if (length(missing) > 0) { idx <- intersect(c("rowid", "rowidcf", missing), colnames(newdata)) estimates <- merge(estimates, newdata[, idx], sort = FALSE, all.x = TRUE) } if (isTRUE(by)) { regex <- "^term$|^group$|^contrast$|^contrast_" bycols <- grep(regex, colnames(estimates), value = TRUE) } else if (isTRUE(checkmate::check_character(by))) { bycols <- by } else if (isTRUE(checkmate::check_data_frame(by))) { idx <- setdiff(intersect(colnames(estimates), colnames(by)), "by") # harmonize column types for (v in colnames(by)) { if (isTRUE(is.character(estimates[[v]])) && isTRUE(is.numeric(by[[v]]))) { by[[v]] <- as.character(by[[v]]) } else if (isTRUE(is.numeric(estimates[[v]])) && isTRUE(is.character(by[[v]]))) { by[[v]] <- as.numeric(by[[v]]) } } estimates[by, by := by, on = idx] bycols <- "by" } if ("by" %in% colnames(estimates) && anyNA(estimates[["by"]])) { msg <- insight::format_message("The `by` data.frame does not cover all combinations of response levels and/or predictors. Some estimates will not be included in the aggregation.") if (isTRUE(verbose)) warning(msg, call. = FALSE) tmp <- !is.na(estimates[["by"]]) draws <- draws[tmp, drop = FALSE] estimates <- estimates[tmp, drop = FALSE] } bycols <- intersect(unique(c("term", bycols)), colnames(estimates)) # bayesian if (!is.null(draws)) { estimates <- average_draws( data = estimates, index = bycols, draws = draws, byfun = byfun) # frequentist } else { if (!is.null(byfun)) { estimates <- estimates[, .(estimate = byfun(estimate)), by = bycols] } else if ("marginaleffects_wts_internal" %in% colnames(newdata)) { estimates <- estimates[, .(estimate = stats::weighted.mean( estimate, marginaleffects_wts_internal, na.rm = TRUE)), by = bycols] } else { estimates <- estimates[, .(estimate = mean(estimate, na.rm = TRUE)), by = bycols] } } return(estimates) }marginaleffects/R/inferences.R0000644000176200001440000002140314631452335016057 0ustar liggesusers#' (EXPERIMENTAL) Bootstrap, Conformal, and Simulation-Based Inference #' #' @description #' Warning: This function is experimental. It may be renamed, the user interface may change, or the functionality may migrate to arguments in other `marginaleffects` functions. #' #' Apply this function to a `marginaleffects` object to change the inferential method used to compute uncertainty estimates. #' #' @param x Object produced by one of the core `marginaleffects` functions. #' @param method String #' + "delta": delta method standard errors #' + "boot" package #' + "fwb": fractional weighted bootstrap #' + "rsample" package #' + "simulation" from a multivariate normal distribution (Krinsky & Robb, 1986) #' + "mi" multiple imputation for missing data #' + "conformal_split": prediction intervals using split conformal prediction (see Angelopoulos & Bates, 2022) #' + "conformal_cv+": prediction intervals using cross-validation+ conformal prediction (see Barber et al., 2020) #' @param R Number of resamples, simulations, or cross-validation folds. #' @param conf_type String: type of bootstrap interval to construct. #' + `boot`: "perc", "norm", "basic", or "bca" #' + `fwb`: "perc", "norm", "basic", "bc", or "bca" #' + `rsample`: "perc" or "bca" #' + `simulation`: argument ignored. #' @param conformal_test Data frame of test data for conformal prediction. #' @param conformal_calibration Data frame of calibration data for split conformal prediction (`method="conformal_split`). #' @param conformal_score String. Warning: The `type` argument in `predictions()` must generate predictions which are on the same scale as the outcome variable. Typically, this means that `type` must be "response" or "probs". #' + "residual_abs" or "residual_sq" for regression tasks (numeric outcome) #' + "softmax" for classification tasks (when `predictions()` returns a `group` columns, such as multinomial or ordinal logit models. #' @param ... #' + If `method="boot"`, additional arguments are passed to `boot::boot()`. #' + If `method="fwb"`, additional arguments are passed to `fwb::fwb()`. #' + If `method="rsample"`, additional arguments are passed to `rsample::bootstraps()`. #' + Additional arguments are ignored for all other methods. #' @details #' When `method="simulation"`, we conduct simulation-based inference following the method discussed in Krinsky & Robb (1986): #' 1. Draw `R` sets of simulated coefficients from a multivariate normal distribution with mean equal to the original model's estimated coefficients and variance equal to the model's variance-covariance matrix (classical, "HC3", or other). #' 2. Use the `R` sets of coefficients to compute `R` sets of estimands: predictions, comparisons, slopes, or hypotheses. #' 3. Take quantiles of the resulting distribution of estimands to obtain a confidence interval and the standard deviation of simulated estimates to estimate the standard error. #' #' When `method="fwb"`, drawn weights are supplied to the model fitting function's `weights` argument; if the model doesn't accept non-integer weights, this method should not be used. If weights were included in the original model fit, they are extracted by [weights()] and multiplied by the drawn weights. These weights are supplied to the `wts` argument of the estimation function (e.g., `comparisons()`). #' #' @section References: #' #' Krinsky, I., and A. L. Robb. 1986. “On Approximating the Statistical Properties of Elasticities.” Review of Economics and Statistics 68 (4): 715–9. #' #' King, Gary, Michael Tomz, and Jason Wittenberg. "Making the most of statistical analyses: Improving interpretation and presentation." American journal of political science (2000): 347-361 #' #' Dowd, Bryan E., William H. Greene, and Edward C. Norton. "Computation of standard errors." Health services research 49.2 (2014): 731-750. #' #' Angelopoulos, Anastasios N., and Stephen Bates. 2022. "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification." arXiv. https://doi.org/10.48550/arXiv.2107.07511. #' #' Barber, Rina Foygel, Emmanuel J. Candes, Aaditya Ramdas, and Ryan J. Tibshirani. 2020. “Predictive Inference with the Jackknife+.” arXiv. http://arxiv.org/abs/1905.02928. #' #' #' @return #' A `marginaleffects` object with simulation or bootstrap resamples and objects attached. #' @examples #' \dontrun{ #' library(marginaleffects) #' library(magrittr) #' set.seed(1024) #' mod <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris) #' #' # bootstrap #' avg_predictions(mod, by = "Species") %>% #' inferences(method = "boot") #' #' avg_predictions(mod, by = "Species") %>% #' inferences(method = "rsample") #' #' # Fractional (bayesian) bootstrap #' avg_slopes(mod, by = "Species") %>% #' inferences(method = "fwb") %>% #' posterior_draws("rvar") %>% #' data.frame() #' #' # Simulation-based inference #' slopes(mod) %>% #' inferences(method = "simulation") %>% #' head() #' } #' @export inferences <- function(x, method, R = 1000, conf_type = "perc", conformal_test = NULL, conformal_calibration = NULL, conformal_score = "residual_abs", ...) { # inherit conf_level from the original object conf_level <- attr(x, "conf_level") if (is.null(conf_level)) conf_level <- 0.95 checkmate::assert( checkmate::check_class(x, "predictions"), checkmate::check_class(x, "comparisons"), checkmate::check_class(x, "slopes"), checkmate::check_class(x, "hypotheses") ) checkmate::assert_number(conf_level, lower = 1e-10, upper = 1 - 1e-10) checkmate::assert_integerish(R, lower = 2) checkmate::assert_choice( method, choices = c("delta", "boot", "fwb", "rsample", "simulation", "conformal_split", "conformal_cv+")) if (method %in% c("conformal_split", "conformal_cv+")) { checkmate::assert_class(x, "predictions") checkmate::assert_choice(conformal_score, choices = c("residual_abs", "residual_sq", "softmax")) checkmate::assert_data_frame(conformal_test, null.ok = FALSE) } if (method == "conformal_split") { checkmate::assert_data_frame(conformal_calibration, null.ok = FALSE) conformal_fun <- conformal_split } if (method == "conformal_cv+") { checkmate::assert_integerish(R, upper = 25) conformal_fun <- conformal_cv_plus } mfx_call <- attr(x, "call") model <- mfx_call[["model"]] # default standard errors are Delta anyway if (method == "delta") { return(x) } else if (method == "boot") { insight::check_if_installed("boot") attr(model, "inferences_method") <- "boot" attr(model, "inferences_dots") <- c(list(R = R), list(...)) attr(model, "inferences_conf_type") <- conf_type } else if (method == "fwb") { insight::check_if_installed("fwb") dots <- list(...) if (!"verbose" %in% names(dots)) { dots[["verbose"]] <- FALSE } attr(model, "inferences_method") <- "fwb" attr(model, "inferences_dots") <- c(list(R = R), dots) attr(model, "inferences_conf_type") <- conf_type if (isTRUE("wts" %in% names(attr(x, "call"))) && !isFALSE(attr(x, "call")[["wts"]])) { insight::format_error('The `fwb` method is not supported with the `wts` argument.') } } else if (method == "rsample") { insight::check_if_installed("rsample") attr(model, "inferences_method") <- "rsample" attr(model, "inferences_dots") <- c(list(times = R), list(...)) attr(model, "inferences_conf_type") <- conf_type } else if (method == "simulation") { insight::check_if_installed("MASS") attr(model, "inferences_method") <- "simulation" attr(model, "inferences_R") <- R } if (isTRUE(grepl("conformal", method))) { out <- conformal_fun( x, R = R, conf_level = conf_level, test = conformal_test, calibration = conformal_calibration, score = conformal_score) } else { mfx_call[["model"]] <- model out <- recall(mfx_call) } return(out) } inferences_dispatch <- function(model, INF_FUN, ...) { args <- list( model = model, INF_FUN = INF_FUN ) args <- c(args, list(...)) if ("rowid" %in% names(args$newdata)) { args$newdata <- subset(args$newdata, rowid > 0) } if (isTRUE(attr(model, "inferences_method") == "rsample")) { do.call(bootstrap_rsample, args) } else if (isTRUE(attr(model, "inferences_method") == "boot")) { do.call(bootstrap_boot, args) } else if (isTRUE(attr(model, "inferences_method") == "fwb")) { do.call(bootstrap_fwb, args) } else if (isTRUE(attr(model, "inferences_method") == "simulation")) { do.call(bootstrap_simulation, args) } else { return(NULL) } } marginaleffects/R/methods_mhurdle.R0000644000176200001440000000143514631452335017124 0ustar liggesusers #' @rdname get_predict #' @export get_predict.mhurdle <- function(model, newdata = insight::get_data(model), type = "response", ...) { out <- stats::predict(model, what = type, newdata = newdata) out <- data.frame(rowid = seq_len(length(out)), estimate = out) return(out) } #' @rdname get_vcov #' @export get_vcov.mhurdle <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !is.logical(vcov)) { insight::format_error("The `vcov` for this class of models must be TRUE or FALSE.") } out <- try(stats::vcov(model), silent = TRUE) if (inherits(out, "try-error")) { out <- tryCatch(model[["vcov"]], error = function(e) NULL) } return(out) } marginaleffects/R/hypotheses_joint.R0000644000176200001440000001101514631452335017332 0ustar liggesusersjoint_test <- function(object, joint_index = NULL, hypothesis = 0, joint_test = "f", df = NULL) { checkmate::assert_choice(joint_test, c("f", "chisq")) if (joint_test == "f") { checkmate::assert_numeric(df, len = 2, null.ok = TRUE) } else { checkmate::assert_numeric(df, len = 1, null.ok = TRUE) } # theta_hat: P x 1 vector of estimated parameters if (inherits(object, c("slopes", "comparisons"))) { nam <- object$term if ("contrast" %in% names(object)) { nam <- paste(nam, object$contrast) } theta_hat <- stats::setNames(object$estimate, nam) } else { theta_hat <- get_coef(object) } # index checkmate::assert( checkmate::check_integerish(joint_index, lower = 1, upper = length(theta_hat)), checkmate::check_character(joint_index), checkmate::check_true(joint_index) ) if (isTRUE(joint_index)) { joint_index <- seq_along(theta_hat) } else if (isTRUE(checkmate::check_string(joint_index))) { joint_index <- grep(joint_index, names(theta_hat), perl = TRUE) } # V_hat: estimated covariance matrix V_hat <- stats::vcov(object) # n: sample size n <- tryCatch(stats::nobs(object), error = function(e) NULL) if (is.null(n)) n <- tryCatch(stats::nobs(attr(object, "model")), error = function(e) NULL) if (is.null(n)) insight::format_error("Could not extract sample size from model object.") # R: Q x P matrix for testing Q hypotheses on P parameters # build R matrix based on joint_index R <- matrix(0, nrow = length(joint_index), ncol = length(theta_hat)) for (i in seq_along(joint_index)) { if (is.numeric(joint_index)) { R[i, joint_index[i]] <- 1 } else { R[i, which(names(theta_hat) == joint_index[i])] <- 1 } } # null hypothesis checkmate::assert( checkmate::check_number(hypothesis), checkmate::check_numeric(hypothesis, len = nrow(R)), checkmate::check_null(hypothesis) ) if (is.null(hypothesis)) hypothesis <- 0 r <- matrix(hypothesis, nrow = nrow(R), ncol = 1) # Calculate the difference between R*theta_hat and r diff <- R %*% theta_hat - r # Calculate the inverse of R*(V_hat/n)*R' inv <- solve(R %*% V_hat %*% t(R)) # Calculate the Wald test statistic if (joint_test == "f") { wald_statistic <- t(diff) %*% inv %*% diff / dim(R)[1] # Q is the number of rows in R } else if (joint_test == "chisq") { wald_statistic <- t(diff) %*% inv %*% diff # Not normalized for chi-squared joint_test } # Degrees of freedom if (is.null(df)) { if (inherits(object, "lme")) { msg <- "The `hypotheses()` functions uses simple heuristics to select degrees of freedom for this test. See the relevant section in `?hypotheses`. These rules are likely to yield inappropriate results for models of class `%s`. Please supply degrees of freedom values explicitly via the `df` argument." msg <- sprintf(msg, class(object)[1]) insight::format_warning(msg) } df1 <- dim(R)[1] # Q df2 <- tryCatch(insight::get_df(attr(object, "model")), error = function(e) NULL) if (is.null(df2)) tryCatch(insight::get_df(object), error = function(e) NULL) if (is.null(df2)) df2 <- n - length(theta_hat) # n - P } else { df1 <- df[1] df2 <- df[2] } # Calculate the p-value if (joint_test == "f") { p_value <- 1 - stats::pf(wald_statistic, df1, df2) } else if (joint_test == "chisq") { p_value <- 1 - stats::pchisq(wald_statistic, df1) df2 <- NULL } # Return the Wald joint_test statistic and p-value out <- data.frame(statistic = drop(wald_statistic), p.value = drop(p_value)) class(out) <- c("hypotheses", "data.frame") if (joint_test == "f") { attr(out, "statistic_label") <- "F" } else if (joint_test == "chisq") { attr(out, "statistic_label") <- "ChiSq" } # degrees of freedom print if (joint_test == "f") { out$df1 <- df1 out$df2 <- df2 } else { out$df <- df1 } # Create the print_head string print_head <- "\nJoint hypothesis test:\n" if (is.character(joint_index)) { for (i in joint_index) { print_head <- paste0(print_head, i, sprintf(" = %s\n", hypothesis)) } } else if (inherits(object, c("marginaleffects", "comparisons", "slopes", "marginal_means"))) { tmp <- paste0(get_term_labels(object, joint_index), sprintf(" = %s\n", hypothesis)) print_head <- c(print_head, tmp) } else { tmp <- paste0(get_term_labels(stats::coef(object), joint_index), sprintf(" = %s\n", hypothesis)) print_head <- c(print_head, tmp) } attr(out, "print_head") <- print_head return(out) } marginaleffects/R/hush.R0000644000176200001440000000052214631452335014704 0ustar liggesusers# Execute code silently # Do not export to avoid conflict with modelsummary hush <- function(code) { void <- utils::capture.output({ out <- invisible( suppressMessages( suppressWarnings( tryCatch(code, error = function(e) NULL)) ) ) }) return(out) } marginaleffects/R/methods_bife.R0000644000176200001440000000127514631452335016373 0ustar liggesusers#' @include get_predict.R #' @rdname get_predict #' @keywords internal #' @export get_predict.bife <- function(model, newdata = insight::get_data(model), type = "response", ...) { pred <- stats::predict(model, X_new = newdata, type = type, ...) sanity_predict_vector(pred = pred, model = model, newdata = newdata, type = type) sanity_predict_numeric(pred = pred, model = model, newdata = newdata, type = type) out <- data.frame( rowid = seq_len(nrow(newdata)), estimate = pred) return(out) } marginaleffects/R/sanity_by.R0000644000176200001440000000312114631452335015734 0ustar liggesuserssanity_by <- function(by, newdata) { checkmate::assert( checkmate::check_flag(by), checkmate::check_data_frame(by, min.cols = 1, min.rows = 1), checkmate::check_character(by, min.len = 1), checkmate::check_null(by)) known <- c("by", "group", "term", "rowid", "rowidcf", "contrast", colnames(newdata)) if (isTRUE(by == "group") && "group" %in% colnames(newdata)) { msg <- 'The "group" variable name is forbidden to avoid conflicts with the column names of the outputs produced by the `marginaleffects` package. Please rename your variable of change the value of the `by` argument.' insight::format_error(msg) } if (isTRUE(checkmate::check_flag(by))) { flag <- FALSE } else if (inherits(by, "data.frame")) { flag <- !all(colnames(by) %in% known) || !"by" %in% colnames(by) } else { flag <- !all(by %in% known | grepl("^contrast_", by)) } if (flag) { bycols <- paste(setdiff(colnames(newdata), c("rowid", "rowidcf", "term", "group")), collapse = ", ") msg <- c( "The `by` argument must be either:", "", sprintf("1. Character vector in which each element is part of: %s", bycols), "", sprintf("2. A data frame with a `by` column of labels, and in which all other columns are elements of: %s", bycols), "", "It can sometimes be useful to supply a data frame explicitly to the `newdata` argument in order to be able to group by different columns." ) stop(insight::format_message(msg), call. = FALSE) } }marginaleffects/R/slopes.R0000644000176200001440000004671314632657466015273 0ustar liggesusers#' Slopes (aka Partial derivatives, Marginal Effects, or Trends) #' #' @description #' Partial derivative of the regression equation with respect to a regressor of interest. #' #' * `slopes()`: unit-level (conditional) estimates. #' * `avg_slopes()`: average (marginal) estimates. #' #' The `newdata` argument and the `datagrid()` function can be used to control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. #' #' See the slopes vignette and package website for worked examples and case studies: #' #' * #' * #' #' @details #' A "slope" or "marginal effect" is the partial derivative of the regression equation #' with respect to a variable in the model. This function uses automatic #' differentiation to compute slopes for a vast array of models, #' including non-linear models with transformations (e.g., polynomials). #' Uncertainty estimates are computed using the delta method. #' #' @param model Model object #' @param variables Focal variables #' * `NULL`: compute slopes or comparisons for all the variables in the model object (can be slow). #' * Character vector: subset of variables (usually faster). #' @param newdata Grid of predictor values at which we evaluate the slopes. #' + Warning: Please avoid modifying your dataset between fitting the model and calling a `marginaleffects` function. This can sometimes lead to unexpected results. #' + `NULL` (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using [insight::get_data()], which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. #' + [datagrid()] call to specify a custom grid of regressors. For example: #' - `newdata = datagrid(cyl = c(4, 6))`: `cyl` variable equal to 4 and 6 and other regressors fixed at their means or modes. #' - See the Examples section and the [datagrid()] documentation. #' + [subset()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = subset(treatment == 1)` #' + [dplyr::filter()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = filter(treatment == 1)` #' + string: #' - "mean": Slopes evaluated when each predictor is held at its mean or mode. #' - "median": Slopes evaluated when each predictor is held at its median or mode. #' - "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. #' - "tukey": Slopes evaluated at Tukey's 5 numbers. #' - "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). #' @param vcov Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: #' * FALSE: Do not compute standard errors. This can speed up computation considerably. #' * TRUE: Unit-level standard errors using the default `vcov(model)` variance-covariance matrix. #' * String which indicates the kind of uncertainty estimates to return. #' - Heteroskedasticity-consistent: `"HC"`, `"HC0"`, `"HC1"`, `"HC2"`, `"HC3"`, `"HC4"`, `"HC4m"`, `"HC5"`. See `?sandwich::vcovHC` #' - Heteroskedasticity and autocorrelation consistent: `"HAC"` #' - Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" #' - Other: `"NeweyWest"`, `"KernHAC"`, `"OPG"`. See the `sandwich` package documentation. #' * One-sided formula which indicates the name of cluster variables (e.g., `~unit_id`). This formula is passed to the `cluster` argument of the `sandwich::vcovCL` function. #' * Square covariance matrix #' * Function which returns a covariance matrix (e.g., `stats::vcov(model)`) #' @param conf_level numeric value between 0 and 1. Confidence level to use to build a confidence interval. #' @param type string indicates the type (scale) of the predictions used to #' compute contrasts or slopes. This can differ based on the model #' type, but will typically be a string such as: "response", "link", "probs", #' or "zero". When an unsupported string is entered, the model-specific list of #' acceptable values is returned in an error message. When `type` is `NULL`, the #' first entry in the error message is used by default. #' @param slope string indicates the type of slope or (semi-)elasticity to compute: #' - "dydx": dY/dX #' - "eyex": dY/dX * Y / X #' - "eydx": dY/dX * Y #' - "dyex": dY/dX / X #' - Y is the predicted value of the outcome; X is the observed value of the predictor. #' @param wts logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in `avg_*()` or with the `by` argument, and not unit-level estimates. See `?weighted.mean` #' + string: column name of the weights variable in `newdata`. When supplying a column name to `wts`, it is recommended to supply the original data (including the weights variable) explicitly to `newdata`. #' + numeric: vector of length equal to the number of rows in the original data or in `newdata` (if supplied). #' + FALSE: Equal weights. #' + TRUE: Extract weights from the fitted object with `insight::find_weights()` and use them when taking weighted averages of estimates. Warning: `newdata=datagrid()` returns a single average weight, which is equivalent to using `wts=FALSE` #' @param hypothesis specify a hypothesis test or custom contrast using a numeric value, vector, or matrix; a string equation; string; a formula, or a function. #' + Numeric: #' - Single value: the null hypothesis used in the computation of Z and p (before applying `transform`). #' - Vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the `hypothesis` argument. #' - Matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. #' + String equation to specify linear or non-linear hypothesis tests. If the `term` column uniquely identifies rows, terms can be used in the formula. Otherwise, use `b1`, `b2`, etc. to identify the position of each parameter. The `b*` wildcard can be used to test hypotheses on all estimates. If a named vector is used, the names are used as labels in the output. Examples: #' - `hp = drat` #' - `hp + drat = 12` #' - `b1 + b2 + b3 = 0` #' - `b* / b1 = 1` #' + String: #' - "pairwise": pairwise differences between estimates in each row. #' - "reference": differences between the estimates in each row and the estimate in the first row. #' - "sequential": difference between an estimate and the estimate in the next row. #' - "meandev": difference between an estimate and the mean of all estimates. #' - "meanotherdev": difference between an estimate and the mean of all other estimates, excluding the current one. #' - "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. #' + Formula: #' - `comparison ~ pairs | group` #' - Left-hand side determines the type of comparison to conduct: `difference` or `ratio`. If the left-hand side is empty, `difference` is chosen. #' - Right-hand side determines the pairs of estimates to compare: `reference`, `sequential`, or `meandev` #' - Optional: Users can supply grouping variables after a vertical bar to conduct comparisons withing subsets. #' - Examples: #' + `~ reference` #' + `ratio ~ pairwise` #' + `difference ~ pairwise | groupid` #' + Function: #' - Accepts an argument `x`: object produced by a `marginaleffects` function or a data frame with column `rowid` and `estimate` #' - Returns a data frame with columns `term` and `estimate` (mandatory) and `rowid` (optional). #' - The function can also accept optional input arguments: `newdata`, `by`, `draws`. #' - This function approach will not work for Bayesian models or with bootstrapping. In those cases, it is easy to use `posterior_draws()` to extract and manipulate the draws directly. #' + See the Examples section below and the vignette: https://marginaleffects.com/vignettes/hypothesis.html #' @param p_adjust Adjust p-values for multiple comparisons: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", or "fdr". See [stats::p.adjust] #' @param df Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and `Inf`. When `df` is `Inf`, the normal distribution is used. When `df` is finite, the `t` distribution is used. See [insight::get_df] for a convenient function to extract degrees of freedom. Ex: `slopes(model, df = insight::get_df(model))` #' @param eps NULL or numeric value which determines the step size to use when #' calculating numerical derivatives: (f(x+eps)-f(x))/eps. When `eps` is #' `NULL`, the step size is 0.0001 multiplied by the difference between #' the maximum and minimum values of the variable with respect to which we #' are taking the derivative. Changing `eps` may be necessary to avoid #' numerical problems in certain models. #' @param numderiv string or list of strings indicating the method to use to for the numeric differentiation used in to compute delta method standard errors. #' + "fdforward": finite difference method with forward differences #' + "fdcenter": finite difference method with central differences (default) #' + "richardson": Richardson extrapolation method #' + Extra arguments can be specified by passing a list to the `numDeriv` argument, with the name of the method first and named arguments following, ex: `numderiv=list("fdcenter", eps = 1e-5)`. When an unknown argument is used, `marginaleffects` prints the list of valid arguments for each method. #' @param ... Additional arguments are passed to the `predict()` method #' supplied by the modeling package.These arguments are particularly useful #' for mixed-effects or bayesian models (see the online vignettes on the #' `marginaleffects` website). Available arguments can vary from model to #' model, depending on the range of supported arguments by each modeling #' package. See the "Model-Specific Arguments" section of the #' `?slopes` documentation for a non-exhaustive list of available #' arguments. #' @inheritParams comparisons #' #' @details #' Numerical derivatives for the `slopes` function are calculated #' using a simple epsilon difference approach: \eqn{\partial Y / \partial X = (f(X + \varepsilon/2) - f(X-\varepsilon/2)) / \varepsilon}{dY/dX = (f(X + e/2) - f(X-e/2)) / e}, #' where f is the `predict()` method associated with the model class, and #' \eqn{\varepsilon}{e} is determined by the `eps` argument. #' @template deltamethod #' @template model_specific_arguments #' @template bayesian #' @template equivalence #' @template type #' @template parallel #' @template references #' @template order_of_operations #' @template options #' #' @return A `data.frame` with one row per observation (per term/group) and several columns: #' * `rowid`: row number of the `newdata` data frame #' * `type`: prediction type, as defined by the `type` argument #' * `group`: (optional) value of the grouped outcome (e.g., categorical outcome models) #' * `term`: the variable whose marginal effect is computed #' * `dydx`: slope of the outcome with respect to the term, for a given combination of predictor values #' * `std.error`: standard errors computed by via the delta method. #' * `p.value`: p value associated to the `estimate` column. The null is determined by the `hypothesis` argument (0 by default), and p values are computed before applying the `transform` argument. For models of class `feglm`, `Gam`, `glm` and `negbin`, p values are computed on the link scale by default unless the `type` argument is specified explicitly. #' * `s.value`: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). #' * `conf.low`: lower bound of the confidence interval (or equal-tailed interval for bayesian models) #' * `conf.high`: upper bound of the confidence interval (or equal-tailed interval for bayesian models) #' #' See `?print.marginaleffects` for printing options. #' #' @examplesIf interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true") #' @examples #' # Unit-level (conditional) Marginal Effects #' mod <- glm(am ~ hp * wt, data = mtcars, family = binomial) #' mfx <- slopes(mod) #' head(mfx) #' #' # Average Marginal Effect (AME) #' avg_slopes(mod, by = TRUE) #' #' #' # Marginal Effect at the Mean (MEM) #' slopes(mod, newdata = datagrid()) #' #' # Marginal Effect at User-Specified Values #' # Variables not explicitly included in `datagrid()` are held at their means #' slopes(mod, newdata = datagrid(hp = c(100, 110))) #' #' # Group-Average Marginal Effects (G-AME) #' # Calculate marginal effects for each observation, and then take the average #' # marginal effect within each subset of observations with different observed #' # values for the `cyl` variable: #' mod2 <- lm(mpg ~ hp * cyl, data = mtcars) #' avg_slopes(mod2, variables = "hp", by = "cyl") #' #' # Marginal Effects at User-Specified Values (counterfactual) #' # Variables not explicitly included in `datagrid()` are held at their #' # original values, and the whole dataset is duplicated once for each #' # combination of the values in `datagrid()` #' mfx <- slopes(mod, #' newdata = datagrid( #' hp = c(100, 110), #' grid_type = "counterfactual")) #' head(mfx) #' #' # Heteroskedasticity robust standard errors #' mfx <- slopes(mod, vcov = sandwich::vcovHC(mod)) #' head(mfx) #' #' # hypothesis test: is the `hp` marginal effect at the mean equal to the `drat` marginal effect #' mod <- lm(mpg ~ wt + drat, data = mtcars) #' #' slopes( #' mod, #' newdata = "mean", #' hypothesis = "wt = drat") #' #' # same hypothesis test using row indices #' slopes( #' mod, #' newdata = "mean", #' hypothesis = "b1 - b2 = 0") #' #' # same hypothesis test using numeric vector of weights #' slopes( #' mod, #' newdata = "mean", #' hypothesis = c(1, -1)) #' #' # two custom contrasts using a matrix of weights #' lc <- matrix( #' c( #' 1, -1, #' 2, 3), #' ncol = 2) #' colnames(lc) <- c("Contrast A", "Contrast B") #' slopes( #' mod, #' newdata = "mean", #' hypothesis = lc) #' #' @export slopes <- function(model, newdata = NULL, variables = NULL, type = NULL, by = FALSE, vcov = TRUE, conf_level = 0.95, slope = "dydx", wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ...) { dots <- list(...) # very early, before any use of newdata # if `newdata` is a call to `typical` or `counterfactual`, insert `model` scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) # build call: match.call() doesn't work well in *apply() call_attr <- c( list( name = "slopes", model = model, newdata = newdata, variables = variables, type = type, vcov = vcov, by = by, conf_level = conf_level, slope = slope, wts = wts, hypothesis = hypothesis, df = df, eps = eps), list(...)) call_attr <- do.call("call", call_attr) # slopes() does not support a named list of variables like comparisons() checkmate::assert_character(variables, null.ok = TRUE) # slope valid <- c("dydx", "eyex", "eydx", "dyex", "dydxavg", "eyexavg", "eydxavg", "dyexavg") checkmate::assert_choice(slope, choices = valid) # sanity checks and pre-processing model <- sanitize_model(model = model, newdata = newdata, wts = wts, vcov = vcov, by = by, calling_function = "marginaleffects", ...) sanity_dots(model = model, calling_function = "marginaleffects", ...) type <- sanitize_type(model = model, type = type, calling_function = "slopes") ############### sanity checks are over # Bootstrap out <- inferences_dispatch( INF_FUN = slopes, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type, conf_level = conf_level, by = by, wts = wts, slope = slope, hypothesis = hypothesis, ...) if (!is.null(out)) { return(out) } out <- comparisons( model, newdata = newdata, variables = variables, vcov = vcov, conf_level = conf_level, type = type, wts = wts, hypothesis = hypothesis, equivalence = equivalence, df = df, p_adjust = p_adjust, by = by, eps = eps, numderiv = numderiv, comparison = slope, cross = FALSE, # secret arguments internal_call = TRUE, ...) data.table::setDT(out) attr(out, "vcov.type") <- get_vcov_label(vcov) attr(out, "newdata") <- newdata # recall attr(out, "call") <- call_attr # class data.table::setDF(out) class(out) <- setdiff(class(out), "comparisons") class(out) <- c("slopes", "marginaleffects", class(out)) return(out) } #' Average slopes (aka Average partial derivatives, marginal effects, or trends) #' @describeIn slopes Average slopes #' @export #' avg_slopes <- function(model, newdata = NULL, variables = NULL, type = NULL, by = TRUE, vcov = TRUE, conf_level = 0.95, slope = "dydx", wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ...) { # order of the first few paragraphs is important # if `newdata` is a call to `typical` or `counterfactual`, insert `model` # should probably not be nested too deeply in the call stack since we eval.parent() (not sure about this) scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) # Bootstrap out <- inferences_dispatch( INF_FUN = avg_slopes, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type, conf_level = conf_level, by = by, wts = wts, slope = slope, hypothesis = hypothesis, ...) if (!is.null(out)) { return(out) } out <- slopes( model = model, newdata = newdata, variables = variables, type = type, vcov = vcov, conf_level = conf_level, by = by, slope = slope, wts = wts, hypothesis = hypothesis, equivalence = equivalence, p_adjust = p_adjust, df = df, eps = eps, numderiv = numderiv, ...) return(out) } marginaleffects/R/tinytest.R0000644000176200001440000001235714631452335015631 0ustar liggesusers#' `tinytest` helper #' #' @export #' @keywords internal expect_slopes <- function( object, n_unique = NULL, pct_na = 5, se = TRUE, ...) { insight::check_if_installed("tinytest") object <- hush(slopes(object, ...)) diff <- "" # class fail_class <- !isTRUE(checkmate::check_class(object, "slopes")) if (fail_class) { msg <- sprintf("Wrong class: `%s`.", class(object)[1]) diff <- c(diff, msg) } # tidy() tid <- hush(tidy(object)) fail_tidy <- !isTRUE(checkmate::check_data_frame(tid)) if (fail_tidy) { msg <- "tidy() failed to return a data frame." diff <- c(diff, msg) } # na fail_na <- isTRUE(hush(mean(is.na(object$estimate)) * 100 > pct_na)) if (fail_na) { msg <- sprintf("More than %s of missing values.", pct_na) diff <- c(diff, msg) } # unique fail_unique <- isTRUE(hush(length(unique(object$estimate)) < n_unique - 1)) if (fail_unique) { msg <- sprintf("Fewer than %s unique values.", n_unique) diff <- c(diff, msg) } # unique if (isTRUE(se) && !"std.error" %in% colnames(object)) { msg <- sprintf("Fewer than %s unique values.", n_unique) diff <- c(diff, msg) fail_se <- TRUE } else { fail_se <- FALSE } # diff message diff <- paste(diff, collapse = "\n") # pass/fail fail <- fail_class || fail_tidy || fail_na || fail_unique || fail_se # tinytest object out <- tinytest::tinytest( result = !fail, call = sys.call(sys.parent(1)), diff = diff) return(out) } #' `tinytest` helper #' #' @export #' @keywords internal expect_predictions <- function(object, se = TRUE, n_row = NULL, n_col = NULL) { insight::check_if_installed("tinytest") diff <- "" # class fail_class <- !isTRUE(checkmate::check_class(object, "predictions")) if (fail_class) { msg <- sprintf("Wrong class: `%s`.", class(object)[1]) diff <- c(diff, msg) } # std.error if (isTRUE(se) && !"std.error" %in% colnames(object)) { msg <- "No standard error." diff <- c(diff, msg) fail_se <- TRUE } else { fail_se <- FALSE } # rows and cols if (isTRUE(n_row > nrow(object))) { msg <- sprintf("Number of rows: %s", nrow(object)) diff <- c(diff, msg) fail_row <- TRUE } else { fail_row <- FALSE } if (isTRUE(n_col > ncol(object))) { msg <- sprintf("Number of columns: %s", ncol(object)) diff <- c(diff, msg) fail_col <- TRUE } else { fail_col <- FALSE } # diff message diff <- paste(diff, collapse = "\n") # pass/fail fail <- fail_class || fail_se || fail_row || fail_col # tinytest object out <- tinytest::tinytest( result = !fail, call = sys.call(sys.parent(1)), diff = diff) return(out) } #' `tinytest` helper #' #' @export #' @keywords internal expect_margins <- function(results, margins_object, se = TRUE, tolerance = 1e-5, verbose = FALSE) { insight::check_if_installed("tinytest") is_equal <- function(x, y) { all(abs((x - y) / x) < tolerance) } results$type <- NULL margins_object <- data.frame(margins_object) term_names <- unique(results$term) flag <- TRUE # dydx for (tn in term_names) { unknown <- results[results$term == tn, "estimate"] lab <- paste0("dydx_", tn) if (lab %in% colnames(margins_object)) { known <- as.numeric(margins_object[, lab]) tmp <- is_equal(known, unknown) if (isFALSE(tmp)) { flag <- FALSE if (isTRUE(verbose)) print(sprintf("dydx: %s", tn)) } } } # std.error if (isTRUE(se) && "std.error" %in% colnames(results)) { for (tn in term_names) { lab_se <- paste0("SE_dydx_", tn) lab_var <- paste0("Var_dydx_", tn) if (lab_se %in% colnames(margins_object)) { unknown <- results[results$term == tn, "std.error"] known <- as.numeric(margins_object[, lab_se]) tmp <- is_equal(known, unknown) if (isFALSE(tmp)) { flag <- FALSE if (isTRUE(verbose)) print(sprintf("se: %s", tn)) } } else if (lab_var %in% colnames(margins_object)) { unknown <- results[results$term == tn, "std.error"] known <- sqrt(as.numeric(margins_object[, lab_var])) tmp <- is_equal(known, unknown) if (isFALSE(tmp)) { flag <- FALSE if (isTRUE(verbose)) print(sprintf("Var: %s", tn)) } } else { flag <- FALSE if (isTRUE(verbose)) print(sprintf("missing column: %s", lab)) } } } # tinytest object out <- tinytest::tinytest( result = flag, call = sys.call(sys.parent(1)), diff = diff) return(out) }marginaleffects/R/methods_dataframe.R0000644000176200001440000000122514631452335017405 0ustar liggesusers#' @include get_coef.R #' @rdname get_coef #' @export get_coef.data.frame <- function(model, ...) { checkmate::assert_data_frame(model) if (!"estimate" %in% colnames(model)) { insight::format_error("The model object is a data.frame but doesn't contain the column 'estimate'. Make sure these columns are present") } out <- model$estimate if ("term" %in% colnames(model)) { names(out) <- model$term } else { names(out) <- seq_along(out) } return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.data.frame <- function(model, coefs, ...) { model$estimate = coefs return(model) } marginaleffects/R/methods_brms.R0000644000176200001440000001104114631452335016421 0ustar liggesusers#' @include sanity_model.R #' @rdname sanitize_model_specific #' @export sanitize_model_specific.brmsfit <- function(model, ...) { insight::check_if_installed("collapse", minimum_version = "1.9.0") # terms: brmsfit objects do not have terms immediately available te <- tryCatch(attr(stats::terms(stats::formula(model)$formula), "term.labels"), error = function(e) NULL) if (any(grepl("^factor\\(", te))) { stop("The `factor()` function cannot be used in the model formula of a `brmsfit` model. Please convert your variable to a factor before fitting the model, or use the `mo()` function to specify monotonic variables (see the `brms` vignette on monotonic variables).", call. = FALSE) } return(model) } #' @rdname get_coef #' @export get_coef.brmsfit <- function(model, ...) { out <- insight::get_parameters(model) out <- collapse::dapply(out, MARGIN = 2, FUN = collapse::fmedian) return(out) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.brmsfit <- function(model, newdata = insight::get_data(model), type = "response", ...) { checkmate::assert_choice(type, choices = c("response", "link", "prediction", "average")) if (type == "link") { insight::check_if_installed("rstantools") draws <- rstantools::posterior_linpred( model, newdata = newdata, ...) } else if (type == "response") { insight::check_if_installed("rstantools") draws <- rstantools::posterior_epred( model, newdata = newdata, ...) } else if (type == "prediction") { insight::check_if_installed("rstantools") draws <- rstantools::posterior_predict( model, newdata = newdata, ...) } else if (type == "average") { insight::check_if_installed("brms") draws <- brms::pp_average( model, newdata = newdata, summary = FALSE, ...) } if ("rowid_internal" %in% colnames(newdata)) { idx <- newdata[["rowid_internal"]] } else if ("rowid" %in% colnames(newdata)) { idx <- newdata[["rowid"]] } else { idx <- 1:nrow(newdata) } # resp_subset sometimes causes dimension mismatch if (length(dim(draws)) == 2 && nrow(newdata) != ncol(draws)) { msg <- sprintf("Dimension mismatch: There are %s parameters in the posterior draws but %s observations in `newdata` (or the original dataset).", ncol(draws), nrow(newdata)) insight::format_error(msg) } # 1d outcome if (length(dim(draws)) == 2) { med <- collapse::dapply(draws, MARGIN = 2, FUN = collapse::fmedian) out <- data.frame( rowid = idx, group = "main_marginaleffect", estimate = med) # multi-dimensional outcome } else if (length(dim(draws)) == 3) { out <- apply(draws, c(2, 3), stats::median) levnames <- dimnames(draws)[[3]] if (is.null(levnames)) { colnames(out) <- seq_len(ncol(out)) } else { colnames(out) <- levnames } out <- data.frame( rowid = rep(idx, times = ncol(out)), group = rep(colnames(out), each = nrow(out)), estimate = c(out)) out$group <- group_to_factor(out$group, model) } else { stop("marginaleffects cannot extract posterior draws from this model. Please report this problem to the Bug tracker with a reporducible example: https://github.com/vincentarelbundock/marginaleffects/issues", call. = FALSE) } # group for multi-valued outcome if (length(dim(draws)) == 3) { draws <- lapply(1:dim(draws)[3], function(i) draws[, , i]) draws <- do.call("cbind", draws) } attr(out, "posterior_draws") <- t(draws) return(out) } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.brmsfit <- function(model, ...) { if (!is.null(model$family) && "cumulative" %in% model$family) { out <- unique(insight::get_response(model)) } else { out <- "main_marginaleffect" } return(out) } #' @rdname get_vcov #' @export get_vcov.brmsfit <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !is.logical(vcov)) { insight::format_warning("The `vcov` argument is not supported for models of this class.") } return(NULL) } marginaleffects/R/methods_Rchoice.R0000644000176200001440000000031714631452335017036 0ustar liggesusers#' @rdname set_coef #' @export set_coef.hetprob <- function(model, coefs, ...) { model[["estimate"]][names(coefs)] <- coefs model } #' @rdname set_coef #' @export set_coef.ivpml <- set_coef.hetprob marginaleffects/R/sanity.R0000644000176200001440000000467114631452335015255 0ustar liggesuserssanity_equivalence_p_adjust <- function(equivalence, p_adjust) { if (!is.null(equivalence) && !is.null(p_adjust)) { insight::format_error("The `equivalence` and `p_adjust` arguments cannot be used together.") } checkmate::assert_choice(p_adjust, choices = stats::p.adjust.methods, null.ok = TRUE) } sanity_df <- function(df, x) { checkmate::assert( checkmate::check_number(df, lower = 1), checkmate::check_numeric(df, len = nrow(x))) } sanity_predict_vector <- function(pred, model, newdata, type) { if (!isTRUE(checkmate::check_atomic_vector(pred)) && !isTRUE(checkmate::check_array(pred, d = 1))) { msg <- sprintf( '`predict(model, type = "%s")` was called on a model of class `%s`, but this command did not produce the expected outcome: A numeric vector of length %s. This can sometimes happen when users try compute a marginal effect for some models with grouped or multivariate outcome which are not supported yet by `marginaleffects` package. Please consult your modeling package documentation to learn what alternative `type` arguments are accepted by the `predict` method, or file a feature request on Github: https://github.com/vincentarelbundock/marginaleffects/issues', type, class(model)[1], nrow(newdata)) stop(msg, call. = FALSE) } } sanity_predict_numeric <- function(pred, model, newdata, type) { if (!isTRUE(checkmate::check_numeric(pred))) { msg <- sprintf( '`predict(model, type = "%s")` was called on a model of class `%s`, but this command did not produce the expected outcome: A numeric vector of length %s. This can sometimes happen when users try compute a marginal effect for an outcome type which is unsupported, or which cannot be differentiated. Please consult your modeling package documentation to learn what alternative `type` arguments are accepted by the `predict` method.', type, class(model)[1], nrow(newdata)) stop(msg, call. = FALSE) } } # OBSOLETE CHECKS KEPT FOR POSTERITY # sanitize_return_data <- function() { # return_data <- getOption("marginaleffects_return_data", default = TRUE) # checkmate::assert_flag(return_data) # return(return_data) # } # sanitize_numDeriv_method <- function() { # numDeriv_method <- getOption("marginaleffects_numDeriv_method", default = "simple") # checkmate::assert_choice(numDeriv_method, choices = c("simple", "complex", "Richardson")) # return(numDeriv_method) # } marginaleffects/R/plot_build.R0000644000176200001440000001277214631452335016104 0ustar liggesusersplot_preprocess <- function(dat, v_x, v_color = NULL, v_facet_1 = NULL, v_facet_2 = NULL, condition = NULL, modeldata = NULL) { for (v in names(condition$condition)) { fun <- function(x, lab) { idx <- match(x, sort(unique(x))) factor(lab[idx], levels = lab) } if (identical(condition$condition[[v]], "threenum")) { dat[[v]] <- fun(dat[[v]], c("-SD", "Mean", "+SD")) } else if (identical(condition$condition[[v]], "minmax")) { dat[[v]] <- fun(dat[[v]], c("Min", "Max")) } else if (identical(condition$condition[[v]], "quartile")) { dat[[v]] <- fun(dat[[v]], c("Q1", "Q2", "Q3")) } } if (get_variable_class(modeldata, v_x, "categorical")) { dat[[v_x]] <- factor(dat[[v_x]]) } # colors, linetypes, and facets are categorical attributes if (isTRUE(v_color %in% colnames(dat))) { dat[[v_color]] <- factor(dat[[v_color]]) } if (isTRUE(v_facet_1 %in% colnames(dat))) { dat[[v_facet_1]] <- factor(dat[[v_facet_1]]) } if (isTRUE(v_facet_2 %in% colnames(dat))) { dat[[v_facet_2]] <- factor(dat[[v_facet_2]]) } return(dat) } plot_build <- function( dat, v_x, v_color = NULL, v_facet_1 = NULL, v_facet_2 = NULL, dv = NULL, modeldata = NULL, points = 0, rug = FALSE, gray = FALSE) { checkmate::assert_flag(rug) checkmate::assert_flag(gray) # create index before building ggplot to make sure it is available dat$marginaleffects_term_index <- get_unique_index(dat, term_only = TRUE) multi_variables <- isTRUE(length(unique(dat$marginaleffects_term_index)) > 1) p <- ggplot2::ggplot() if (points > 0 && !get_variable_class(modeldata, v_x, "categorical") && !get_variable_class(modeldata, dv, "categorical")) { if (!is.null(v_color) && get_variable_class(modeldata, v_color, "categorical")) { p <- p + ggplot2::geom_point( data = modeldata, alpha = points, ggplot2::aes(x = .data[[v_x]], y = .data[[dv]], color = factor(.data[[v_color]]))) } else { p <- p + ggplot2::geom_point( data = modeldata, alpha = points, ggplot2::aes(x = .data[[v_x]], y = .data[[dv]])) } } if (isTRUE(rug)) { p <- p + ggplot2::geom_rug(data = modeldata, ggplot2::aes(x = .data[[v_x]])) if (!is.null(dv)) { p <- p + ggplot2::geom_rug(data = modeldata, ggplot2::aes(y = .data[[dv]])) } } aes_args <- list( x = substitute(.data[[v_x]]), y = substitute(estimate) ) if ("conf.low" %in% colnames(dat)) { aes_args$ymin <- substitute(conf.low) aes_args$ymax <- substitute(conf.high) } aes_args_ribbon <- aes_args aes_args_ribbon$fill <- aes_args$color aes_args_ribbon$color <- NULL # discrete x-axis if (is.factor(dat[[v_x]])) { if (!is.null(v_color)) { if (gray) { aes_args$shape <- substitute(factor(.data[[v_color]])) } else { aes_args$color <- substitute(factor(.data[[v_color]])) } } aes_obj <- do.call(ggplot2::aes, aes_args) if ("conf.low" %in% colnames(dat)) { p <- p + ggplot2::geom_pointrange( data = dat, mapping = aes_obj, position = ggplot2::position_dodge(.15)) } else { p <- p + ggplot2::geom_point( data = dat, mapping = aes_obj, position = ggplot2::position_dodge(.15)) } # continuous x-axis } else { if (!is.null(v_color)) { if (gray) { aes_args$linetype <- substitute(factor(.data[[v_color]])) aes_args_ribbon$linetype <- substitute(factor(.data[[v_color]])) } else { aes_args$color <- substitute(factor(.data[[v_color]])) aes_args_ribbon$fill <- substitute(factor(.data[[v_color]])) } } aes_obj_ribbon <- do.call(ggplot2::aes, aes_args_ribbon) aes_args$ymin <- aes_args$ymax <- NULL aes_obj <- do.call(ggplot2::aes, aes_args) if ("conf.low" %in% colnames(dat)) { p <- p + ggplot2::geom_ribbon(data = dat, aes_obj_ribbon, alpha = .1) p <- p + ggplot2::geom_line(data = dat, aes_obj) } p <- p + ggplot2::geom_line(data = dat, aes_obj) } # facets: 3rd and 4th variable and/or multiple effects ## If pass two facets then make facet grid if (!is.null(v_facet_1) && !is.null(v_facet_2)) { fo <- stats::as.formula(paste(v_facet_2, "~", ifelse(multi_variables, "marginaleffects_term_index +", ""), v_facet_1)) p <- p + ggplot2::facet_grid(fo, scales = "free", labeller = function(x){ lapply(ggplot2::label_both(x), gsub, pattern = "marginaleffects_term_index: ", replacement="") }) ## if pass only 1 facet then facet_wrap } else if (!is.null(v_facet_1) && is.null(v_facet_2)) { fo <- stats::as.formula(paste("~", ifelse(multi_variables, "marginaleffects_term_index +", ""), v_facet_1)) p <- p + ggplot2::facet_wrap(fo, scales = "free") } else if (multi_variables) { fo <- stats::as.formula("~ marginaleffects_term_index") p <- p + ggplot2::facet_wrap(fo, scales = "free") } return(p) }marginaleffects/R/get_contrast_data_factor.R0000644000176200001440000002113314631452335020761 0ustar liggesusersget_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$name, "binary")) { levs <- variable$value 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 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$name]] if (any(!variable$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$value[2]) } } tmp <- contrast_categories_processing(first_cross, levs_idx, levs, variable, newdata) lo <- tmp[[1]] hi <- tmp[[2]] original <- tmp[[3]] if (is.factor(newdata[[variable$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) } marginaleffects/R/sanitize_hypothesis.R0000644000176200001440000000225514631452335020047 0ustar liggesuserssanitize_hypothesis <- function(hypothesis, ...) { if (isTRUE(checkmate::check_formula(hypothesis))) { hypothesis <- sanitize_hypothesis_formula(hypothesis) } checkmate::assert( checkmate::check_character(hypothesis, pattern = "="), checkmate::check_choice(hypothesis, choices = c("pairwise", "reference", "sequential", "meandev", "meanotherdev", "revpairwise", "revreference", "revsequential")), checkmate::check_numeric(hypothesis), checkmate::check_matrix(hypothesis), checkmate::check_function(hypothesis), checkmate::check_null(hypothesis)) hnull <- 0 if (isTRUE(checkmate::check_character(hypothesis, pattern = "="))) { out <- paste(gsub("=", "-(", hypothesis), ")") attr(out, "label") <- hypothesis hypothesis <- out } else if (isTRUE(checkmate::check_matrix(hypothesis))) { attr(hypothesis, "label") <- colnames(hypothesis) } else if (isTRUE(checkmate::check_numeric(hypothesis, len = 1))) { hnull <- hypothesis hypothesis <- NULL } out <- list( "hypothesis" = hypothesis, "hypothesis_null" = hnull ) return(out) } marginaleffects/R/posterior_draws.R0000644000176200001440000001024214631452335017163 0ustar liggesusers#' Extract Posterior Draws or Bootstrap Resamples from `marginaleffects` Objects #' #' @param x An object produced by a `marginaleffects` package function, such as `predictions()`, `avg_slopes()`, `hypotheses()`, etc. #' @param shape string indicating the shape of the output format: #' * "long": long format data frame #' * "DxP": Matrix with draws as rows and parameters as columns #' * "PxD": Matrix with draws as rows and parameters as columns #' * "rvar": Random variable datatype (see `posterior` package documentation). #' @return A data.frame with `drawid` and `draw` columns. #' @export posterior_draws <- function(x, shape = "long") { checkmate::assert_choice(shape, choices = c("long", "DxP", "PxD", "rvar")) # tidy.comparisons() sometimes already saves draws in a nice long format draws <- attr(x, "posterior_draws") if (inherits(draws, "posterior_draws")) return(draws) if (is.null(attr(x, "posterior_draws"))) { warning('This object does not include a "posterior_draws" attribute. The `posterior_draws` function only supports bayesian models produced by the `marginaleffects` or `predictions` functions of the `marginaleffects` package.', call. = FALSE) return(x) } if (nrow(draws) != nrow(x)) { stop('The number of parameters in the object does not match the number of parameters for which posterior draws are available.', call. = FALSE) } if (shape %in% c("PxD", "DxP")) { row.names(draws) <- paste0("b", seq_len(nrow(draws))) colnames(draws) <- paste0("draw", seq_len(ncol(draws))) } if (shape == "PxD") { return(draws) } if (shape == "DxP") { return(t(draws)) } if (shape == "rvar") { insight::check_if_installed("posterior") draws <- t(draws) if (!is.null(attr(x, "nchains"))) { x[["rvar"]] <- posterior::rvar(draws, nchains = attr(x, "nchains")) } else { x[["rvar"]] <- posterior::rvar(draws) } return(x) } if (shape == "long") { draws <- data.table(draws) setnames(draws, as.character(seq_len(ncol(draws)))) for (v in colnames(x)) { draws[[v]] <- x[[v]] } out <- melt( draws, id.vars = colnames(x), variable.name = "drawid", value.name = "draw") cols <- unique(c("drawid", "draw", "rowid", colnames(out))) cols <- intersect(cols, colnames(out)) setcolorder(out, cols) data.table::setDF(out) return(out) } } average_draws <- function(data, index, draws, byfun = NULL) { insight::check_if_installed("collapse", minimum_version = "1.9.0") w <- data[["marginaleffects_wts_internal"]] if (all(is.na(w))) { w <- NULL } if (is.null(index)) { index <- intersect(colnames(data), "type") } if (length(index) > 0) { g <- collapse::GRP(data, by = index) if (is.null(byfun)) { draws <- collapse::fmean( draws, g = g, w = w, drop = FALSE) } else { draws <- collapse::BY( draws, g = g, FUN = byfun, drop = FALSE) } out <- data.table( g[["groups"]], average = collapse::dapply(draws, MARGIN = 1, FUN = collapse::fmedian)) } else { if (is.null(byfun)) { draws <- collapse::fmean( draws, w = w, drop = FALSE) } else { draws <- collapse::BY( draws, g = g, FUN = byfun, drop = FALSE) } out <- data.table(average = collapse::dapply(draws, MARGIN = 1, FUN = collapse::fmedian)) } setnames(out, old = "average", new = "estimate") attr(out, "posterior_draws") <- draws return(out) } #' `posteriordraws()` is an alias to `posterior_draws()` #' #' This alias is kept for backward compatibility and because some users may prefer that name. #' #' @inherit posterior_draws #' @keywords internal #' @export posteriordraws <- posterior_drawsmarginaleffects/R/methods_aod.R0000644000176200001440000000256014631452335016227 0ustar liggesusers#' @rdname get_predict #' @export get_predict.glimML <- function(model, newdata = insight::get_data(model), type = "response", ...) { insight::check_if_installed("aod") out <- aod::predict(model, newdata = newdata, type = type, ...) out <- data.frame( rowid = 1:nrow(newdata), estimate = out) return(out) } #' @rdname set_coef #' @export set_coef.glimML <- function(model, coefs, ...) { # in basic model classes coefficients are named vector model@fixed.param[names(coefs)] <- coefs model } #' @rdname get_vcov #' @export get_vcov.glimML <- function(model, vcov = NULL, ...) { insight::check_if_installed("aod") if (!is.null(vcov) && !is.logical(vcov)) { stop("The `vcov` argument is not supported for this kind of model.") } aod::vcov(model) } #' @rdname sanitize_model_specific sanitize_model_specific.glimML <- function(model, ...) { mdat <- get_modeldata(model, additional_variables = FALSE) if (isTRUE("character" %in% attr(mdat, "marginaleffects_variable_class"))) { insight::format_error("This function does not support character predictors. Please convert them to factors before fitting the model.") } return(model) } marginaleffects/R/methods_pscl.R0000644000176200001440000000236514631452335016430 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.hurdle <- function(model, coefs, ...) { # in pscl::hurdle, coefficients are held in a named list: # model$coefficients. Each element of the list is a vector which # corresponds to one equation (e.g., "zero" or "response"). When calling # "coef(model)", the equation label is prefixed to the term name with an # underscore. out <- model for (lab in names(out$coefficients)) { idx <- paste0(lab, "_", names(out$coefficients[[lab]])) idx <- match(idx, names(coefs)) # probably too conservative if (anyNA(idx)) { stop("Mismatched coefficients names. Please check the `marginaleffects::`set_coef.hurdle` or `set_coef.zeroinfl` function.", call. = FALSE) } out$coefficients[[lab]] <- stats::setNames(coefs[idx], names(out$coefficients[[lab]])) } return(out) } #' @rdname set_coef #' @export set_coef.zeroinfl <- set_coef.hurdle #' @rdname get_group_names #' @export get_group_names.hurdle <- function(model, type = "count", ...) { if (type == "prob") { out <- colnames(stats::predict(model, type = "prob")) } else { out <- "main_marginaleffect" } return(out) } marginaleffects/R/predictions.R0000644000176200001440000006621214632657453016301 0ustar liggesusers#' Predictions #' #' @description #' Outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels (a.k.a. "reference grid"). #' #' * `predictions()`: unit-level (conditional) estimates. #' * `avg_predictions()`: average (marginal) estimates. #' #' The `newdata` argument and the `datagrid()` function can be used to control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. #' #' See the predictions vignette and package website for worked examples and case studies: #' * #' * #' #' @rdname predictions #' @param model Model object #' @param variables Counterfactual variables. #' * Output: #' - `predictions()`: The entire dataset is replicated once for each unique combination of `variables`, and predictions are made. #' - `avg_predictions()`: The entire dataset is replicated, predictions are made, and they are marginalized by `variables` categories. #' - Warning: This can be expensive in large datasets. #' - Warning: Users who need "conditional" predictions should use the `newdata` argument instead of `variables`. #' * Input: #' - `NULL`: computes one prediction per row of `newdata` #' - Character vector: the dataset is replicated once of every combination of unique values of the variables identified in `variables`. #' - Named list: names identify the subset of variables of interest and their values. For numeric variables, the `variables` argument supports functions and string shortcuts: #' + A function which returns a numeric value #' + Numeric vector: Contrast between the 2nd element and the 1st element of the `x` vector. #' + "iqr": Contrast across the interquartile range of the regressor. #' + "sd": Contrast across one standard deviation around the regressor mean. #' + "2sd": Contrast across two standard deviations around the regressor mean. #' + "minmax": Contrast between the maximum and the minimum values of the regressor. #' + "threenum": mean and 1 standard deviation on both sides #' + "fivenum": Tukey's five numbers #' @param newdata Grid of predictor values at which we evaluate predictions. #' + Warning: Please avoid modifying your dataset between fitting the model and calling a `marginaleffects` function. This can sometimes lead to unexpected results. #' + `NULL` (default): Unit-level predictions for each observed value in the dataset (empirical distribution). The dataset is retrieved using [insight::get_data()], which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. #' + string: #' - "mean": Predictions evaluated when each predictor is held at its mean or mode. #' - "median": Predictions evaluated when each predictor is held at its median or mode. #' - "balanced": Predictions evaluated on a balanced grid with every combination of categories and numeric variables held at their means. #' - "tukey": Predictions evaluated at Tukey's 5 numbers. #' - "grid": Predictions evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). #' + [datagrid()] call to specify a custom grid of regressors. For example: #' - `newdata = datagrid(cyl = c(4, 6))`: `cyl` variable equal to 4 and 6 and other regressors fixed at their means or modes. #' - See the Examples section and the [datagrid()] documentation. #' + [subset()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = subset(treatment == 1)` #' + [dplyr::filter()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = filter(treatment == 1)` #' @param byfun A function such as `mean()` or `sum()` used to aggregate #' estimates within the subgroups defined by the `by` argument. `NULL` uses the #' `mean()` function. Must accept a numeric vector and return a single numeric #' value. This is sometimes used to take the sum or mean of predicted #' probabilities across outcome or predictor #' levels. See examples section. #' @param type string indicates the type (scale) of the predictions used to #' compute contrasts or slopes. This can differ based on the model #' type, but will typically be a string such as: "response", "link", "probs", #' or "zero". When an unsupported string is entered, the model-specific list of #' acceptable values is returned in an error message. When `type` is `NULL`, the #' first entry in the error message is used by default. #' @param transform A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries. #' #' @template deltamethod #' @template model_specific_arguments #' @template bayesian #' @template equivalence #' @template type #' @template order_of_operations #' @template parallel #' @template references #' @template options #' #' @return A `data.frame` with one row per observation and several columns: #' * `rowid`: row number of the `newdata` data frame #' * `type`: prediction type, as defined by the `type` argument #' * `group`: (optional) value of the grouped outcome (e.g., categorical outcome models) #' * `estimate`: predicted outcome #' * `std.error`: standard errors computed using the delta method. #' * `p.value`: p value associated to the `estimate` column. The null is determined by the `hypothesis` argument (0 by default), and p values are computed before applying the `transform` argument. For models of class `feglm`, `Gam`, `glm` and `negbin`, p values are computed on the link scale by default unless the `type` argument is specified explicitly. #' * `s.value`: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). #' * `conf.low`: lower bound of the confidence interval (or equal-tailed interval for bayesian models) #' * `conf.high`: upper bound of the confidence interval (or equal-tailed interval for bayesian models) #' #' See `?print.marginaleffects` for printing options. #' #' @examplesIf interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true") #' @examples #' # Adjusted Prediction for every row of the original dataset #' mod <- lm(mpg ~ hp + factor(cyl), data = mtcars) #' pred <- predictions(mod) #' head(pred) #' #' # Adjusted Predictions at User-Specified Values of the Regressors #' predictions(mod, newdata = datagrid(hp = c(100, 120), cyl = 4)) #' #' m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars) #' predictions(m, newdata = datagrid(FUN_factor = unique, FUN_numeric = median)) #' #' # Average Adjusted Predictions (AAP) #' library(dplyr) #' mod <- lm(mpg ~ hp * am * vs, mtcars) #' #' avg_predictions(mod) #' #' predictions(mod, by = "am") #' #' # Conditional Adjusted Predictions #' plot_predictions(mod, condition = "hp") #' #' # Counterfactual predictions with the `variables` argument #' # the `mtcars` dataset has 32 rows #' #' mod <- lm(mpg ~ hp + am, data = mtcars) #' p <- predictions(mod) #' head(p) #' nrow(p) #' #' # average counterfactual predictions #' avg_predictions(mod, variables = "am") #' #' # counterfactual predictions obtained by replicating the entire for different #' # values of the predictors #' p <- predictions(mod, variables = list(hp = c(90, 110))) #' nrow(p) #' #' #' # hypothesis test: is the prediction in the 1st row equal to the prediction in the 2nd row #' mod <- lm(mpg ~ wt + drat, data = mtcars) #' #' predictions( #' mod, #' newdata = datagrid(wt = 2:3), #' hypothesis = "b1 = b2") #' #' # same hypothesis test using row indices #' predictions( #' mod, #' newdata = datagrid(wt = 2:3), #' hypothesis = "b1 - b2 = 0") #' #' # same hypothesis test using numeric vector of weights #' predictions( #' mod, #' newdata = datagrid(wt = 2:3), #' hypothesis = c(1, -1)) #' #' # two custom contrasts using a matrix of weights #' lc <- matrix(c( #' 1, -1, #' 2, 3), #' ncol = 2) #' predictions( #' mod, #' newdata = datagrid(wt = 2:3), #' hypothesis = lc) #' #' #' # `by` argument #' mod <- lm(mpg ~ hp * am * vs, data = mtcars) #' predictions(mod, by = c("am", "vs")) #' #' library(nnet) #' nom <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE) #' #' # first 5 raw predictions #' predictions(nom, type = "probs") |> head() #' #' # average predictions #' avg_predictions(nom, type = "probs", by = "group") #' #' by <- data.frame( #' group = c("3", "4", "5"), #' by = c("3,4", "3,4", "5")) #' #' predictions(nom, type = "probs", by = by) #' #' # sum of predicted probabilities for combined response levels #' mod <- multinom(factor(cyl) ~ mpg + am, data = mtcars, trace = FALSE) #' by <- data.frame( #' by = c("4,6", "4,6", "8"), #' group = as.character(c(4, 6, 8))) #' predictions(mod, newdata = "mean", byfun = sum, by = by) #' #' @inheritParams slopes #' @inheritParams comparisons #' @export predictions <- function(model, newdata = NULL, variables = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, by = FALSE, byfun = NULL, wts = FALSE, transform = NULL, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, numderiv = "fdforward", ...) { dots <- list(...) if ("transform_post" %in% names(dots)) { transform <- dots[["transform_post"]] insight::format_warning("The `transform_post` argument is deprecated. Use `transform` instead.") } # very early, before any use of newdata # if `newdata` is a call to `typical` or `counterfactual`, insert `model` scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) if ("cross" %in% names(dots)) { insight::format_error("The `cross` argument is not available in this function.") } # extracting modeldata repeatedly is slow. # checking dots allows marginalmeans to pass modeldata to predictions. if (isTRUE(by)) { modeldata <- get_modeldata(model, additional_variables = FALSE, modeldata = dots[["modeldata"]], wts = wts) } else { modeldata <- get_modeldata(model, additional_variables = by, modeldata = dots[["modeldata"]], wts = wts) } # build call: match.call() doesn't work well in *apply() # after sanitize_newdata_call call_attr <- c(list( name = "predictions", model = model, newdata = newdata, variables = variables, vcov = vcov, conf_level = conf_level, type = type, by = by, byfun = byfun, wts = wts, transform = transform, hypothesis = hypothesis, df = df), dots) if ("modeldata" %in% names(dots)) { call_attr[["modeldata"]] <- modeldata } call_attr <- do.call("call", call_attr) # sanity checks sanity_dots(model = model, ...) numderiv <- sanitize_numderiv(numderiv) sanity_df(df, newdata) sanity_equivalence_p_adjust(equivalence, p_adjust) model <- sanitize_model( model = model, newdata = newdata, wts = wts, vcov = vcov, by = by, calling_function = "predictions", ...) tmp <- sanitize_hypothesis(hypothesis, ...) hypothesis <- tmp$hypothesis hypothesis_null <- tmp$hypothesis_null # multiple imputation if (inherits(model, c("mira", "amest"))) { out <- process_imputation(model, call_attr) return(out) } # if type is NULL, we backtransform if relevant type_string <- sanitize_type( model = model, type = type, by = by, calling_function = "predictions") if (identical(type_string, "invlink(link)")) { if (is.null(hypothesis)) { type_call <- "link" } else { type_call <- "response" type_string <- "response" insight::format_warning('The `type="invlink"` argument is not available unless `hypothesis` is `NULL` or a single number. The value of the `type` argument was changed to "response" automatically. To suppress this warning, use `type="response"` explicitly in your function call.') } } else { type_call <- type_string } # save the original because it gets converted to a named list, which breaks # user-input sanity checks transform_original <- transform transform <- sanitize_transform(transform) conf_level <- sanitize_conf_level(conf_level, ...) newdata <- sanitize_newdata( model = model, newdata = newdata, modeldata = modeldata, by = by, wts = wts) # after sanitize_newdata sanity_by(by, newdata) # after sanity_by newdata <- dedup_newdata( model = model, newdata = newdata, wts = wts, by = by, byfun = byfun) if (isFALSE(wts) && "marginaleffects_wts_internal" %in% colnames(newdata)) { wts <- "marginaleffects_wts_internal" } # analogous to comparisons(variables=list(...)) if (!is.null(variables)) { args <- list( "model" = model, "newdata" = newdata, "grid_type" = "counterfactual") tmp <- sanitize_variables( variables = variables, model = model, newdata = newdata, modeldata = modeldata, calling_function = "predictions" )$conditional for (v in tmp) { args[[v$name]] <- v$value } newdata <- do.call("datagrid", args) # the original rowids are no longer valid after averaging et al. newdata[["rowid"]] <- NULL } character_levels <- attr(newdata, "newdata_character_levels") # trust newdata$rowid if (!"rowid" %in% colnames(newdata)) { newdata[["rowid"]] <- seq_len(nrow(newdata)) } # mlogit models sometimes returns an `idx` column that is impossible to `rbind` if (inherits(model, "mlogit") && inherits(newdata[["idx"]], "idx")) { newdata[["idx"]] <- NULL } # padding destroys `newdata` attributes, so we save them newdata_attr_cache <- get_marginaleffects_attributes(newdata, include_regex = "^newdata") # mlogit uses an internal index that is very hard to track, so we don't # support `newdata` and assume no padding the `idx` column is necessary for # `get_predict` but it breaks binding, so we can't remove it in # sanity_newdata and we can't rbind it with padding # pad factors: `model.matrix` breaks when factor levels are missing if (inherits(model, "mlogit")) { padding <- data.frame() } else { padding <- complete_levels(newdata, character_levels) if (nrow(padding) > 0) { newdata <- rbindlist(list(padding, newdata)) } } if (is.null(by) || isFALSE(by)) { vcov_tmp <- vcov } else { vcov_tmp <- FALSE } ############### sanity checks are over # Bootstrap out <- inferences_dispatch( INF_FUN = predictions, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type_string, by = by, conf_level = conf_level, byfun = byfun, wts = wts, transform = transform_original, hypothesis = hypothesis, ...) if (!is.null(out)) { return(out) } # pre-building the model matrix can speed up repeated predictions newdata <- get_model_matrix_attribute(model, newdata) # main estimation args <- list( model = model, newdata = newdata, type = type_call, hypothesis = hypothesis, wts = wts, by = by, byfun = byfun) args <- utils::modifyList(args, dots) tmp <- do.call(get_predictions, args) hyp_by <- attr(tmp, "hypothesis_function_by") # two cases when tmp is a data.frame # get_predict gets us rowid with the original rows if (inherits(tmp, "data.frame")) { setnames(tmp, old = c("Predicted", "SE", "CI_low", "CI_high"), new = c("estimate", "std.error", "conf.low", "conf.high"), skip_absent = TRUE) } else { tmp <- data.frame(newdata$rowid, type, tmp) colnames(tmp) <- c("rowid", "estimate") if ("rowidcf" %in% colnames(newdata)) { tmp[["rowidcf"]] <- newdata[["rowidcf"]] } } # issue #1105: hypothesis may change the meaning of rows, so we don't want to force-merge `newdata` if (!"rowid" %in% colnames(tmp) && nrow(tmp) == nrow(newdata) && is.null(hypothesis)) { tmp$rowid <- newdata$rowid } # degrees of freedom if (isTRUE(vcov == "satterthwaite") || isTRUE(vcov == "kenward-roger")) { df <- tryCatch( # df_per_observation is an undocumented argument introduced in 0.18.4.7 to preserve backward incompatibility insight::get_df(model, data = newdata, type = vcov, df_per_observation = TRUE), error = function(e) NULL) if (isTRUE(length(df) == nrow(tmp))) { tmp$df <- df } } # bayesian posterior draws draws <- attr(tmp, "posterior_draws") V <- NULL J <- NULL if (!isFALSE(vcov)) { V <- get_vcov(model, vcov = vcov, type = type, ...) # Delta method if (!"std.error" %in% colnames(tmp) && is.null(draws)) { if (isTRUE(checkmate::check_matrix(V))) { # vcov = FALSE to speed things up fun <- function(...) { get_predictions(..., wts = wts, verbose = FALSE)$estimate } args <- list( model, newdata = newdata, vcov = V, type = type_call, FUN = fun, J = J, hypothesis = hypothesis, by = by, byfun = byfun, numderiv = numderiv) args <- utils::modifyList(args, dots) se <- do.call(get_se_delta, args) if (is.numeric(se) && length(se) == nrow(tmp)) { J <- attr(se, "jacobian") attr(se, "jacobian") <- NULL tmp[["std.error"]] <- se } } } tmp <- get_ci( tmp, conf_level = conf_level, vcov = vcov, draws = draws, estimate = "estimate", null_hypothesis = hypothesis_null, df = df, model = model, p_adjust = p_adjust, ...) } out <- data.table::data.table(tmp) data.table::setDT(newdata) # expensive: only do this inside jacobian if necessary if (!inherits(model, "mclogit")) { # weird case. probably a cleaner way but lazy now... out <- merge_by_rowid(out, newdata) } # save weights as attribute and not column marginaleffects_wts_internal <- out[["marginaleffects_wts_internal"]] out[["marginaleffects_wts_internal"]] <- NULL # bycols if (isTRUE(checkmate::check_data_frame(by))) { bycols <- setdiff(colnames(by), "by") } else { bycols <- by } # sort rows: do NOT sort rows because it breaks hypothesis b1, b2, b3 indexing. # clean columns stubcols <- c( "rowid", "rowidcf", "term", "group", "hypothesis", bycols, "estimate", "std.error", "statistic", "p.value", "s.value", "conf.low", "conf.high", "marginaleffects_wts", sort(grep("^predicted", colnames(newdata), value = TRUE))) cols <- intersect(stubcols, colnames(out)) cols <- unique(c(cols, colnames(out))) out <- out[, ..cols] attr(out, "posterior_draws") <- draws # equivalence tests out <- equivalence(out, equivalence = equivalence, df = df, ...) # after rename to estimate / after assign draws if (identical(type_string, "invlink(link)")) { linv <- tryCatch(insight::link_inverse(model), error = function(e) identity) out <- backtransform(out, transform = linv) } out <- backtransform(out, transform = transform) data.table::setDF(out) class(out) <- c("predictions", class(out)) out <- set_marginaleffects_attributes(out, attr_cache = newdata_attr_cache) attr(out, "model") <- model attr(out, "type") <- type_string attr(out, "model_type") <- class(model)[1] attr(out, "vcov.type") <- get_vcov_label(vcov) attr(out, "jacobian") <- J attr(out, "vcov") <- V attr(out, "newdata") <- newdata attr(out, "weights") <- marginaleffects_wts_internal attr(out, "conf_level") <- conf_level attr(out, "by") <- by attr(out, "call") <- call_attr attr(out, "hypothesis_by") <- hyp_by attr(out, "transform_label") <- names(transform)[1] attr(out, "transform") <- transform[[1]] # save newdata for use in recall() attr(out, "newdata") <- newdata if (inherits(model, "brmsfit")) { insight::check_if_installed("brms") attr(out, "nchains") <- brms::nchains(model) } if ("group" %in% names(out) && all(out$group == "main_marginaleffect")) { out$group <- NULL } return(out) } # wrapper used only for standard_error_delta get_predictions <- function(model, newdata, type, by = NULL, byfun = byfun, hypothesis = NULL, verbose = TRUE, wts = FALSE, ...) { out <- myTryCatch(get_predict( model, newdata = newdata, type = type, ...)) if (inherits(out$value, "data.frame")) { out <- out$value } else { # tidymodels if (inherits(out$error, "rlang_error") && isTRUE(grepl("the object should be", out$error$message))) { insight::format_error(out$error$message) } msg <- "Unable to compute predicted values with this model. You can try to supply a different dataset to the `newdata` argument." if (!is.null(out$error)) { msg <- c(paste(msg, "This error was also raised:"), "", out$error$message) } if (inherits(out$value, "try-error")) { msg <- c(paste(msg, "", "This error was also raised:"), "", as.character(out$value)) } msg <- c(msg, "", "Bug Tracker: https://github.com/vincentarelbundock/marginaleffects/issues") insight::format_error(msg) } if (!"rowid" %in% colnames(out) && "rowid" %in% colnames(newdata) && nrow(out) == nrow(newdata)) { out$rowid <- newdata$rowid } # extract attributes before setDT draws <- attr(out, "posterior_draws") data.table::setDT(out) # unpad factors before averaging # trust `newdata` rowid more than `out` because sometimes `get_predict()` will add a positive index even on padded data # HACK: the padding indexing rowid code is still a mess # Do not merge `newdata` with `hypothesis`, because it may have the same # number of rows but represent different quantities if ("rowid" %in% colnames(newdata) && nrow(newdata) == nrow(out) && is.null(hypothesis)) { out$rowid <- newdata$rowid } # unpad if ("rowid" %in% colnames(out)) draws <- subset(draws, out$rowid > 0) if ("rowid" %in% colnames(out)) out <- subset(out, rowid > 0) if ("rowid" %in% colnames(newdata)) newdata <- subset(newdata, rowid > 0) # expensive: only do this inside the jacobian if necessary if (!isFALSE(wts) || !isTRUE(checkmate::check_flag(by, null.ok = TRUE)) || inherits(model, "mclogit")) { # not sure why sorting is so finicky here out <- merge_by_rowid(out, newdata) } # by: auto group if (isTRUE(checkmate::check_character(by))) { by <- intersect(c("group", by), colnames(out)) } # averaging by groups out <- get_by( out, draws = draws, newdata = newdata, by = by, byfun = byfun, verbose = verbose, ...) draws <- attr(out, "posterior_draws") # hypothesis tests using the delta method out <- get_hypothesis(out, hypothesis = hypothesis, by = by, newdata = newdata, draws = draws) # WARNING: we cannot sort rows at the end because `get_hypothesis()` is # applied in the middle, and it must already be sorted in the final order, # otherwise, users cannot know for sure what is going to be the first and # second rows, etc. out <- sort_columns(out, newdata, by) return(out) } #' Average predictions #' @describeIn predictions Average predictions #' @export #' avg_predictions <- function(model, newdata = NULL, variables = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, by = TRUE, byfun = NULL, wts = FALSE, transform = NULL, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, numderiv = "fdforward", ...) { # order of the first few paragraphs is important # if `newdata` is a call to `typical` or `counterfactual`, insert `model` scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) # group by focal variable automatically unless otherwise stated if (isTRUE(by)) { if (isTRUE(checkmate::check_character(variables))) { by <- variables } else if (isTRUE(checkmate::check_list(variables, names = "named"))) { by <- names(variables) } } # Bootstrap out <- inferences_dispatch( INF_FUN = avg_predictions, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type, by = by, conf_level = conf_level, byfun = byfun, wts = wts, transform = transform, hypothesis = hypothesis, ...) if (!is.null(out)) { return(out) } out <- predictions( model = model, newdata = newdata, variables = variables, vcov = vcov, conf_level = conf_level, type = type, by = by, byfun = byfun, wts = wts, transform = transform, hypothesis = hypothesis, equivalence = equivalence, p_adjust = p_adjust, df = df, ...) return(out) } marginaleffects/R/methods_nlme.R0000644000176200001440000000022614631452335016414 0ustar liggesusers#' @rdname set_coef #' @export set_coef.lme <- function(model, coefs, ...) { model[["coefficients"]][["fixed"]][names(coefs)] <- coefs model }marginaleffects/R/comparisons.R0000644000176200001440000006611414632657760016315 0ustar liggesusers#' Comparisons Between Predictions Made With Different Regressor Values #' #' @description #' Predict the outcome variable at different regressor values (e.g., college #' graduates vs. others), and compare those predictions by computing a difference, #' ratio, or some other function. `comparisons()` can return many quantities of #' interest, such as contrasts, differences, risk ratios, changes in log odds, lift, #' slopes, elasticities, etc. #' #' * `comparisons()`: unit-level (conditional) estimates. #' * `avg_comparisons()`: average (marginal) estimates. #' #' `variables` identifies the focal regressors whose "effect" we are interested in. `comparison` determines how predictions with different regressor values are compared (difference, ratio, odds, etc.). The `newdata` argument and the `datagrid()` function control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. #' #' See the comparisons vignette and package website for worked examples and case studies: #' #' * #' * #' #' @inheritParams slopes #' @inheritParams predictions #' @param variables Focal variables #' * `NULL`: compute comparisons for all the variables in the model object (can be slow). #' * Character vector: subset of variables (usually faster). #' * Named list: names identify the subset of variables of interest, and values define the type of contrast to compute. Acceptable values depend on the variable type: #' - Factor or character variables: #' * "reference": Each factor level is compared to the factor reference (base) level #' * "all": All combinations of observed levels #' * "sequential": Each factor level is compared to the previous factor level #' * "pairwise": Each factor level is compared to all other levels #' * "minmax": The highest and lowest levels of a factor. #' * "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses. #' * Vector of length 2 with the two values to compare. #' * Data frame with the same number of rows as `newdata`, with two columns of "lo" and "hi" values to compare. #' * Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. #' - Logical variables: #' * NULL: contrast between TRUE and FALSE #' * Data frame with the same number of rows as `newdata`, with two columns of "lo" and "hi" values to compare. #' * Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. #' - Numeric variables: #' * Numeric of length 1: Forward contrast for a gap of `x`, computed between the observed value and the observed value plus `x`. Users can set a global option to get a "center" or "backward" contrast instead: `options(marginaleffects_contrast_direction="center")` #' * Numeric vector of length 2: Contrast between the largest and the smallest elements of the `x` vector. #' * Data frame with the same number of rows as `newdata`, with two columns of "lo" and "hi" values to compare. #' * Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. #' * "iqr": Contrast across the interquartile range of the regressor. #' * "sd": Contrast across one standard deviation around the regressor mean. #' * "2sd": Contrast across two standard deviations around the regressor mean. #' * "minmax": Contrast between the maximum and the minimum values of the regressor. #' - Examples: #' + `variables = list(gear = "pairwise", hp = 10)` #' + `variables = list(gear = "sequential", hp = c(100, 120))` #' + `variables = list(hp = \(x) data.frame(low = x - 5, high = x + 10))` #' + See the Examples section below for more. #' @param newdata Grid of predictor values at which we evaluate the comparisons. #' + Warning: Avoid modifying your dataset between fitting the model and calling a `marginaleffects` function. This can sometimes lead to unexpected results. #' + `NULL` (default): Unit-level contrasts for each observed value in the dataset (empirical distribution). The dataset is retrieved using [insight::get_data()], which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. #' + data frame: Unit-level contrasts for each row of the `newdata` data frame. #' + string: #' - "mean": Contrasts at the Mean. Contrasts when each predictor is held at its mean or mode. #' - "median": Contrasts at the Median. Contrasts when each predictor is held at its median or mode. #' - "balanced": Contrasts evaluated on a balanced grid with every combination of categories and numeric variables held at their means. #' - "tukey": Contrasts at Tukey's 5 numbers. #' - "grid": Contrasts on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). #' + [datagrid()] call to specify a custom grid of regressors. For example: #' - `newdata = datagrid(cyl = c(4, 6))`: `cyl` variable equal to 4 and 6 and other regressors fixed at their means or modes. #' - `newdata = datagrid(mpg = fivenum)`: `mpg` variable held at Tukey's five numbers (using the `fivenum` function), and other regressors fixed at their means or modes. #' - See the Examples section and the [datagrid] documentation. #' + [subset()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = subset(treatment == 1)` #' + [dplyr::filter()] call with a single argument to select a subset of the dataset used to fit the model, ex: `newdata = filter(treatment == 1)` #' @param comparison How should pairs of predictions be compared? Difference, ratio, odds ratio, or user-defined functions. #' * string: shortcuts to common contrast functions. #' - Supported shortcuts strings: `r paste(names(marginaleffects:::comparison_function_dict), collapse = ", ")` #' - See the Comparisons section below for definitions of each transformation. #' * function: accept two equal-length numeric vectors of adjusted predictions (`hi` and `lo`) and returns a vector of contrasts of the same length, or a unique numeric value. #' - See the Transformations section below for examples of valid functions. #' @param transform string or function. Transformation applied to unit-level estimates and confidence intervals just before the function returns results. Functions must accept a vector and return a vector of the same length. Support string shortcuts: "exp", "ln" #' @param equivalence Numeric vector of length 2: bounds used for the two-one-sided test (TOST) of equivalence, and for the non-inferiority and non-superiority tests. See Details section below. #' @param by Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: #' - `FALSE`: return the original unit-level estimates. #' - `TRUE`: aggregate estimates for each term. #' - Character vector of column names in `newdata` or in the data frame produced by calling the function without the `by` argument. #' - Data frame with a `by` column of group labels, and merging columns shared by `newdata` or the data frame produced by calling the same function without the `by` argument. #' - See examples below. #' - For more complex aggregations, you can use the `FUN` argument of the `hypotheses()` function. See that function's documentation and the Hypothesis Test vignettes on the `marginaleffects` website. #' @param cross #' * `FALSE`: Contrasts represent the change in adjusted predictions when one predictor changes and all other variables are held constant. #' * `TRUE`: Contrasts represent the changes in adjusted predictions when all the predictors specified in the `variables` argument are manipulated simultaneously (a "cross-contrast"). #' @template deltamethod #' @template model_specific_arguments #' @template comparison_functions #' @template bayesian #' @template equivalence #' @template type #' @template order_of_operations #' @template parallel #' @template references #' @template options #' #' @return A `data.frame` with one row per observation (per term/group) and several columns: #' * `rowid`: row number of the `newdata` data frame #' * `type`: prediction type, as defined by the `type` argument #' * `group`: (optional) value of the grouped outcome (e.g., categorical outcome models) #' * `term`: the variable whose marginal effect is computed #' * `dydx`: slope of the outcome with respect to the term, for a given combination of predictor values #' * `std.error`: standard errors computed by via the delta method. #' * `p.value`: p value associated to the `estimate` column. The null is determined by the `hypothesis` argument (0 by default), and p values are computed before applying the `transform` argument. #' * `s.value`: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). #' * `conf.low`: lower bound of the confidence interval (or equal-tailed interval for bayesian models) #' * `conf.high`: upper bound of the confidence interval (or equal-tailed interval for bayesian models) #' #' See `?print.marginaleffects` for printing options. #' #' @examplesIf interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true") #' @examples #' library(marginaleffects) #' #' # Linear model #' tmp <- mtcars #' tmp$am <- as.logical(tmp$am) #' mod <- lm(mpg ~ am + factor(cyl), tmp) #' avg_comparisons(mod, variables = list(cyl = "reference")) #' avg_comparisons(mod, variables = list(cyl = "sequential")) #' avg_comparisons(mod, variables = list(cyl = "pairwise")) #' #' # GLM with different scale types #' mod <- glm(am ~ factor(gear), data = mtcars) #' avg_comparisons(mod, type = "response") #' avg_comparisons(mod, type = "link") #' #' # Contrasts at the mean #' comparisons(mod, newdata = "mean") #' #' # Contrasts between marginal means #' comparisons(mod, newdata = "marginalmeans") #' #' # Contrasts at user-specified values #' comparisons(mod, newdata = datagrid(am = 0, gear = tmp$gear)) #' comparisons(mod, newdata = datagrid(am = unique, gear = max)) #' #' m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars) #' comparisons(m, variables = "hp", newdata = datagrid(FUN_factor = unique, FUN_numeric = median)) #' #' # Numeric contrasts #' mod <- lm(mpg ~ hp, data = mtcars) #' avg_comparisons(mod, variables = list(hp = 1)) #' avg_comparisons(mod, variables = list(hp = 5)) #' avg_comparisons(mod, variables = list(hp = c(90, 100))) #' avg_comparisons(mod, variables = list(hp = "iqr")) #' avg_comparisons(mod, variables = list(hp = "sd")) #' avg_comparisons(mod, variables = list(hp = "minmax")) #' #' # using a function to specify a custom difference in one regressor #' dat <- mtcars #' dat$new_hp <- 49 * (dat$hp - min(dat$hp)) / (max(dat$hp) - min(dat$hp)) + 1 #' modlog <- lm(mpg ~ log(new_hp) + factor(cyl), data = dat) #' fdiff <- \(x) data.frame(x, x + 10) #' avg_comparisons(modlog, variables = list(new_hp = fdiff)) #' #' # Adjusted Risk Ratio: see the contrasts vignette #' mod <- glm(vs ~ mpg, data = mtcars, family = binomial) #' avg_comparisons(mod, comparison = "lnratioavg", transform = exp) #' #' # Adjusted Risk Ratio: Manual specification of the `comparison` #' avg_comparisons( #' mod, #' comparison = function(hi, lo) log(mean(hi) / mean(lo)), #' transform = exp) # #' # cross contrasts #' mod <- lm(mpg ~ factor(cyl) * factor(gear) + hp, data = mtcars) #' avg_comparisons(mod, variables = c("cyl", "gear"), cross = TRUE) #' #' # variable-specific contrasts #' avg_comparisons(mod, variables = list(gear = "sequential", hp = 10)) #' #' # hypothesis test: is the `hp` marginal effect at the mean equal to the `drat` marginal effect #' mod <- lm(mpg ~ wt + drat, data = mtcars) #' #' comparisons( #' mod, #' newdata = "mean", #' hypothesis = "wt = drat") #' #' # same hypothesis test using row indices #' comparisons( #' mod, #' newdata = "mean", #' hypothesis = "b1 - b2 = 0") #' #' # same hypothesis test using numeric vector of weights #' comparisons( #' mod, #' newdata = "mean", #' hypothesis = c(1, -1)) #' #' # two custom contrasts using a matrix of weights #' lc <- matrix(c( #' 1, -1, #' 2, 3), #' ncol = 2) #' comparisons( #' mod, #' newdata = "mean", #' hypothesis = lc) #' #' # Effect of a 1 group-wise standard deviation change #' # First we calculate the SD in each group of `cyl` #' # Second, we use that SD as the treatment size in the `variables` argument #' library(dplyr) #' mod <- lm(mpg ~ hp + factor(cyl), mtcars) #' tmp <- mtcars %>% #' group_by(cyl) %>% #' mutate(hp_sd = sd(hp)) #' avg_comparisons(mod, #' variables = list(hp = function(x) data.frame(x, x + tmp$hp_sd)), #' by = "cyl") #' #' # `by` argument #' mod <- lm(mpg ~ hp * am * vs, data = mtcars) #' comparisons(mod, by = TRUE) #' #' mod <- lm(mpg ~ hp * am * vs, data = mtcars) #' avg_comparisons(mod, variables = "hp", by = c("vs", "am")) #' #' library(nnet) #' mod <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE) #' by <- data.frame( #' group = c("3", "4", "5"), #' by = c("3,4", "3,4", "5")) #' comparisons(mod, type = "probs", by = by) #' #' @export comparisons <- function(model, newdata = NULL, variables = NULL, comparison = "difference", type = NULL, vcov = TRUE, by = FALSE, conf_level = 0.95, transform = NULL, cross = FALSE, wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ...) { dots <- list(...) # backward compatibility if ("transform_post" %in% names(dots)) { transform <- dots[["transform_post"]] insight::format_warning("The `transform_post` argument is deprecated. Use `transform` instead.") } if ("transform_pre" %in% names(dots)) { comparison <- dots[["transform_pre"]] insight::format_warning("The `transform_pre` argument is deprecated. Use `comparison` instead.") } # very early, before any use of newdata # if `newdata` is a call to `typical` or `counterfactual`, insert `model` scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) # extracting modeldata repeatedly is slow. # checking dots allows marginalmeans to pass modeldata to predictions. if (isTRUE(by)) { modeldata <- get_modeldata(model, additional_variables = FALSE, modeldata = dots[["modeldata"]], wts = wts) } else { modeldata <- get_modeldata(model, additional_variables = by, modeldata = dots[["modeldata"]], wts = wts) } # build call: match.call() doesn't work well in *apply() # after sanitize_newdata_call call_attr <- c(list( name = "comparisons", model = model, newdata = newdata, variables = variables, type = type, vcov = vcov, by = by, conf_level = conf_level, comparison = comparison, transform = transform, cross = cross, wts = wts, hypothesis = hypothesis, equivalence = equivalence, p_adjust = p_adjust, df = df), dots) if ("modeldata" %in% names(dots)) { call_attr[["modeldata"]] <- modeldata } call_attr <- do.call("call", call_attr) # required by stubcols later, but might be overwritten bycols <- NULL # sanity checks sanity_dots(model, ...) sanity_df(df, newdata) conf_level <- sanitize_conf_level(conf_level, ...) checkmate::assert_number(eps, lower = 1e-10, null.ok = TRUE) numderiv <- sanitize_numderiv(numderiv) sanity_equivalence_p_adjust(equivalence, p_adjust) model <- sanitize_model( model = model, newdata = newdata, wts = wts, vcov = vcov, by = by, calling_function = "comparisons", ...) cross <- sanitize_cross(cross, variables, model) type <- sanitize_type(model = model, type = type, calling_function = "comparisons") sanity_comparison(comparison) # multiple imputation if (inherits(model, c("mira", "amest"))) { out <- process_imputation(model, call_attr) return(out) } # transforms comparison_label <- transform_label <- NULL if (is.function(comparison)) { comparison_label <- deparse(substitute(comparison)) } if (is.function(transform)) { transform_label <- deparse(substitute(transform)) transform <- sanitize_transform(transform) names(transform) <- transform_label } else { transform <- sanitize_transform(transform) transform_label <- names(transform) } marginalmeans <- isTRUE(checkmate::check_choice(newdata, choices = "marginalmeans")) newdata <- sanitize_newdata( model = model, newdata = newdata, modeldata = modeldata, by = by, wts = wts) # after sanitize_newdata sanity_by(by, newdata) # after sanity_by newdata <- dedup_newdata( model = model, newdata = newdata, wts = wts, by = by, cross = cross, comparison = comparison) if (isFALSE(wts) && "marginaleffects_wts_internal" %in% colnames(newdata)) { wts <- "marginaleffects_wts_internal" } # after sanitize_newdata # after dedup_newdata variables_list <- sanitize_variables( model = model, newdata = newdata, modeldata = modeldata, variables = variables, cross = cross, by = by, comparison = comparison, eps = eps) # get dof before transforming the vcov arg # get_df() produces a weird warning on non lmerMod. We can skip them # because get_vcov() will produce an informative error later. if (inherits(model, "lmerMod") && (isTRUE(hush(vcov %in% c("satterthwaite", "kenward-roger"))))) { # predict.lmerTest requires the DV dv <- insight::find_response(model) if (!dv %in% colnames(newdata)) { newdata[[dv]] <- mean(insight::get_response(model)) } if (!isTRUE(hush(is.infinite(df)))) { insight::format_error('The `df` argument is not supported when `vcov` is "satterthwaite" or "kenward-roger".') } # df_per_observation is an undocumented argument introduced in 0.18.4.7 to preserve backward incompatibility df <- insight::get_df(model, type = vcov, data = newdata, df_per_observation = TRUE) } vcov_false <- isFALSE(vcov) vcov.type <- get_vcov_label(vcov) vcov <- get_vcov(model, vcov = vcov, type = type, ...) predictors <- variables_list$conditional ############### sanity checks are over # Bootstrap out <- inferences_dispatch( INF_FUN = comparisons, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type, by = by, conf_level = conf_level, cross = cross, comparison = comparison, transform = transform, wts = wts, hypothesis = hypothesis, eps = eps, ...) if (!is.null(out)) { return(out) } # after inferences dispatch tmp <- sanitize_hypothesis(hypothesis, ...) hypothesis <- tmp$hypothesis hypothesis_null <- tmp$hypothesis_null # compute contrasts and standard errors args <- list(model = model, newdata = newdata, variables = predictors, cross = cross, marginalmeans = marginalmeans, modeldata = modeldata) dots[["modeldata"]] <- NULL # dont' pass twice args <- c(args, dots) contrast_data <- do.call("get_contrast_data", args) args <- list(model, newdata = newdata, variables = predictors, type = type, original = contrast_data[["original"]], hi = contrast_data[["hi"]], lo = contrast_data[["lo"]], wts = contrast_data[["original"]][["marginaleffects_wts_internal"]], by = by, marginalmeans = marginalmeans, cross = cross, hypothesis = hypothesis, modeldata = modeldata) args <- c(args, dots) mfx <- do.call("get_contrasts", args) hyp_by <- attr(mfx, "hypothesis_function_by") # bayesian posterior if (!is.null(attr(mfx, "posterior_draws"))) { draws <- attr(mfx, "posterior_draws") J <- NULL # standard errors via delta method } else if (!vcov_false && isTRUE(checkmate::check_matrix(vcov))) { idx <- intersect(colnames(mfx), c("group", "term", "contrast")) idx <- mfx[, (idx), drop = FALSE] args <- list(model, vcov = vcov, type = type, FUN = get_se_delta_contrasts, newdata = newdata, index = idx, variables = predictors, marginalmeans = marginalmeans, hypothesis = hypothesis, hi = contrast_data$hi, lo = contrast_data$lo, original = contrast_data$original, by = by, eps = eps, cross = cross, numderiv = numderiv) args <- c(args, dots) se <- do.call("get_se_delta", args) J <- attr(se, "jacobian") attr(se, "jacobian") <- NULL mfx$std.error <- as.numeric(se) draws <- NULL # no standard error } else { J <- draws <- NULL } # merge original data back in if ((is.null(by) || isFALSE(by)) && "rowid" %in% colnames(mfx)) { if ("rowid" %in% colnames(newdata)) { idx <- c("rowid", "rowidcf", "term", "contrast", "by", setdiff(colnames(contrast_data$original), colnames(mfx))) idx <- intersect(idx, colnames(contrast_data$original)) tmp <- contrast_data$original[, ..idx, drop = FALSE] # contrast_data is duplicated to compute contrasts for different terms or pairs bycols <- intersect(colnames(tmp), colnames(mfx)) idx <- duplicated(tmp, by = bycols) tmp <- tmp[!idx] mfx <- merge(mfx, tmp, all.x = TRUE, by = bycols, sort = FALSE) # HACK: relies on NO sorting at ANY point } else { idx <- setdiff(colnames(contrast_data$original), colnames(mfx)) mfx <- data.table(mfx, contrast_data$original[, ..idx]) } } # meta info mfx <- get_ci( mfx, conf_level = conf_level, df = df, draws = draws, estimate = "estimate", null_hypothesis = hypothesis_null, p_adjust = p_adjust, model = model) # clean rows and columns # WARNING: we cannot sort rows at the end because `get_hypothesis()` is # applied in the middle, and it must already be sorted in the final order, # otherwise, users cannot know for sure what is going to be the first and # second rows, etc. mfx <- sort_columns(mfx, newdata, by) # bayesian draws attr(mfx, "posterior_draws") <- draws # equivalence tests mfx <- equivalence(mfx, equivalence = equivalence, df = df, ...) # after draws attribute mfx <- backtransform(mfx, transform) # save as attribute and not column if (any(!is.na(mfx[["marginaleffects_wts_internal"]]))) { marginaleffects_wts_internal <- mfx[["marginaleffects_wts_internal"]] } else { marginaleffects_wts_internal <- NULL } mfx[["marginaleffects_wts_internal"]] <- NULL out <- mfx data.table::setDF(out) out <- set_marginaleffects_attributes( out, get_marginaleffects_attributes(newdata, include_regex = "^newdata.*class|explicit|matrix|levels")) # other attributes attr(out, "newdata") <- newdata attr(out, "call") <- call_attr attr(out, "type") <- type attr(out, "model_type") <- class(model)[1] attr(out, "model") <- model attr(out, "variables") <- predictors attr(out, "jacobian") <- J attr(out, "vcov") <- vcov attr(out, "vcov.type") <- vcov.type attr(out, "weights") <- marginaleffects_wts_internal attr(out, "comparison") <- comparison attr(out, "transform") <- transform[[1]] attr(out, "comparison_label") <- comparison_label attr(out, "hypothesis_by") <- hyp_by attr(out, "transform_label") <- transform_label attr(out, "conf_level") <- conf_level attr(out, "by") <- by if (inherits(model, "brmsfit")) { insight::check_if_installed("brms") attr(out, "nchains") <- brms::nchains(model) } class(out) <- c("comparisons", class(out)) return(out) } #' Average comparisons #' @describeIn comparisons Average comparisons #' @export #' avg_comparisons <- function(model, newdata = NULL, variables = NULL, type = NULL, vcov = TRUE, by = TRUE, conf_level = 0.95, comparison = "difference", transform = NULL, cross = FALSE, wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ...) { # order of the first few paragraphs is important # if `newdata` is a call to `typical` or `counterfactual`, insert `model` scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model, by = by) # Bootstrap out <- inferences_dispatch( INF_FUN = avg_comparisons, model = model, newdata = newdata, vcov = vcov, variables = variables, type = type, by = by, cross = cross, conf_level = conf_level, comparison = comparison, transform = transform, wts = wts, hypothesis = hypothesis, eps = eps, ...) if (!is.null(out)) { return(out) } out <- comparisons( model = model, newdata = newdata, variables = variables, type = type, vcov = vcov, by = by, conf_level = conf_level, comparison = comparison, transform = transform, cross = cross, wts = wts, hypothesis = hypothesis, equivalence = equivalence, p_adjust = p_adjust, df = df, eps = eps, ...) return(out) } marginaleffects/R/methods_AER.R0000644000176200001440000000042214631452335016066 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.tobit <- set_coef.default # Note: needed because otherwise set.coef.survreg() would be called, # which requires different treatment (see insight:::get_parameters.tobit() # vs insight:::get_parameters.survreg()) marginaleffects/R/methods.R0000644000176200001440000000202414631452335015377 0ustar liggesusers#' @noRd #' @export vcov.comparisons <- function(object, ...) { attr(object, "jacobian") %*% attr(object, "vcov") %*% t(attr(object, "jacobian")) } #' @noRd #' @export vcov.predictions <- vcov.comparisons #' @noRd #' @export vcov.hypotheses <- vcov.comparisons #' @noRd #' @export vcov.slopes <- vcov.comparisons #' @noRd #' @export vcov.marginalmeans <- vcov.comparisons #' @export #' @noRd coef.comparisons <- function(object, ...) { if (!is.null(object$estimate)) { out <- object$estimate if (is.null(names(out))) { lab <- tryCatch(get_term_labels(object), error = function(e) NULL) if (length(lab) == length(out)) { out <- stats::setNames(out, lab) } } return(out) } else { stop("The input object does not contain an 'estimate' element.") } } #' @export #' @noRd coef.slopes <- coef.comparisons #' @export #' @noRd coef.marginalmeans <- coef.comparisons #' @export #' @noRd coef.predictions <- coef.comparisons #' @export #' @noRd coef.hypotheses <- coef.comparisonsmarginaleffects/R/get_hypothesis.R0000644000176200001440000003463414631452335017006 0ustar liggesusers get_hypothesis <- function( x, hypothesis, by = NULL, newdata = NULL, draws = NULL) { if (is.null(hypothesis)) return(x) if (isTRUE(checkmate::check_choice(hypothesis, "meandev"))) { hypothesis <- function(x) { out <- x out$estimate <- out$estimate - mean(out$estimate) out$hypothesis <- "Mean deviation" return(out) } } else if (isTRUE(checkmate::check_choice(hypothesis, "meanotherdev"))) { hypothesis <- function(x) { out <- x s <- sum(out$estimate) m_other <- (s - out$estimate) / (nrow(x) - 1) out$estimate <- out$estimate - m_other out$hypothesis <- "Mean deviation (other)" return(out) } } if (is.function(hypothesis)) { if (!is.null(draws)) { msg <- "The `hypothesis` argument does not support function for models with draws. You can use `posterior_draws()` to extract draws and manipulate them directly instead." insight::format_error(msg) } if ("rowid" %in% colnames(x) && "rowid" %in% colnames(newdata)) { x <- merge(x, newdata, all.x = TRUE, by = intersect(colnames(x), colnames(newdata))) } else if (isTRUE(nrow(x) == nrow(newdata))) { x <- cbind(x, newdata) } attr(x, "variables_datagrid") <- attr(newdata, "variables_datagrid") attr(x, "by") <- if (is.character(by)) by else names(by) argnames <- names(formals(hypothesis)) if (!"x" %in% argnames) insight::format_error("The `hypothesis` function must accept an `x` argument.") if (any(!argnames %in% c("x", "draws"))) { msg <- "The allowable arguments for the `hypothesis` function are: `x` and `draws`" insight::format_error(msg) } args <- list(x = x, newdata = newdata, by = by, draws = draws) args <- args[names(args) %in% argnames] out <- do.call(hypothesis, args) at <- attr(out, "hypothesis_function_by") # sanity msg <- "The `hypothesis` argument function must return a data frame with `term` (or `hypothesis`) and `estimate` columns." if (inherits(out, "data.frame")) { if (!all(c("term", "estimate") %in% colnames(out)) && !all(c("hypothesis", "estimate") %in% colnames(out))) { insight::format_error(msg) } } else if (isTRUE(checkmate::check_numeric(out))) { if (isTRUE(checkmate::check_data_frame(x, nrows = length(out))) && "term" %in% colnames(out)) { out <- data.frame(term = out$term, estimate = out) } else { out <- data.frame(term = seq_along(out), estimate = out) } } else { insight::format_error(msg) } attr(out, "hypothesis_function_by") <- at return(out) } lincom <- NULL # lincom: numeric vector or matrix if (isTRUE(checkmate::check_numeric(hypothesis))) { if (isTRUE(checkmate::check_atomic_vector(hypothesis))) { checkmate::assert_numeric(hypothesis, len = nrow(x)) lincom <- as.matrix(hypothesis) } else if (isTRUE(checkmate::check_matrix(hypothesis))) { lincom <- hypothesis } } # lincom: string shortcuts valid <- c("pairwise", "reference", "sequential", "revpairwise", "revreference", "revsequential") if (isTRUE(hypothesis %in% valid)) { safe <- isFALSE(getOption("marginaleffects_safe", default = TRUE)) if (nrow(x) > 25 && !safe) { msg <- 'The "pairwise", "reference", and "sequential" options of the `hypotheses` argument are not supported for `marginaleffects` commands which generate more than 25 rows of results. Use the `newdata`, `by`, and/or `variables` arguments to compute a smaller set of results on which to conduct hypothesis tests.' insight::format_error(msg) } } if (isTRUE(hypothesis == "reference")) { lincom <- lincom_reference(x, by) } else if (isTRUE(hypothesis == "revreference")) { lincom <- lincom_revreference(x, by) } else if (isTRUE(hypothesis == "sequential")) { lincom <- lincom_sequential(x, by) } else if (isTRUE(hypothesis == "revsequential")) { lincom <- lincom_revsequential(x, by) } else if (isTRUE(hypothesis == "pairwise")) { lincom <- lincom_pairwise(x, by) } else if (isTRUE(hypothesis == "revpairwise")) { lincom <- lincom_revpairwise(x, by) } lincom <- sanitize_lincom(lincom, x) # matrix hypothesis if (isTRUE(checkmate::check_matrix(lincom))) { out <- lincom_multiply(x, lincom) return(out) # string hypothesis } else if (is.character(hypothesis)) { out_list <- draws_list <- list() lab <- attr(hypothesis, "label") tmp <- expand_wildcard(hypothesis, nrow(x), lab) hypothesis <- tmp[[1]] labs <- tmp[[2]] for (i in seq_along(hypothesis)) { out_list[[i]] <- eval_string_hypothesis(x, hypothesis[i], labs[i]) draws_list[[i]] <- attr(out_list[[i]], "posterior_draws") } out <- do.call(rbind, out_list) attr(out, "posterior_draws") <- do.call(rbind, draws_list) attr(out, "label") <- if (!is.null(attr(labs, "names"))) { attr(labs, "names") } else { labs } return(out) } insight::format_error("`hypotheses` is broken. Please report this bug: https://github.com/vincentarelbundock/marginaleffects/issues.") } get_hypothesis_row_labels <- function(x, by = NULL) { lab <- grep("^term$|^by$|^group$|^value$|^contrast$|^contrast_", colnames(x), value = TRUE) lab <- Filter(function(z) length(unique(x[[z]])) > 1, lab) if (isTRUE(checkmate::check_character(by))) { lab <- unique(c(lab, by)) } if (length(lab) == 0) { lab <- NULL } else { lab_df <- data.frame(x)[, lab, drop = FALSE] idx <- vapply(lab_df, FUN = function(x) length(unique(x)) > 1, FUN.VALUE = logical(1)) if (sum(idx) > 0) { lab <- apply(lab_df[, idx, drop = FALSE], 1, paste, collapse = ", ") } else { lab <- apply(lab_df, 1, paste, collapse = ", ") } } # wrap in parentheses to avoid a-b-c-d != (a-b)-(c-d) if (any(grepl("-", lab))) { lab <- sprintf("(%s)", lab) } return(lab) } sanitize_lincom <- function(lincom, x) { if (isTRUE(checkmate::check_matrix(lincom))) { checkmate::assert_matrix(lincom, nrows = nrow(x)) if (is.null(colnames(lincom))) { colnames(lincom) <- rep("custom", ncol(lincom)) } } return(lincom) } lincom_revreference <- function(x, by) { lincom <- -1 * diag(nrow(x)) lincom[1, ] <- 1 lab <- get_hypothesis_row_labels(x, by = by) if (length(lab) == 0 || anyDuplicated(lab) > 0) { lab <- sprintf("Row 1 - Row %s", seq_len(ncol(lincom))) } else { lab <- sprintf("%s - %s", lab[1], lab) } colnames(lincom) <- lab lincom <- lincom[, 2:ncol(lincom), drop = FALSE] return(lincom) } lincom_reference <- function(x, by) { lincom <- diag(nrow(x)) lincom[1, ] <- -1 lab <- get_hypothesis_row_labels(x, by = by) if (length(lab) == 0 || anyDuplicated(lab) > 0) { lab <- sprintf("Row %s - Row 1", seq_len(ncol(lincom))) } else { lab <- sprintf("%s - %s", lab, lab[1]) } colnames(lincom) <- lab lincom <- lincom[, 2:ncol(lincom), drop = FALSE] return(lincom) } lincom_revsequential <- function(x, by) { lincom <- matrix(0, nrow = nrow(x), ncol = nrow(x) - 1) lab <- get_hypothesis_row_labels(x, by = by) if (length(lab) == 0 || anyDuplicated(lab) > 0) { lab <- sprintf("Row %s - Row %s", seq_len(ncol(lincom)), seq_len(ncol(lincom)) + 1) } else { lab <- sprintf("%s - %s", lab[seq_len(ncol(lincom))], lab[seq_len(ncol(lincom)) + 1]) } for (i in seq_len(ncol(lincom))) { lincom[i:(i + 1), i] <- c(1, -1) } colnames(lincom) <- lab return(lincom) } lincom_sequential <- function(x, by) { lincom <- matrix(0, nrow = nrow(x), ncol = nrow(x) - 1) lab <- get_hypothesis_row_labels(x, by = by) if (length(lab) == 0 || anyDuplicated(lab) > 0) { lab <- sprintf("Row %s - Row %s", seq_len(ncol(lincom)) + 1, seq_len(ncol(lincom))) } else { lab <- sprintf("%s - %s", lab[seq_len(ncol(lincom)) + 1], lab[seq_len(ncol(lincom))]) } for (i in seq_len(ncol(lincom))) { lincom[i:(i + 1), i] <- c(-1, 1) } colnames(lincom) <- lab return(lincom) } lincom_revpairwise <- function(x, by) { lab_row <- get_hypothesis_row_labels(x, by = by) lab_col <- NULL flag <- length(lab_row) == 0 || anyDuplicated(lab_row) > 0 mat <- list() for (i in seq_len(nrow(x))) { for (j in 2:nrow(x)) { if (i < j) { tmp <- matrix(0, nrow = nrow(x), ncol = 1) tmp[i, ] <- -1 tmp[j, ] <- 1 mat <- c(mat, list(tmp)) if (isTRUE(flag)) { lab_col <- c(lab_col, sprintf("Row %s - Row %s", j, i)) } else { lab_col <- c(lab_col, sprintf("%s - %s", lab_row[j], lab_row[i])) } } } } lincom <- do.call("cbind", mat) colnames(lincom) <- lab_col return(lincom) } lincom_pairwise <- function(x, by) { lab_row <- get_hypothesis_row_labels(x, by = by) lab_col <- NULL flag <- length(lab_row) == 0 || anyDuplicated(lab_row) > 0 mat <- list() for (i in seq_len(nrow(x))) { for (j in 2:nrow(x)) { if (i < j) { tmp <- matrix(0, nrow = nrow(x), ncol = 1) tmp[j, ] <- -1 tmp[i, ] <- 1 mat <- c(mat, list(tmp)) if (isTRUE(flag)) { lab_col <- c(lab_col, sprintf("Row %s - Row %s", i, j)) } else { lab_col <- c(lab_col, sprintf("%s - %s", lab_row[i], lab_row[j])) } } } } lincom <- do.call("cbind", mat) colnames(lincom) <- lab_col return(lincom) } lincom_multiply <- function(x, lincom) { # bayesian draws <- attr(x, "posterior_draws") if (!is.null(draws)) { draws <- t(as.matrix(lincom)) %*% draws out <- data.table( term = colnames(lincom), tmp = apply(draws, 1, stats::median)) setnames(out, old = "tmp", new = "estimate") attr(out, "posterior_draws") <- draws # frequentist } else { out <- data.table( term = colnames(lincom), tmp = as.vector(x[["estimate"]] %*% lincom)) setnames(out, old = "tmp", new = "estimate") } out <- out[out$term != "1 - 1", , drop = FALSE] return(out) } eval_string_hypothesis <- function(x, hypothesis, lab) { # row indices: `hypotheses` includes them, but `term` does not if (isTRUE(grepl("\\bb\\d+\\b", hypothesis)) && !any(grepl("\\bb\\d+\\b", x[["term"]]))) { bmax <- regmatches(lab, gregexpr("\\bb\\d+\\b", lab))[[1]] bmax <- tryCatch(max(as.numeric(gsub("b", "", bmax))), error = function(e) 0) if (bmax > nrow(x)) { msg <- "%s cannot be used in `hypothesis` because the call produced just %s estimate(s). Try executing the exact same command without the `hypothesis` argument to see which estimates are available for hypothesis testing." msg <- sprintf(msg, paste0("b", bmax), nrow(x)) insight::format_error(msg) } for (i in seq_len(nrow(x))) { tmp <- paste0("marginaleffects__", i) hypothesis <- gsub(paste0("b", i), tmp, hypothesis) } rowlabels <- paste0("marginaleffects__", seq_len(nrow(x))) # term names } else { if (!"term" %in% colnames(x) || anyDuplicated(x$term) > 0) { msg <- c( 'To use term names in a `hypothesis` string, the same function call without `hypothesis` argument must produce a `term` column with unique row identifiers. You can use `b1`, `b2`, etc. indices instead of term names in the `hypotheses` string Ex: "b1 + b2 = 0" Alternatively, you can use the `newdata`, `variables`, or `by` arguments:', "", "mod <- lm(mpg ~ am * vs + cyl, data = mtcars)", 'comparisons(mod, newdata = "mean", hypothesis = "b1 = b2")', 'comparisons(mod, newdata = "mean", hypothesis = "am = vs")', 'comparisons(mod, variables = "am", by = "cyl", hypothesis = "pairwise")') insight::format_error(msg) } rowlabels <- x$term } eval_string_function <- function(vec, hypothesis, rowlabels) { envir <- parent.frame() void <- sapply( seq_along(vec), function(i) { assign(rowlabels[i], vec[i], envir = envir) }) out <- eval(parse(text = hypothesis), envir = envir) return(out) } if (!is.null(attr(lab, "names"))) { lab = attr(lab, "names") } else { lab = gsub("\\s+", "", lab) } draws <- attr(x, "posterior_draws") if (!is.null(draws)) { insight::check_if_installed("collapse", minimum_version = "1.9.0") tmp <- apply( draws, MARGIN = 2, FUN = eval_string_function, hypothesis = hypothesis, rowlabels = rowlabels) draws <- matrix(tmp, ncol = ncol(draws)) out <- data.table( term = lab, tmp = collapse::dapply(draws, MARGIN = 1, FUN = collapse::fmedian)) } else { out <- eval_string_function( x[["estimate"]], hypothesis = hypothesis, rowlabels = rowlabels) out <- data.table( term = lab, tmp = out) } setnames(out, old = "tmp", new = "estimate") attr(out, "posterior_draws") <- draws return(out) } expand_wildcard <- function(hyp, bmax, lab) { # Find all occurrences of b* bstar_indices <- gregexpr("b\\*", hyp)[[1]] if (bstar_indices[1] == -1) return(list(hyp, lab)) bstar_count <- length(bstar_indices) if (bstar_count > 1) { insight::format_error("Error: More than one 'b*' substring found.") } # Replace b* with b1, b2, b3, ..., bmax labs <- character(bmax) result <- character(bmax) for (i in 1:bmax) { result[i] <- sub("b\\*", paste0("b", i), hyp) labs[i] <- sub("b\\*", paste0("b", i), lab) } return(list(result, labs)) } marginaleffects/R/broom.R0000644000176200001440000000436114631452335015060 0ustar liggesusers#' @importFrom generics tidy #' @export generics::tidy #' @importFrom generics glance #' @export generics::glance #' tidy helper #' #' @noRd #' @export tidy.comparisons <- function(x, ...) { insight::check_if_installed("tibble") out <- tibble::as_tibble(x) if (!"term" %in% names(out)) { lab <- seq_len(nrow(out)) if ("group" %in% colnames(out) || is.character(attr(x, "by"))) { tmp <- c("group", attr(x, "by")) tmp <- Filter(function(j) j %in% colnames(x), tmp) if (length(tmp) > 0) { tmp <- do.call(paste, out[, tmp]) if (anyDuplicated(tmp)) { tmp <- paste(seq_len(nrow(out)), tmp) } lab <- tmp } } out[["term"]] <- lab } return(out) } #' tidy helper #' #' @noRd #' @export tidy.slopes <- tidy.comparisons #' tidy helper #' #' @noRd #' @export tidy.predictions <- tidy.comparisons #' tidy helper #' #' @noRd #' @export tidy.hypotheses <- tidy.comparisons #' tidy helper #' #' @noRd #' @export tidy.marginalmeans <- function(x, ...) { insight::check_if_installed("tibble") tibble::as_tibble(x) } #' @noRd #' @export glance.slopes <- function(x, ...) { insight::check_if_installed("insight") insight::check_if_installed("modelsummary") model <- tryCatch(attr(x, "model"), error = function(e) NULL) if (is.null(model) || isTRUE(checkmate::check_string(model))) { model <- tryCatch(attr(x, "call")[["model"]], error = function(e) NULL) } gl <- suppressMessages(suppressWarnings(try( modelsummary::get_gof(model, ...), silent = TRUE))) if (inherits(gl, "data.frame")) { out <- data.frame(gl) } else { out <- NULL } vcov.type <- attr(x, "vcov.type") if (is.null(out) && !is.null(vcov.type)) { out <- data.frame("vcov.type" = vcov.type) } else if (!is.null(out)) { out[["vcov.type"]] <- vcov.type } out <- tibble::as_tibble(out) return(out) } #' @noRd #' @export glance.predictions <- glance.slopes #' @noRd #' @export glance.comparisons <- glance.slopes #' @noRd #' @export glance.hypotheses <- glance.slopes #' @noRd #' @export glance.marginalmeans <- glance.slopesmarginaleffects/R/methods_survey.R0000644000176200001440000000552014631452335017020 0ustar liggesusers#' @include get_coef.R #' @rdname get_coef #' @export get_coef.svyolr <- function(model, ...) { out <- insight::get_parameters(model) out <- stats::setNames(out$Estimate, out$Parameter) return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.svyolr <- function(model, coefs, ...) { # in basic model classes coefficients are named vector idx <- match(names(model$coefficients), names(coefs)) model[["coefficients"]] <- coefs[idx] idx <- match(names(model$zeta), names(coefs)) model[["zeta"]] <- coefs[idx] model } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.svyolr <- function(model, ...) { resp <- insight::get_response(model) if (is.factor(resp)) { out <- levels(resp) } else { out <- unique(resp) } return(out) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.svyolr <- function(model, newdata = insight::get_data(model), type = "probs", ...) { type <- sanitize_type(model, type, calling_function = "predictions") # hack: 1-row newdata returns a vector, so get_predict.default does not learn about groups if (nrow(newdata) == 1) { hack <- TRUE newdata <- newdata[c(1, 1), , drop = FALSE] newdata$rowid[1] <- -Inf } else { hack <- FALSE } out <- get_predict.default(model, newdata = newdata, type = type, ...) # hack out <- out[out$rowid != -Inf, ] return(out) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.svyglm <- function(model, newdata = insight::get_data(model), type = "response", se.fit = FALSE, ...) { estimate <- stats::predict(model, newdata = newdata, type = type, se.fit = se.fit) rowid <- attr(estimate, "names") if (is.null(rowid)) { rowid <- seq_len(estimate) } else { # rowid might be character, but survey::predict() requires non-negative integers rowid <- seq_along(rowid) } out <- data.frame(rowid, estimate = as.numeric(estimate)) row.names(out) <- NULL return(out) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @export sanitize_model_specific.svyolr <- function(model, wts = FALSE, by = FALSE, ...) { if (isFALSE(wts) && !isFALSE(by)) { warning("With models of this class, it is normally good practice to specify weights using the `wts` argument. Otherwise, weights will be ignored in the computation of quantities of interest.", call. = FALSE) } return(model) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @export sanitize_model_specific.svyglm <- sanitize_model_specific.svyolr marginaleffects/R/methods_biglm.R0000644000176200001440000000203514631452335016553 0ustar liggesusers#' @rdname get_predict #' @export get_predict.biglm <- function(model, newdata = insight::get_data(model), type = "response", ...) { type <- sanitize_type(model, type, calling_function = "predictions") type_base <- unname(type) out <- stats::predict( model, newdata = newdata, type = type) out <- as.vector(out) out <- data.frame( rowid = seq_along(out), estimate = out) return(out) } #' @rdname get_vcov #' @export get_vcov.biglm <- function(model, vcov = NULL, ...) { if (!isFALSE(vcov)) { insight::format_warning(c("The `vcov` argument is not supported for this model type. Set `vcov=FALSE` to silence this warning, and visit this link to learn why standard errors for this model are not yet supported and how you can help:", "https://github.com/vincentarelbundock/marginaleffects/issues/387")) } return(FALSE) } marginaleffects/R/bootstrap_boot.R0000644000176200001440000000530314631452335016777 0ustar liggesusersbootstrap_boot <- function(model, INF_FUN, ...) { # attached by `inferences()` conf_type <- attr(model, "inferences_conf_type") checkmate::assert_choice(conf_type, choices = c("perc", "norm", "basic", "bca")) # bootstrap using the original data and call modcall <- insight::get_call(model) modeldata <- get_modeldata(model, additional_variables = FALSE) # evaluate the {marginaleffects} call to get output without inferences() # use ... because arguments are not the same for different {marginaleffects} functions dots <- list(...) dots[["vcov"]] <- FALSE # avoid recursion attr(model, "inferences_method") <- NULL out <- do.call(INF_FUN, c(list(model), dots)) # default confidence level may be implicit in original call, but we need numeric if (is.null(dots[["conf_level"]])) { conf_level <- 0.95 } else { conf_level <- dots[["conf_level"]] } bootfun <- function(data, indices) { d <- data[indices, , drop = FALSE] modcall[["data"]] <- d modboot <- eval(modcall) modboot <- eval(modboot) args <- c(list(modboot, modeldata = d), dots) out <- do.call(INF_FUN, args)$estimate return(out) } args <- list("data" = modeldata, "statistic" = bootfun) args <- c(args, attr(model, "inferences_dots")) args <- args[unique(names(args))] B <- do.call(boot::boot, args) # print.boot prints an ugly nested call B$call <- match.call() # HACK: boot::boot() output is non-standard. There must be a better way! # NG: just compute them manually as the SD of the bootstrap distribution pr <- utils::capture.output(print(B)) pr <- pr[(grep("^Bootstrap Statistics :", pr) + 1):length(pr)] pr <- gsub("std. error", "std.error", pr) pr <- paste(pr, collapse = "\n") pr <- utils::read.table(text = pr, header = TRUE) out$std.error <- pr$std.error # extract from weird boot.ci() list (inspired from `broom::tidy.broom` under MIT) ci_list <- lapply(seq_along(B$t0), boot::boot.ci, boot.out = B, conf = conf_level, type = conf_type) pos <- pmatch(conf_type, names(ci_list[[1]])) if (conf_type == "norm") { cols <- 2:3 } else { cols <- 4:5 } ci <- lapply(ci_list, function(x) x[[pos]]) ci <- do.call("rbind", ci)[, cols] # add CI to original {marginaleffects} call if (is.matrix(ci)) { out$conf.low <- ci[, 1] out$conf.high <- ci[, 2] } else { out$conf.low <- ci[1] out$conf.high <- ci[2] } attr(out, "inferences") <- B attr(out, "posterior_draws") <- t(B$t) return(out) } marginaleffects/R/sanity_model.R0000644000176200001440000001005414631452335016425 0ustar liggesusers#' Method to raise model-specific warnings and errors #' #' @inheritParams slopes #' @return A warning, an error, or nothing #' @rdname sanitize_model_specific #' @keywords internal sanitize_model_specific <- function (model, ...) { UseMethod("sanitize_model_specific", model) } #' @rdname sanitize_model_specific sanitize_model_specific.default <- function(model, vcov = NULL, calling_function = "marginaleffects", ...) { return(model) } sanity_model_supported_class <- function(model) { checkmate::assert_character( getOption("marginaleffects_model_classes", default = NULL), null.ok = TRUE) custom_classes <- getOption("marginaleffects_model_classes", default = NULL) custom_classes <- as.list(custom_classes) supported <- append(custom_classes, list( "afex_aov", "amest", #package: Amelia "bart", # package: dbarts "betareg", "bglmerMod", "blmerMod", # "bife", "biglm", c("bigglm", "biglm"), "brglmFit", "brmsfit", c("bracl", "brmultinom", "brglmFit"), c("brnb", "negbin", "glm"), c("clogit", "coxph"), "clm", c("clmm2", "clm2"), "coxph", "crch", "flexsurvreg", # package: flexsurv "fixest", "flic", "flac", c("Gam", "glm", "lm"), # package: gam c("gam", "glm", "lm"), # package: mgcv c("gamlss", "gam", "glm", "lm"), # package: gamlss c("geeglm", "gee", "glm"), c("Gls", "rms", "gls"), "glm", "gls", "glmerMod", "glmrob", "glmmTMB", c("glmmPQL", "lme"), "glimML", "glmx", "hetprob", "hurdle", "hxlr", "ivreg", "iv_robust", "ivpml", "Learner", "lm", "lme", "lmerMod", "lmerModLmerTest", "lmrob", "lmRob", "lm_robust", # "logitr", "loess", "logistf", c("lrm", "lm"), c("lrm", "rms", "glm"), c("mblogit", "mclogit"), c("mclogit", "lm"), "MCMCglmm", "mhurdle", "mira", "mlogit", "model_fit", c("multinom", "nnet"), "mvgam", c("negbin", "glm", "lm"), "nls", c("ols", "rms", "lm"), c("orm", "rms"), c("oohbchoice", "dbchoice"), "phylolm", "phyloglm", c("plm", "panelmodel"), "polr", "Rchoice", "rendo.base", "rlmerMod", "rq", c("scam", "glm", "lm"), c("selection", "selection", "list"), "speedglm", "speedlm", "stanreg", "survreg", "svyolr", c("tobit", "survreg"), "tobit1", "truncreg", "workflow", "zeroinfl")) flag <- FALSE for (sup in supported) { if (!is.null(sup) && isTRUE(all(sup %in% class(model)))) { flag <- TRUE } } if (isFALSE(flag)) { support <- paste(sort(unique(sapply(supported, function(x) x[1]))), collapse = ", ") msg <- c( sprintf('Models of class "%s" are not supported. Supported model classes include:', class(model)[1]), "", support, "", "New modeling packages can usually be supported by `marginaleffects` if they include a working `predict()` method. If you believe that this is the case, please file a feature request on Github: https://github.com/vincentarelbundock/marginaleffects/issues") msg <- insight::format_message(msg) stop(msg, call. = FALSE) } } sanitize_model <- function(model, newdata = NULL, vcov = NULL, by = FALSE, ...) { model <- sanitize_model_specific(model, vcov = vcov, newdata = newdata, by = by, ...) sanity_model_supported_class(model) return(model) } marginaleffects/R/methods_plm.R0000644000176200001440000000131614631452335016252 0ustar liggesusers#' @include sanity_model.R #' @rdname sanitize_model_specific #' @keywords internal sanitize_model_specific.plm <- function(model, ...) { if ("within" %in% model$args$model) { stop('The `plm::predict` function does not appear to support the `newdata` argument when `plm(model="within")`. Therefore, `marginaleffects` cannot support "within" models, even if it supports many other models produced by the `plm` package. You may want to try the `fixest` package instead.', call. = FALSE) } return(model) } #' @rdname sanitize_model_specific sanitize_model_specific.plm <- function(model, ...) { insight::check_if_installed("insight", minimum_version = "0.17.1") return(model) } marginaleffects/R/bootstrap_fwb.R0000644000176200001440000000365314631452335016620 0ustar liggesusersbootstrap_fwb <- function(model, INF_FUN, ...) { # attached by `inferences()` conf_type <- attr(model, "inferences_conf_type") checkmate::assert_choice(conf_type, choices = c("perc", "norm", "basic", "bc", "bca")) # bootstrap using the original data and call modeldata <- get_modeldata(model, additional_variables = FALSE) # evaluate the {marginaleffects} call to get output without inferences() # use ... because arguments are not the same for different {marginaleffects} functions dots <- list(...) dots[["vcov"]] <- FALSE # avoid recursion attr(model, "inferences_method") <- NULL out <- do.call(INF_FUN, c(list(model), dots)) # default confidence level may be implicit in original call, but we need numeric if (is.null(dots[["conf_level"]])) { conf_level <- 0.95 } else { conf_level <- dots[["conf_level"]] } bootfun <- function(data, w) { # If model has weights, multiply them by random weights if (!is.null(w0 <- stats::weights(model))) w <- w * w0 # Update the model's call and evaluate modboot <- stats::update(model, weights = w, evaluate = TRUE) # {marginaleffects} function needs to incorporate weights if # averaging. May be a problem if other weights supplied to # `wts` argument. dots[["wts"]] <- w args <- c(list(modboot), dots) out <- do.call(INF_FUN, args)$estimate return(out) } args <- list("data" = modeldata, "statistic" = bootfun) args <- c(args, attr(model, "inferences_dots")) args <- args[unique(names(args))] B <- do.call(fwb::fwb, args) # print.boot prints an ugly nested call B$call <- match.call() # Extract SEs and CIs fwb_summary <- summary(B, conf = conf_level, ci.type = conf_type) out$std.error <- fwb_summary[, "Std. Error"] out$conf.low <- fwb_summary[, 3] out$conf.high <- fwb_summary[, 4] attr(out, "inferences") <- B attr(out, "posterior_draws") <- t(B$t) return(out) } marginaleffects/R/modelarchive.R0000644000176200001440000000105214631452335016376 0ustar liggesusers# unexported functions for use in tests modelarchive_model <- function(name) { tmp <- tempfile() url <- paste0("https://raw.github.com/vincentarelbundock/modelarchive/main/data/", name, ".rds") try(utils::download.file(url, tmp, quiet = TRUE), silent = TRUE) out <- try(readRDS(tmp), silent = TRUE) return(out) } modelarchive_data <- function(name) { dat <- sprintf( "https://raw.githubusercontent.com/vincentarelbundock/modelarchive/main/data-raw/%s.csv", name) out <- utils::read.csv(dat) return(out) }marginaleffects/R/sort.R0000644000176200001440000000233414631452335014727 0ustar liggesusers sort_columns <- function(x, newdata = data.frame(), by = NULL) { if (!inherits(x, "data.table")) { data.table::setDT(x) } if (isTRUE(checkmate::check_character(by))) { bycols <- by } else if (isTRUE(checkmate::check_data_frame(by))) { bycols <- colnames(by) } else { bycols <- NULL } stubcols <- c( "rowid", "rowidcf", "term", "group", "hypothesis", "by", grep("^contrast", colnames(x), value = TRUE), bycols, "estimate", "std.error", "statistic", "p.value", "s.value", "conf.low", "conf.high", attr(newdata, "newdata_variables_datagrid"), "marginaleffects_wts", sort(grep("^predicted", colnames(newdata), value = TRUE))) cols <- intersect(stubcols, colnames(x)) cols <- unique(c(cols, colnames(x))) x <- x[, ..cols] if ("group" %in% names(x) && all(x$group == "main_marginaleffect")) { x$group <- NULL } # return contrast column only when relevant if ("contrast" %in% colnames(x)) { x[is.na(contrast), "contrast" := ""] x[contrast == "dydx", "contrast" := "dY/dX"] if (all(x$contrast == "dY/dX")) { x[, "contrast" := NULL] } } return(x) }marginaleffects/R/methods_rms.R0000644000176200001440000000167514631452335016273 0ustar liggesusers#' @rdname get_vcov #' @export get_vcov.orm <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !isTRUE(checkmate::check_flag(vcov))) { msg <- "The `vcov` argument is not supported for models of this class." insight::format_error(msg) } out <- stats::vcov(model, intercepts = "all") return(out) } #' @rdname get_predict #' @export get_predict.rms <- function(model, newdata = insight::get_data(model), type = NULL, ...) { if (is.null(type)) { type <- sanitize_type(model, type, calling_function = "predictions") } # {rms} predict methods break on additional arguments get_predict.default(model, newdata = newdata, type = type) } #' @rdname get_predict #' @export get_predict.orm <- get_predict.rms #' @rdname get_predict #' @export get_predict.lrm <- get_predict.rms #' @rdname get_predict #' @export get_predict.ols <- get_predict.rmsmarginaleffects/R/methods_dbarts.R0000644000176200001440000000275614631452335016752 0ustar liggesusers#' @include get_predict.R #' @rdname get_predict #' @export get_predict.bart <- function(model, newdata = NULL, ...) { args <- c( list( object = model, newdata = newdata), list(...)) p <- do.call(stats::predict, args) p_med <- collapse::fmedian(p) if ("rowid" %in% colnames(newdata) && nrow(newdata) == length(p_med)) { out <- data.frame( rowid = newdata$rowid, group = "main_marginaleffect", estimate = p_med ) } else { out <- data.frame( rowid = seq_along(length(p_med)), group = "main_marginaleffect", estimate = p_med ) } attr(out, "posterior_draws") <- t(p) return(out) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @export sanitize_model_specific.bart <- function(model, ...) { insight::check_if_installed("collapse", minimum_version = "1.9.0") if (!isTRUE(as.character(insight::get_call(model))[1] %in% c("bart2", "dbarts::bart2"))) { msg <- "`marginaleffects` only supports models estimated using the formula interface in `bart2()` function, not the matrix input in `bart()`." insight::format_error(msg) } return(model) } #' @rdname get_vcov #' @export get_vcov.bart <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !is.logical(vcov)) { insight::format_warning("The `vcov` argument is not supported for models of this class.") } return(NULL) }marginaleffects/R/myTryCatch.R0000644000176200001440000000072314631452335016027 0ustar liggesusers# License: https://creativecommons.org/licenses/by-sa/3.0/ # Source: https://stackoverflow.com/a/24569739/342331 myTryCatch <- function(expr) { warn <- err <- NULL value <- withCallingHandlers( tryCatch(expr, error = function(e) { err <<- e NULL }), warning = function(w) { warn <<- w invokeRestart("muffleWarning") } ) list(value = value, warning = warn, error = err) } marginaleffects/R/sanity_dots.R0000644000176200001440000000775214631452335016311 0ustar liggesusers# This function is very strict. sanity_dots <- function(model, calling_function = NULL, ...) { dots <- list(...) if (isTRUE(calling_function == "marginaleffects")) { # comparison: this would break `dydx` normalization # interaction: cross countrast+slope do not make sense # transform: should we really be back-transforming slopes? unsupported <- c("comparison", "transform", "cross", "transform_pre", "transform_post") unsupported <- intersect(names(dots), unsupported) if (length(unsupported) > 0) { msg <- sprintf( "These arguments are supported by the `comparisons()` function but not by the `slopes()` function: %s", paste(unsupported, collapse = ", ")) stop(msg, call. = FALSE) } } # deprecated if ("interaction" %in% names(dots)) { msg <- "The `interaction` argument has been deprecated. Please use `cross` instead." insight::format_warning(msg) } valid <- list() # mixed effects valid[["merMod"]] <- valid[["lmerMod"]] <- valid[["glmerMod"]] <- valid[["lmerModLmerTest"]] <- c("include_random", "re.form", "allow.new.levels", "random.only") valid[["brmsfit"]] <- c("draw_ids", "nlpar", "ndraws", "re_formula", "allow_new_levels", "sample_new_levels", "dpar", "resp") valid[["brmsfit_multiple"]] <- valid[["brmsfit"]] valid[["selection"]] <- c("part") # sampleSelection valid[["glmmTMB"]] <- c("re.form", "allow.new.levels", "zitype") # glmmTMB valid[["bam"]] <- c("exclude") # mgcv valid[["gam"]] <- c("exclude") # mgcv valid[["rlmerMod"]] <- c("re.form", "allow.new.levels") valid[["gamlss"]] <- c("what", "safe") # gamlss valid[["lme"]] <- c("level") # nlme::lme valid[["bife"]] <- c("alpha_new", "corrected") # nlme::lme valid[["process_error"]] <- # mvgam::mvgam # flexsurv valid[["flexsurvreg"]] <- c("times", "p", "start") white_list <- c( "conf.int", "modeldata", "internal_call", "df", "transform", "comparison", "side", "delta", "null", "equivalence", "draw", "flag", # internal dev "transform_pre", "transform_post", # backward compatibility everywhere "variables_grid", # backward compatibility in marginal_means() "at" # topmodels procast ) model_class <- class(model)[1] good <- NULL if (model_class %in% names(valid)) { good <- valid[[model_class]] } backward_compatibility <- c("conf.level") good <- c(good, backward_compatibility) bad <- setdiff(names(dots), c(good, white_list)) if (length(bad) > 0) { if (model_class %in% names(valid)) { msg <- sprintf("These arguments are not known to be supported for models of class `%s`: %s. These arguments are known to be valid: %s. All arguments are still passed to the model-specific prediction function, but users are encouraged to check if the argument is indeed supported by their modeling package. Please file a request on Github if you believe that an unknown argument should be added to the `marginaleffects` white list of known arguments, in order to avoid raising this warning: https://github.com/vincentarelbundock/marginaleffects/issues", model_class, paste(bad, collapse = ", "), paste(valid[[model_class]], collapse = ", ")) } else { msg <- sprintf("These arguments are not known to be supported for models of class `%s`: %s. All arguments are still passed to the model-specific prediction function, but users are encouraged to check if the argument is indeed supported by their modeling package. Please file a request on Github if you believe that an unknown argument should be added to the `marginaleffects` white list of known arguments, in order to avoid raising this warning: https://github.com/vincentarelbundock/marginaleffects/issues", model_class, paste(bad, collapse = ", ")) } warning(msg, call. = FALSE) } } marginaleffects/R/datagrid.R0000644000176200001440000005133614631452335015525 0ustar liggesusers#' Data grids #' #' @description #' Generate a data grid of user-specified values for use in the `newdata` argument of the `predictions()`, `comparisons()`, and `slopes()` functions. This is useful to define where in the predictor space we want to evaluate the quantities of interest. Ex: the predicted outcome or slope for a 37 year old college graduate. #' #' @param ... named arguments with vectors of values or functions for user-specified variables. #' + Functions are applied to the variable in the `model` dataset or `newdata`, and must return a vector of the appropriate type. #' + Character vectors are automatically transformed to factors if necessary. #' +The output will include all combinations of these variables (see Examples below.) #' @param model Model object #' @param newdata data.frame (one and only one of the `model` and `newdata` arguments can be used.) #' @param by character vector with grouping variables within which `FUN_*` functions are applied to create "sub-grids" with unspecified variables. #' @param response Logical should the response variable be included in the grid, even if it is not specified explicitly. #' @param FUN_character the function to be applied to character variables. #' @param FUN_factor the function to be applied to factor variables. This only applies if the variable in the original data is a factor. For variables converted to factor in a model-fitting formula, for example, `FUN_character` is used. #' @param FUN_logical the function to be applied to logical variables. #' @param FUN_integer the function to be applied to integer variables. #' @param FUN_binary the function to be applied to binary variables. #' @param FUN_numeric the function to be applied to numeric variables. #' @param FUN_other the function to be applied to other variable types. #' @param grid_type character. Determines the functions to apply to each variable. The defaults can be overridden by defining individual variables explicitly in `...`, or by supplying a function to one of the `FUN_*` arguments. #' * "mean_or_mode": Character, factor, logical, and binary variables are set to their modes. Numeric, integer, and other variables are set to their means. #' * "balanced": Each unique level of character, factor, logical, and binary variables are preserved. Numeric, integer, and other variables are set to their means. Warning: When there are many variables and many levels per variable, a balanced grid can be very large. In those cases, it is better to use `grid_type="mean_or_mode"` and to specify the unique levels of a subset of named variables explicitly. #' * "counterfactual": the entire dataset is duplicated for each combination of the variable values specified in `...`. Variables not explicitly supplied to `datagrid()` are set to their observed values in the original dataset. #' @details #' If `datagrid` is used in a `predictions()`, `comparisons()`, or `slopes()` call as the #' `newdata` argument, the model is automatically inserted in the `model` argument of `datagrid()` #' call, and users do not need to specify either the `model` or `newdata` arguments. The same behavior will occur when the value supplied to `newdata=` is a function call which starts with "datagrid". This is intended to allow users to create convenience shortcuts like: #' #' \preformatted{ #' library(marginaleffects) #' mod <- lm(mpg ~ am + vs + factor(cyl) + hp, mtcars) #' datagrid_bal <- function(...) datagrid(..., grid_type = "balanced") #' predictions(model, newdata = datagrid_bal(cyl = 4)) #' } #' #' #' If users supply a model, the data used to fit that model is retrieved using #' the `insight::get_data` function. #' @return #' A `data.frame` in which each row corresponds to one combination of the named #' predictors supplied by the user via the `...` dots. Variables which are not #' explicitly defined are held at their mean or mode. #' @export #' @examples #' # The output only has 2 rows, and all the variables except `hp` are at their #' # mean or mode. #' datagrid(newdata = mtcars, hp = c(100, 110)) #' #' # We get the same result by feeding a model instead of a data.frame #' mod <- lm(mpg ~ hp, mtcars) #' datagrid(model = mod, hp = c(100, 110)) #' #' # Use in `marginaleffects` to compute "Typical Marginal Effects". When used #' # in `slopes()` or `predictions()` we do not need to specify the #' #`model` or `newdata` arguments. #' slopes(mod, newdata = datagrid(hp = c(100, 110))) #' #' # datagrid accepts functions #' datagrid(hp = range, cyl = unique, newdata = mtcars) #' comparisons(mod, newdata = datagrid(hp = fivenum)) #' #' # The full dataset is duplicated with each observation given counterfactual #' # values of 100 and 110 for the `hp` variable. The original `mtcars` includes #' # 32 rows, so the resulting dataset includes 64 rows. #' dg <- datagrid(newdata = mtcars, hp = c(100, 110), grid_type = "counterfactual") #' nrow(dg) #' #' # We get the same result by feeding a model instead of a data.frame #' mod <- lm(mpg ~ hp, mtcars) #' dg <- datagrid(model = mod, hp = c(100, 110), grid_type = "counterfactual") #' nrow(dg) datagrid <- function( ..., model = NULL, newdata = NULL, by = NULL, grid_type = "mean_or_mode", response = FALSE, FUN_character = NULL, FUN_factor = NULL, FUN_logical = NULL, FUN_numeric = NULL, FUN_integer = NULL, FUN_binary = NULL, FUN_other = NULL) { dots <- list(...) # backward compatibility: 20231220 if (identical(grid_type, "typical")) { grid_type <- "mean_or_mode" } # sanity checkmate::assert_choice(grid_type, choices = c("mean_or_mode", "balanced", "counterfactual")) checkmate::assert_function(FUN_character, null.ok = TRUE) checkmate::assert_function(FUN_factor, null.ok = TRUE) checkmate::assert_function(FUN_logical, null.ok = TRUE) checkmate::assert_function(FUN_binary, null.ok = TRUE) checkmate::assert_function(FUN_integer, null.ok = TRUE) checkmate::assert_function(FUN_numeric, null.ok = TRUE) checkmate::assert_function(FUN_other, null.ok = TRUE) checkmate::assert_character(by, null.ok = TRUE) checkmate::assert_data_frame(newdata, null.ok = TRUE) checkmate::assert_flag(response) if (grid_type == "mean_or_mode") { if (is.null(FUN_character)) FUN_character <- get_mode if (is.null(FUN_logical)) FUN_logical <- get_mode if (is.null(FUN_factor)) FUN_factor <- get_mode if (is.null(FUN_binary)) FUN_binary <- get_mode if (is.null(FUN_numeric)) FUN_numeric <- function(x) mean(x, na.rm = TRUE) if (is.null(FUN_other)) FUN_other <- function(x) mean(x, na.rm = TRUE) if (is.null(FUN_integer)) FUN_integer <- function(x) round(mean(x, na.rm = TRUE)) } else if (grid_type == "balanced") { if (is.null(FUN_character)) FUN_character <- unique if (is.null(FUN_logical)) FUN_logical <- unique # not just levels(), because that is string, and sorts badly ex: "2" vs "10" if (is.null(FUN_factor)) FUN_factor <- function(k) sort(unique(k)) if (is.null(FUN_binary)) FUN_binary <- unique if (is.null(FUN_numeric)) FUN_numeric <- function(x) mean(x, na.rm = TRUE) if (is.null(FUN_other)) FUN_other <- function(x) mean(x, na.rm = TRUE) if (is.null(FUN_integer)) FUN_integer <- function(x) round(mean(x, na.rm = TRUE)) } else if (grid_type == "counterfactual") { if (!is.null(by)) { insight::format_error("The `by` argument is not supported for counterfactual grids.") } args <- list( model = model, newdata = newdata) args <- c(dots, args) out <- do.call("datagridcf_internal", args) return(out) } if (!is.null(by)) { if (is.null(newdata) && is.null(model)) { insight::format_error("One of `newdata` and `model` must not be `NULL`.") } if (is.null(newdata)) { newdata <- get_modeldata(model, additional_variables = by) } if (!all(by %in% colnames(newdata))) { insight::format_error("All elements of `by` must match column names in `newdata`.") } data.table::setDT(newdata) idx <- subset(newdata, select = by) newdata_list <- split(newdata, idx, keep.by = TRUE) for (i in seq_along(newdata_list)) { args <- c(list(...), list( model = model, newdata = newdata_list[[i]], response = response, FUN_character = FUN_character, FUN_factor = FUN_factor, FUN_logical = FUN_logical, FUN_binary = FUN_binary, FUN_numeric = FUN_numeric, FUN_integer = FUN_integer, FUN_other = FUN_other, by = by)) for (b in by) { args[[b]] <- unique } newdata_list[[i]] <- do.call(datagrid_engine, args) } # Issue 1058: missing attributes with `by` at <- attributes(newdata_list[[1]]) out <- data.table::rbindlist(newdata_list) data.table::setDF(out) # Issue 1058: missing attributes with `by` # overwriting everything corrupts the data frame for (n in names(at)) { if (!n %in% names(attributes(out))) { attr(out, n) <- at[[n]] } } return(out) } out <- datagrid_engine(..., model = model, newdata = newdata, response = response, FUN_character = FUN_character, FUN_factor = FUN_factor, FUN_logical = FUN_logical, FUN_binary = FUN_binary, FUN_numeric = FUN_numeric, FUN_integer = FUN_integer, FUN_other = FUN_other) if (!"rowid" %in% colnames(out)) { out$rowid <- seq_len(nrow(out)) } return(out) } datagrid_engine <- function( ..., model = NULL, newdata = NULL, response = response, FUN_character = get_mode, # need to be explicit for numeric variables transfered to factor in model formula FUN_factor = get_mode, FUN_logical = get_mode, FUN_binary = get_mode, FUN_numeric = function(x) mean(x, na.rm = TRUE), FUN_integer = function(x) round(mean(x, na.rm = TRUE)), FUN_other = function(x) mean(x, na.rm = TRUE), by = NULL) { dots <- list(...) tmp <- prep_datagrid(..., model = model, newdata = newdata, by = by) at <- tmp$at dat <- tmp$newdata variables_all <- tmp$all variables_manual <- names(at) variables_automatic <- tmp$automatic # usually we don't want the response in the grid, but # sometimes there are two responses and we need one of them: # brms::brm(y | trials(n) ~ x + w + z) if (!is.null(model) && isFALSE(response)) { resp <- insight::find_response(model) if (inherits(model, "brmsfit")) { fl <- as.character(stats::formula(model)) matches <- regexpr("trials\\(.*?\\)", fl) extracted <- regmatches(fl, matches)[1] if (isFALSE(is.na(extracted[1]))) { extracted <- gsub("trials\\((.*)\\)", "\\1", extracted) } else { extracted <- NULL } extracted <- unlist(extracted) resp <- setdiff(resp, extracted) } variables_automatic <- setdiff(variables_automatic, resp) } if (length(variables_automatic) > 0) { idx <- intersect(variables_automatic, colnames(dat)) dat_automatic <- dat[, ..idx, drop = FALSE] dat_automatic <- stats::na.omit(dat_automatic) out <- list() # na.omit destroys attributes, and we need the "factor" attribute # created by insight::get_data for (n in names(dat_automatic)) { if (get_variable_class(dat, n, c("factor", "strata", "cluster")) || n %in% tmp[["cluster"]]) { if (is.factor(dat_automatic[[n]])) { out[[n]] <- FUN_factor(dat_automatic[[n]]) } else { out[[n]] <- FUN_character(dat_automatic[[n]]) } } else if (get_variable_class(dat, n, "binary")) { out[[n]] <- FUN_binary(dat_automatic[[n]]) } else if (get_variable_class(dat, n, "logical")) { out[[n]] <- FUN_logical(dat_automatic[[n]]) } else if (get_variable_class(dat, n, "character")) { out[[n]] <- FUN_character(dat_automatic[[n]]) } else if (get_variable_class(dat, n, "numeric")) { if (is.integer(dat_automatic[[n]])) { out[[n]] <- FUN_integer(dat_automatic[[n]]) } else { out[[n]] <- FUN_numeric(dat_automatic[[n]]) } } else { out[[n]] <- FUN_other(dat_automatic[[n]]) } } } else { out <- list() } if (!is.null(at)) { for (n in names(at)) { out[n] <- at[n] } } # unique before counting out <- lapply(out, unique) # warn on very large prediction grid num <- as.numeric(sapply(out, length)) # avoid integer overflow num <- Reduce(f = "*", num) if (isTRUE(num > 1e9)) { stop("You are trying to create a prediction grid with more than 1 billion rows, which is likely to exceed the memory and computational power available on your local machine. Presumably this is because you are considering many variables with many levels. All of the functions in the `marginaleffects` package include arguments to specify a restricted list of variables over which to create a prediction grid.", call. = FALSE) } fun <- data.table::CJ args <- c(out, list(sorted = FALSE)) out <- do.call("fun", args) # na.omit destroys attributes, and we need the "factor" attribute # created by insight::get_data for (n in names(out)) { attr(out, "marginaleffects_variable_class") <- attr(dat, "marginaleffects_variable_class") } # better to assume "standard" class as output data.table::setDF(out) attr(out, "variables_datagrid") <- names(dots) return(out) } datagridcf_internal <- function( ..., model = NULL, newdata = NULL) { dots <- list(...) if (length(dots) == 0) { insight::format_error("Users must specify variable values when `grid_type='counterfactual'") } tmp <- prep_datagrid(..., model = model, newdata = newdata) at <- tmp$at dat <- tmp$newdata variables_all <- tmp$all variables_manual <- names(at) variables_automatic <- c(tmp$automatic, "marginaleffects_wts_internal", "rowid_dedup") # `at` -> `data.frame` at <- lapply(at, unique) fun <- data.table::CJ args <- c(at, list(sorted = FALSE)) at <- do.call("fun", args) rowid <- data.frame(rowidcf = seq_len(nrow(dat))) if (length(variables_automatic) > 0) { idx <- intersect(variables_automatic, colnames(dat)) dat_automatic <- dat[, ..idx, drop = FALSE] dat_automatic[, rowidcf := rowid$rowidcf] setcolorder(dat_automatic, c("rowidcf", setdiff(names(dat_automatic), "rowidcf"))) # cross-join 2 data.tables, faster than merging two dataframes out <- cjdt(list(dat_automatic, at)) } else { out <- merge(rowid, at, all = TRUE) } data.table::setDF(out) attr(out, "variables_datagrid") <- names(out) return(out) } prep_datagrid <- function(..., model = NULL, newdata = NULL, by = NULL) { checkmate::assert_data_frame(newdata, null.ok = TRUE) at <- list(...) # e.g., mlogit vignette we plot by group, but group is of length 0 because # we don't know how many groups there are until we make the first # prediction. for (i in seq_along(at)) { if (length(at[[i]]) == 0) { at[[i]] <- NULL } } # if (!is.null(model) & !is.null(newdata)) { # msg <- "One of the `model` or `newdata` arguments must be `NULL`." # stop(msg, call. = FALSE) # } if (is.null(model) & is.null(newdata)) { msg <- "The `model` and `newdata` arguments are both `NULL`. When calling `datagrid()` *inside* the `slopes()` or `comparisons()` functions, the `model` and `newdata` arguments can both be omitted. However, when calling `datagrid()` on its own, users must specify either the `model` or the `newdata` argument (but not both)." insight::format_error(msg) } if (!is.null(model)) { variables_list <- insight::find_variables(model) variables_all <- unlist(variables_list, recursive = TRUE) # weights are not extracted by default variables_all <- c(variables_all, insight::find_weights(model)) } else if (!is.null(newdata)) { variables_list <- NULL variables_all <- colnames(newdata) newdata <- set_variable_class(modeldata = newdata, model = model) } variables_manual <- names(at) variables_automatic <- setdiff(variables_all, variables_manual) # fill in missing data after sanity checks if (is.null(newdata)) { newdata <- get_modeldata(model, additional_variables = FALSE) } attr_variable_classes <- attr(newdata, "marginaleffects_variable_class") # subset columns, otherwise it can be ultra expensive to compute summaries for every variable if (!is.null(model)) { variables_sub <- c( hush(insight::find_variables(model, flatten = TRUE)), hush(unlist(insight::find_weights(model), use.names = FALSE))) # glmmTMB needs weights column for predictions variables_sub <- c(variables_sub, variables_manual) variables_sub <- c(variables_sub, c("marginaleffects_wts_internal", "rowid_dedup")) variables_sub <- intersect(colnames(newdata), variables_sub) if (length(variables_sub) > 0) { newdata <- subset(newdata, select = variables_sub) } } # check `at` names variables_missing <- setdiff(names(at), c(variables_all, "group", by)) if (length(variables_missing) > 0) { warning(sprintf("Some of the variable names are missing from the model data: %s", paste(variables_missing, collapse = ", ")), call. = FALSE) } idx <- vapply(newdata, is.matrix, logical(1L)) if (any(idx)) { if (any(names(newdata)[idx] %in% variables_all)) { insight::format_warning("Matrix columns are not supported as predictors and are therefore omitted. This may prevent computation of the quantities of interest. You can construct your own prediction dataset and supply it explicitly to the `newdata` argument. This issue can happen when using functions like `scale()`, which returns matrices rather than vectors. Convert your column to a vector before fitting you model.") } newdata <- newdata[, !idx, drop = FALSE] } # restore attributes after subsetting attr(newdata, "marginaleffects_variable_class") <- attr_variable_classes # check `at` elements and convert them to factor as needed for (n in names(at)) { # functions first otherwise we try to coerce functions to character if (is.function(at[[n]])) { modeldata <- attr(newdata, "newdata_modeldata") if (!is.null(modeldata) && n %in% colnames(modeldata)) { at[[n]] <- at[[n]](modeldata[[n]]) } else { at[[n]] <- at[[n]](newdata[[n]]) } } # not an "else" situation because we want to process the output of functions too if (is.factor(newdata[[n]]) || isTRUE(get_variable_class(newdata, n, "factor"))) { if (is.factor(newdata[[n]])) { levs <- levels(newdata[[n]]) } else { levs <- as.character(sort(unique(newdata[[n]]))) } at[[n]] <- as.character(at[[n]]) if (!all(at[[n]] %in% c(levs, NA))) { msg <- sprintf('The "%s" element of the `at` list corresponds to a factor variable. The values entered in the `at` list must be one of the factor levels: "%s".', n, paste(levels(newdata[[n]]), collapse = '", "')) stop(msg, call. = FALSE) } else { at[[n]] <- factor(at[[n]], levels = levs) } } } # cluster identifiers will eventually be treated as factors if (!is.null(model)) { v <- insight::find_variables(model) v <- unlist(v[names(v) %in% c("cluster", "strata")], recursive = TRUE) variables_cluster <- c(v, insight::find_random(model, flatten = TRUE)) } else { variables_cluster <- NULL } data.table::setDT(newdata) out <- list("newdata" = newdata, "at" = at, "all" = variables_all, "manual" = variables_manual, "automatic" = variables_automatic, "cluster" = variables_cluster) return(out) } marginaleffects/R/methods_ordinal.R0000644000176200001440000000410214631452335017106 0ustar liggesusers#' @rdname get_predict #' @export get_predict.clm <- function(model, newdata = insight::get_data(model), type = "prob", ...) { # `predict.clm()` only makes predictions for the observed response group of # each observation in `newdata`. When we remove the response from # `newdata`, `predict.clm()` makes predictions for all levels, which is # what we want. resp <- insight::find_response(model) # otherwise `predict.clm` does not see some columns (mystery) data.table::setDF(newdata) newdata <- newdata[, setdiff(colnames(newdata), resp), drop = FALSE] pred <- stats::predict(model, newdata = newdata, type = type) contenders <- c("fit", "eta1", "eta2", "cprob1", "cprob2") tmp <- NULL for (con in contenders) { if (is.null(tmp) && con %in% names(pred)) { tmp <- pred[[con]] } } pred <- tmp out <- data.frame( group = rep(colnames(pred), each = nrow(pred)), estimate = c(pred)) out$group <- group_to_factor(out$group, model) # often an internal call if ("rowid" %in% colnames(newdata)) { out$rowid <- rep(newdata$rowid, times = ncol(pred)) } else { out$rowid <- rep(1:nrow(pred), times = ncol(pred)) } return(out) } #' @include get_group_names.R #' @rdname get_group_names #' @export get_group_names.clm <- get_group_names.polr #' @include sanity_model.R #' @rdname sanitize_model_specific #' @keywords internal sanitize_model_specific.clm <- function(model, ...) { # Corner case: The `predict.clm` method does not make predictions when the # response was transformed to a factor in the formula AND the response is # missing from `newdata`. lhs <- names(attr(stats::terms(model), "dataClasses"))[1] if (isTRUE(grepl("^factor\\(", lhs))) { stop("The response variable should not be transformed to a factor in the formula. Please convert the variable to factor before fitting your model.", call. = FALSE) } return(model) } marginaleffects/R/sanitize_interaction.R0000644000176200001440000000140414631452335020162 0ustar liggesuserssanitize_cross <- function(cross, variables, model) { # cross: flip NULL to TRUE if there are interactions in the formula # and FALSE otherwise checkmate::assert_flag(cross, null.ok = FALSE) if (isTRUE(cross) && is.null(variables)) { msg <- "When `cross = TRUE`, you must use the `variables` argument to specify which variables should be interacted." insight::format_error(msg) } if (isTRUE(checkmate::check_flag(cross))) { return(cross) } inter <- try(insight::find_interactions(model, flatten = TRUE), silent = TRUE) # variables is length 1 means we don't want an interaction if (length(variables) > 1 && isTRUE(length(inter) > 0)) { return(TRUE) } else { return(FALSE) } } marginaleffects/R/methods_lme4.R0000644000176200001440000000344714631452335016332 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @keywords internal #' @export set_coef.merMod <- function(model, coefs, ...) { # in 'merMod', predictions work the slot called "beta", which is unnamed # `fixef(model)` returns the same thing named beta <- methods::slot(model, "beta") beta[match(names(coefs), names(lme4::fixef(model)))] <- as.numeric(coefs) methods::slot(model, "beta") <- beta model } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.merMod <- function(model, ...) { lme4::fixef(model) } #' @rdname get_predict #' @export get_predict.merMod <- function(model, newdata = insight::get_data(model), type = "response", ...) { get_predict.default(model, newdata = newdata, type = type, ...) } #' @rdname set_coef #' @export set_coef.lmerModLmerTest <- set_coef.merMod #' @rdname get_coef #' @export get_coef.lmerModLmerTest <- get_coef.merMod #' @rdname get_predict #' @export get_predict.lmerModLmerTest <- get_predict.merMod #' @rdname set_coef #' @export set_coef.lmerMod <- set_coef.merMod #' @rdname get_coef #' @export get_coef.lmerMod <- get_coef.merMod #' @rdname get_predict #' @export get_predict.lmerMod <- get_predict.merMod #' @rdname sanitize_model_specific #' @export sanitize_model_specific.merMod <- function(model, re.form = NULL, ...) { # re.form=NA if (!isTRUE(is.na(re.form))) { msg <- "For this model type, `marginaleffects` only takes into account the uncertainty in fixed-effect parameters. You can use the `re.form=NA` argument to acknowledge this explicitly and silence this warning." insight::format_warning(msg) } return(model) } marginaleffects/R/sanitize_comparison.R0000644000176200001440000001070714631452335020023 0ustar liggesuserswmean <- function(x, w) { stats::weighted.mean(x, w) } comparison_function_dict <- list( # default = difference between predictions "difference" = function(hi, lo) hi - lo, "differenceavg" = function(hi, lo) mean(hi - lo), "differenceavgwts" = function(hi, lo, w) wmean(hi - lo, w), # slopes and elasticities "dydx" = function(hi, lo, eps) (hi - lo) / eps, "eyex" = function(hi, lo, eps, y, x) (hi - lo) / eps * (x / y), "eydx" = function(hi, lo, eps, y, x) ((hi - lo) / eps) / y, "dyex" = function(hi, lo, eps, x) ((hi - lo) / eps) * x, # average slopes and elasticities "dydxavg" = function(hi, lo, eps) mean((hi - lo) / eps), "eyexavg" = function(hi, lo, eps, y, x) mean((hi - lo) / eps * (x / y)), "eydxavg" = function(hi, lo, eps, y, x) mean(((hi - lo) / eps) / y), "dyexavg" = function(hi, lo, eps, x) mean(((hi - lo) / eps) * x), "dydxavgwts" = function(hi, lo, eps, w) wmean((hi - lo) / eps, w), "eyexavgwts" = function(hi, lo, eps, y, x, w) wmean((hi - lo) / eps * (x / y), w), "eydxavgwts" = function(hi, lo, eps, y, x, w) wmean(((hi - lo) / eps) / y, w), "dyexavgwts" = function(hi, lo, eps, x, w) wmean(((hi - lo) / eps) * x, w), # ratios "ratio" = function(hi, lo) hi / lo, "ratioavg" = function(hi, lo) mean(hi) / mean(lo), "ratioavgwts" = function(hi, lo, w) wmean(hi, w) / wmean(lo, w), "lnratio" = function(hi, lo) log(hi / lo), "lnratioavg" = function(hi, lo) log(mean(hi) / mean(lo)), "lnratioavgwts" = function(hi, lo, w) log(wmean(hi, w) / wmean(lo, w)), "lnor" = function(hi, lo) log((hi / (1 - hi)) / (lo / (1 - lo))), "lnoravg" = function(hi, lo) log((mean(hi) / (1 - mean(hi))) / (mean(lo) / (1 - mean(lo)))), "lnoravgwts" = function(hi, lo, w) log((wmean(hi, w) / (1 - wmean(hi, w))) / (wmean(lo, w) / (1 - wmean(lo, w)))), # others "lift" = function(hi, lo) (hi - lo) / lo, "liftavg" = function(hi, lo) (mean(hi - lo)) / mean(lo), "expdydx" = function(hi, lo, eps) ((exp(hi) - exp(lo)) / exp(eps)) / eps, "expdydxavg" = function(hi, lo, eps) mean(((exp(hi) - exp(lo)) / exp(eps)) / eps), "expdydxavgwts" = function(hi, lo, eps, w) wmean(((exp(hi) - exp(lo)) / exp(eps)) / eps, w) ) comparison_label_dict <- list( "difference" = "%s - %s", "differenceavg" = "mean(%s) - mean(%s)", "differenceavgwts" = "mean(%s) - mean(%s)", "dydx" = "dY/dX", "eyex" = "eY/eX", "eydx" = "eY/dX", "dyex" = "dY/eX", "dydxavg" = "mean(dY/dX)", "eyexavg" = "mean(eY/eX)", "eydxavg" = "mean(eY/dX)", "dyexavg" = "mean(dY/eX)", "dydxavg" = "mean(dY/dX)", "eyexavg" = "mean(eY/eX)", "eydxavg" = "mean(eY/dX)", "dyexavg" = "mean(dY/eX)", "dydxavgwts" = "mean(dY/dX)", "eyexavgwts" = "mean(eY/eX)", "eydxavgwts" = "mean(eY/dX)", "dyexavgwts" = "mean(dY/eX)", "ratio" = "%s / %s", "ratioavg" = "mean(%s) / mean(%s)", "ratioavgwts" = "mean(%s) / mean(%s)", "lnratio" = "ln(%s / %s)", "lnratioavg" = "ln(mean(%s) / mean(%s))", "lnratioavgwts" = "ln(mean(%s) / mean(%s))", "lnor" = "ln(odds(%s) / odds(%s))", "lnoravg" = "ln(odds(%s) / odds(%s))", "lnoravgwts" = "ln(odds(%s) / odds(%s))", "lift" = "lift", "liftavg" = "liftavg", "expdydx" = "exp(dY/dX)" ) sanity_comparison <- function(comparison) { # wts versions are used internally but not available directly to users valid <- names(comparison_function_dict) valid <- valid[!grepl("wts$", valid)] checkmate::assert( checkmate::check_choice(comparison, choices = valid), checkmate::check_function(comparison)) } sanitize_transform <- function(x) { good <- c("exp", "ln") # issue #1115: sanitize_transform() wraps `transform` into a named list, so the assertion may fail when using `inferences()` if (isTRUE(checkmate::check_list(x, names = "named"))) { checkmate::assert( checkmate::check_choice(x[[1]], choices = good, null.ok = TRUE), checkmate::check_function(x[[1]])) x <- x[[1]] } else { checkmate::assert( checkmate::check_choice(x, choices = good, null.ok = TRUE), checkmate::check_function(x)) } if (is.null(x)) { return(x) } else if (is.function(x)) { out <- list(x) names(out) <- deparse(substitute(x)) } else if (x == "exp") { out <- list("exp" = exp) } else if (x == "ln") { out <- list("ln" = log) } return(out) } marginaleffects/R/plot_slopes.R0000644000176200001440000001133314631452335016302 0ustar liggesusers#' Plot Conditional or Marginal Slopes #' #' @description #' Plot slopes 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 slopes, that is, slopes made on the original data, but averaged by subgroups. This is analogous to using the `by` argument in the `slopes()` function. #' #' The `condition` argument is used to plot conditional slopes, that is, slopes computed on a user-specified grid. This is analogous to using the `newdata` argument and `datagrid()` function in a `slopes()` 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 marginal effect (slope) we want to plot on the y-axis. #' @param condition Conditional slopes #' + Character vector (max length 4): Names of the predictors to display. #' + Named list (max length 4): List names correspond to predictors. List elements can be: #' - Numeric vector #' - Function which returns a numeric vector or a set of unique categorical values #' - Shortcut strings for common reference values: "minmax", "quartile", "threenum" #' + 1: x-axis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid). #' + Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers `?stats::fivenum`. #' @param rug TRUE displays tick marks on the axes to mark the distribution of raw data. #' @param gray FALSE grayscale or color plot #' @param draw `TRUE` returns a `ggplot2` plot. `FALSE` returns a `data.frame` of the underlying data. #' @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 `slopes()` function. #' @inheritParams slopes #' @template model_specific_arguments #' @return A `ggplot2` object #' @export #' @examples #' library(marginaleffects) #' mod <- lm(mpg ~ hp * drat * factor(am), data = mtcars) #' #' plot_slopes(mod, variables = "hp", condition = "drat") #' #' plot_slopes(mod, variables = "hp", condition = c("drat", "am")) #' #' plot_slopes(mod, variables = "hp", condition = list("am", "drat" = 3:5)) #' #' plot_slopes(mod, variables = "am", condition = list("hp", "drat" = range)) #' #' plot_slopes(mod, variables = "am", condition = list("hp", "drat" = "threenum")) #' plot_slopes <- function(model, variables = NULL, condition = NULL, by = NULL, newdata = NULL, type = "response", vcov = NULL, conf_level = 0.95, wts = FALSE, slope = "dydx", 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 (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 # if `newdata` is a call to `typical` or `counterfactual`, insert `model` # should probably not be nested too deeply in the call stack since we eval.parent() (not sure about this) scall <- rlang::enquo(newdata) newdata <- sanitize_newdata_call(scall, newdata, model) valid <- c("dydx", "eyex", "eydx", "dyex") checkmate::assert_choice(slope, choices = valid) out <- plot_comparisons( model, variables = variables, condition = condition, by = by, newdata = newdata, type = type, vcov = vcov, conf_level = conf_level, wts = wts, draw = draw, rug = rug, gray = gray, comparison = slope, ...) if (inherits(out, "ggplot")) { out <- out + ggplot2::labs(x = condition[1], y = "Slope") } return(out) } marginaleffects/R/package.R0000644000176200001440000000251114631452335015330 0ustar liggesusers# https://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when#comment20826625_12429344 # 2012 hadley says "globalVariables is a hideous hack and I will never use it" # 2014 hadley updates his own answer with globalVariables as one of "two solutions" #' @importFrom Rcpp evalCpp #' @useDynLib marginaleffects #' @import data.table utils::globalVariables(c( "marginaleffects_contrast_hi", "marginaleffects_contrast_lo", "..bycols", "..to_keep", "..cols", "..idx_by", "..idx_hi", "..cl", "..idx_lo", ".data", "..idx_or", "..idx", "..idx1", "..idx2", "..nonfocal", "rowidcf", "elast", "..tmp", "..variables_grid", "..w", "marginaleffects_lab", "marginaleffects_ter", "..label_columns", "marginaleffects_internal_label", ".", "tmp_idx", "comparison", "condition_variable", "conf_int", "conf_level", "conf.high", "conf.low", "contrast", "draw", "dydx", "eps", "estimates", "estimate", "formatBicLL", "gn", "group", "logLik", "m", "marginaleffects_function", "marginaleffects_wts_internal", "marginalmean", "model", "N", "p.value", "part", "patterns", "position", "predicted_hi", "predicted_lo", "predicted_or", "predicted", "rowid", "rowidunique", "hi", "lo", "section", "stars_note", "statistic", "statistic1", "std.error", "term", "ticks", "tmp_grp", "comparison_idx", "value", "where", "wts"))marginaleffects/R/sanitize_vcov.R0000644000176200001440000000243714631452335016627 0ustar liggesuserssanitize_vcov <- function(model, vcov) { # TRUE generates a warning in `insight::get_varcov` for some models if (isTRUE(checkmate::check_flag(vcov))) { return(NULL) } # no vcov matrix for bayesian models if (inherits(model, c("brmsfit", "stanreg", "bart"))) { return(NULL) } # strings should be case-insensitive vcov_strings <- c("stata", "robust", "HC", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5", "HAC", "NeweyWest", "kernHAC", "OPG", "satterthwaite", "kenward-roger") if (isTRUE(checkmate::check_choice(hush(tolower(vcov)), choices = tolower(vcov_strings)))) { idx <- match(tolower(vcov), tolower(vcov_strings)) return(vcov_strings[idx]) } checkmate::assert( checkmate::check_null(vcov), checkmate::check_function(vcov), checkmate::check_matrix(vcov), checkmate::check_formula(vcov), checkmate::check_choice(vcov, choices = vcov_strings)) out <- vcov if (isTRUE(checkmate::check_function(out))) { out <- hush(out(model)) } if (isTRUE(checkmate::check_matrix(out))) { if (ncol(out) != nrow(out)) { stop("The `vcov` matrix must be square.", call. = FALSE) } } return(out) }marginaleffects/R/methods_mgcv.R0000644000176200001440000000016614631452335016420 0ustar liggesusers#' @rdname get_coef #' @export get_coef.gam <- function(model, ...) { out <- model$coefficients return(out) } marginaleffects/R/methods_scam.R0000644000176200001440000000221314631452335016402 0ustar liggesusers#' @rdname set_coef #' @export set_coef.scam <- function(model, coefs, ...) { # in basic model classes coefficients are named vector model[["coefficients.t"]][names(coefs)] <- coefs model } #' @rdname get_coef #' @export get_coef.scam <- function(model, ...) { model$coefficients.t } #' @rdname get_vcov #' @export get_vcov.scam <- function(model, vcov = NULL, ...) { if (isTRUE(checkmate::check_matrix(vcov))) { return(vcov) } # email from developer Natalya Pya # "one of the elements of the returned 'scam' object is 'Vp.t' which is an # estimated covariance matrix for the reparametrized parameters, # 'model$coefficients.t'." if (!is.null(vcov) && !is.logical(vcov)) { stop("The `vcov` argument is not supported for models of class `scam`.", .call = FALSE) } V <- model$Vp.t b <- model$coefficients.t if (length(b) != nrow(V)) { stop("The size of the variance-covariance matrix does not match the length of the coefficients vector.", call. = FALSE) } colnames(V) <- row.names(V) <- names(b) return(V) } marginaleffects/R/methods_tidymodels.R0000644000176200001440000000464114631452335017643 0ustar liggesuserssupported_engine <- function(x) { insight::check_if_installed("parsnip") tmp <- parsnip::extract_fit_engine(x) flag <- inherits(try(sanitize_model(tmp), silent = TRUE), "try-error") return(!flag) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.model_fit <- function(model, coefs, ...) { if (!"fit" %in% names(model)) { return(model) } model$fit <- set_coef(model$fit, coefs, ...) return(model) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.workflow <- function(model, coefs, ...) { if ("fit" %in% names(model) && "fit" %in% names(model$fit)) { model$fit$fit <- set_coef(model$fit$fit, coefs, ...) } return(model) } #' @include get_predict.R #' @rdname get_predict #' @keywords internal #' @export get_predict.model_fit <- function(model, newdata, type = NULL, ...) { out <- stats::predict(model, new_data = newdata, type = type) if (type == "numeric") { v <- intersect(c(".pred", ".pred_res"), colnames(out))[1] out <- data.frame(rowid = seq_along(out), estimate = out[[v]]) } else if (type == "class") { out <- data.frame(rowid = seq_along(out), estimate = out[[".pred_class"]]) } else if (type == "prob") { colnames(out) <- substr(colnames(out), 7, nchar(colnames(out))) out$rowid <- seq_len(nrow(out)) out <- data.table::melt( out, id.vars = "rowid", variable.name = "group", value.name = "estimate") } return(out) } #' @include get_predict.R #' @rdname get_predict #' @keywords internal #' @export get_predict.workflow <- get_predict.model_fit #' @include get_vcov.R #' @rdname get_vcov #' @keywords internal #' @export get_vcov.model_fit <- function(model, type = NULL, ...) { if (isTRUE(type == "class")) { return(FALSE) } if (isTRUE(supported_engine(model))) { tmp <- parsnip::extract_fit_engine(model) out <- get_vcov(tmp) } else { out <- FALSE } return(out) } #' @include get_vcov.R #' @rdname get_vcov #' @keywords internal #' @export get_vcov.workflow <- get_vcov.model_fit #' @include get_coef.R #' @rdname get_coef #' @keywords internal #' @export get_coef.workflow <- function(model, ...) { if (isTRUE(supported_engine(model))) { tmp <- parsnip::extract_fit_engine(model) out <- get_coef(tmp) } else { out <- NULL } return(out) } marginaleffects/R/utils.R0000644000176200001440000001041314631452335015075 0ustar liggesusersget_unique_index <- function(x, term_only = FALSE) { idx <- c("term", "contrast", grep("^contrast_", colnames(x), value = TRUE)) if (!term_only) { by <- attr(x, "by") if (isTRUE(checkmate::check_data_frame(by))) { idx <- c(idx, colnames(by)) } else { idx <- c(idx, by) } explicit <- attr(x, "newdata_explicit") if (isTRUE(checkmate::check_character(explicit))) { idx <- explicit } } idx <- intersect(unique(idx), colnames(x)) if (length(idx) == 0) { return(NULL) } else if (length(idx) == 1) { return(x[[idx]]) } out <- x[, idx, drop = FALSE] for (i in ncol(out):2) { if (length(unique(out[[i]])) == 1) { out[[i]] <- NULL } } out <- apply(out, 1, paste, collapse = ", ") return(out) } get_marginaleffects_attributes <- function(x, exclude = NULL, include = NULL, include_regex = NULL) { out <- list() attr_names <- names(attributes(x)) attr_names <- setdiff(attr_names, exclude) if (!is.null(include)) attr_names <- intersect(attr_names, include) if (!is.null(include_regex)) attr_names <- attr_names[grepl(include_regex, attr_names)] for (n in attr_names) { out[[n]] <- attr(x, n) } return(out) } set_marginaleffects_attributes <- function(x, attr_cache, prefix = "") { for (n in names(attr_cache)) { attr(x, paste0(prefix, n)) <- attr_cache[[n]] } return(x) } warn_once <- function(msg, id) { msg <- c(msg, "", "This warning appears once per session.") if (isTRUE(getOption(id, default = TRUE))) { insight::format_warning(msg, call. = FALSE) opts <- list(FALSE) names(opts) <- id options(opts) } } # Cross join a list of data tables # Source: https://github.com/Rdatatable/data.table/issues/1717#issuecomment-545758165 cjdt <- function(dtlist) { Reduce(function(DT1, DT2) cbind(DT1, DT2[rep(1:.N, each = nrow(DT1))]), dtlist) } # recurse up. mostly useful for `tinytest` # this is dumb, but it's late and i don't feel like thinking about this evalup <- function(xcall) { out <- myTryCatch(eval(xcall)) if (inherits(out$error, "simpleError")) { msg <- out$error$message out <- NULL } else { msg <- NULL out <- out$value } for (i in 1:10) { if (is.null(out)) { out <- hush(eval(xcall, parent.frame(i))) } } if (is.null(out) && !is.null(msg)) stop(msg) return(out) } merge_by_rowid <- function(x, y) { # return data # very import to avoid sorting, otherwise bayesian draws won't fit predictions # merge only with rowid; not available for hypothesis mergein <- setdiff(colnames(y), colnames(x)) if ("rowid" %in% colnames(x) && "rowid" %in% colnames(y) && length(mergein) > 0) { idx <- c("rowid", mergein) if (!data.table::is.data.table(y)) { data.table::setDT(y) tmp <- y[, ..idx] } else { tmp <- y[, ..idx] } # TODO: this breaks in mclogit. maybe there's a more robust merge # solution for weird grouped data. But it seems fine because # `predictions()` output does include the original predictors. out <- tryCatch( merge(x, tmp, by = "rowid", sort = FALSE), error = function(e) x) } else { out <- x } return(out) } # faster than all(x %in% 0:1) is_binary <- function(x) { isTRUE(checkmate::check_integerish( x, null.ok = TRUE, upper = 1, lower = 0, any.missing = FALSE) ) } sub_named_vector <- function(x, y) { # issue 1005 xlab <- gsub("^`|`$", "", names(x)) ylab <- gsub("^`|`$", "", names(y)) idx <- match(ylab, xlab) if (length(stats::na.omit(idx)) > 0) { x[stats::na.omit(idx)] <- y[!is.na(idx)] } else if (length(y) == length(x)) { return(y) } else { stop("set_coef() substitution error. Please report on Github with a reproducible example: https://github.com/vincentarelbundock/marginaleffects/issues", call. = FALSE) } return(x) } group_to_factor <- function(group, model) { dv <- try(insight::get_response(model), silent = TRUE) if (inherits(dv, "factor")) { group <- factor(group, levels(dv)) } return(group) }marginaleffects/R/methods_tobit1.R0000644000176200001440000000057314631452335016670 0ustar liggesusers#' @rdname get_predict #' @export get_predict.tobit1 <- function(model, newdata = insight::get_data(model), type = "response", ...) { out <- stats::predict(model, what = type, newdata = newdata) out <- data.frame(rowid = seq_len(length(out)), estimate = out) return(out) } marginaleffects/R/methods_survival.R0000644000176200001440000000162414631452335017337 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.survreg <- function(model, coefs, ...) { #Reverse engineering insight::get_get_parameters.survreg(), #which uses summary.survreg() nvar0 <- length(model$coefficients) nvar <- nrow(model$var) if (nvar > nvar0) { model[["coefficients"]][] <- coefs[-nvar0] model[["scale"]][] <- exp(coefs[nvar0]) } else { model$coefficients[] <- coefs } model } #' @rdname get_predict #' @export get_predict.coxph <- function(model, newdata = insight::get_data(model), type = "lp", ...) { out <- stats::predict(model, newdata = newdata, type = type, ...) out <- data.frame(rowid = seq_len(nrow(newdata)), estimate = out) return(out) } marginaleffects/R/sanitize_hypothesis_formula.R0000644000176200001440000000222114631452335021565 0ustar liggesuserssanitize_hypothesis_formula <- function(hypothesis) { insight::check_if_installed("Formula") hypothesis <- Formula::Formula(hypothesis) if (!length(hypothesis)[1] %in% 0:1) { insight::format_error("The left-hand side of the `hypothesis` formula must be of length 0 or 1.") } if (!length(hypothesis)[2] %in% 1:2) { insight::format_error("The right-hand side of the `hypothesis` formula must have 1 or two parts, separated by |") } if (length(hypothesis)[1] == 0) { hypothesis <- stats::update(hypothesis, difference ~ .) } if (length(hypothesis)[2] == 1) { by <- NULL } else { by <- all.vars(stats::formula(hypothesis, lhs = 0, rhs = 2)) } lhs <- all.vars(stats::formula(hypothesis, rhs = 0)) rhs <- all.vars(stats::formula(hypothesis, lhs = 0, rhs = 1)) checkmate::assert_choice(lhs, c("difference", "ratio"), .var.name = "left-hand side of `hypothesis` formula") checkmate::assert_choice(rhs, c("reference", "sequential", "meandev"), .var.name = "Right-hand side of `hypothesis` formula") out <- specify_hypothesis( hypothesis = rhs, comparison = lhs, by = by, internal = TRUE) return(out) } marginaleffects/R/hypotheses.R0000644000176200001440000003516214631452335016140 0ustar liggesusers#' (Non-)Linear Tests for Null Hypotheses, Joint Hypotheses, Equivalence, Non Superiority, and Non Inferiority #' #' @description #' Uncertainty estimates are calculated as first-order approximate standard errors for linear or non-linear functions of a vector of random variables with known or estimated covariance matrix. In that sense, [`hypotheses`] emulates the behavior of the excellent and well-established [car::deltaMethod] and [car::linearHypothesis] functions, but it supports more models; requires fewer dependencies; expands the range of tests to equivalence and superiority/inferiority; and offers convenience features like robust standard errors. #' #' To learn more, read the hypothesis tests vignette, visit the #' package website, or scroll down this page for a full list of vignettes: #' #' * #' * #' #' Warning #1: Tests are conducted directly on the scale defined by the `type` argument. For some models, it can make sense to conduct hypothesis or equivalence tests on the `"link"` scale instead of the `"response"` scale which is often the default. #' #' Warning #2: For hypothesis tests on objects produced by the `marginaleffects` package, it is safer to use the `hypothesis` argument of the original function. Using `hypotheses()` may not work in certain environments, in lists, or when working programmatically with *apply style functions. #' #' Warning #3: The tests assume that the `hypothesis` expression is (approximately) normally distributed, which for non-linear functions of the parameters may not be realistic. More reliable confidence intervals can be obtained using the \code{inferences()} function with `method = "boot"`. #' #' @inheritParams comparisons #' @param model Model object or object generated by the `comparisons()`, `slopes()`, or `predictions()` functions. #' @param joint Joint test of statistical significance. The null hypothesis value can be set using the `hypothesis` argument. #' - FALSE: Hypotheses are not tested jointly. #' - TRUE: All parameters are tested jointly. #' - String: A regular expression to match parameters to be tested jointly. `grep(joint, perl = TRUE)` #' - Character vector of parameter names to be tested. Characters refer to the names of the vector returned by `coef(object)`. #' - Integer vector of indices. Which parameters positions to test jointly. #' @param joint_test A character string specifying the type of test, either "f" or "chisq". The null hypothesis is set by the `hypothesis` argument, with default null equal to 0 for all parameters. #' @param df Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and `Inf`. When using `joint_test="f"`, the `df` argument should be a numeric vector of length 2. #' #' @section Joint hypothesis tests: #' The test statistic for the joint Wald test is calculated as (R * theta_hat - r)' * inv(R * V_hat * R') * (R * theta_hat - r) / Q, #' where theta_hat is the vector of estimated parameters, V_hat is the estimated covariance matrix, R is a Q x P matrix for testing Q hypotheses on P parameters, #' r is a Q x 1 vector for the null hypothesis, and Q is the number of rows in R. If the test is a Chi-squared test, the test statistic is not normalized. #' #' The p-value is then calculated based on either the F-distribution (for F-test) or the Chi-squared distribution (for Chi-squared test). #' For the F-test, the degrees of freedom are Q and (n - P), where n is the sample size and P is the number of parameters. #' For the Chi-squared test, the degrees of freedom are Q. #' #' @template equivalence #' @examples #' library(marginaleffects) #' mod <- lm(mpg ~ hp + wt + factor(cyl), data = mtcars) #' #' hypotheses(mod) #' #' # Test of equality between coefficients #' hypotheses(mod, hypothesis = "hp = wt") #' #' # Non-linear function #' hypotheses(mod, hypothesis = "exp(hp + wt) = 0.1") #' #' # Robust standard errors #' hypotheses(mod, hypothesis = "hp = wt", vcov = "HC3") #' #' # b1, b2, ... shortcuts can be used to identify the position of the #' # parameters of interest in the output of #' hypotheses(mod, hypothesis = "b2 = b3") #' #' # wildcard #' hypotheses(mod, hypothesis = "b* / b2 = 1") #' #' # term names with special characters have to be enclosed in backticks #' hypotheses(mod, hypothesis = "`factor(cyl)6` = `factor(cyl)8`") #' #' mod2 <- lm(mpg ~ hp * drat, data = mtcars) #' hypotheses(mod2, hypothesis = "`hp:drat` = drat") #' #' # predictions(), comparisons(), and slopes() #' mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial) #' cmp <- comparisons(mod, newdata = "mean") #' hypotheses(cmp, hypothesis = "b1 = b2") #' #' mfx <- slopes(mod, newdata = "mean") #' hypotheses(cmp, hypothesis = "b2 = 0.2") #' #' pre <- predictions(mod, newdata = datagrid(hp = 110, mpg = c(30, 35))) #' hypotheses(pre, hypothesis = "b1 = b2") #' #' # The `hypothesis` argument can be used to compute standard errors for fitted values #' mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial) #' #' f <- function(x) predict(x, type = "link", newdata = mtcars) #' p <- hypotheses(mod, hypothesis = f) #' head(p) #' #' f <- function(x) predict(x, type = "response", newdata = mtcars) #' p <- hypotheses(mod, hypothesis = f) #' head(p) #' #' # Complex aggregation #' # Step 1: Collapse predicted probabilities by outcome level, for each individual #' # Step 2: Take the mean of the collapsed probabilities by group and `cyl` #' library(dplyr) #' library(MASS) #' library(dplyr) #' #' dat <- transform(mtcars, gear = factor(gear)) #' mod <- polr(gear ~ factor(cyl) + hp, dat) #' #' aggregation_fun <- function(x) { #' predictions(x, vcov = FALSE) |> #' mutate(group = ifelse(group %in% c("3", "4"), "3 & 4", "5")) |> #' summarize(estimate = sum(estimate), .by = c("rowid", "cyl", "group")) |> #' summarize(estimate = mean(estimate), .by = c("cyl", "group")) |> #' rename(term = cyl) #' } #' #' hypotheses(mod, hypothesis = aggregation_fun) #' #' # Equivalence, non-inferiority, and non-superiority tests #' mod <- lm(mpg ~ hp + factor(gear), data = mtcars) #' p <- predictions(mod, newdata = "median") #' hypotheses(p, equivalence = c(17, 18)) #' #' mfx <- avg_slopes(mod, variables = "hp") #' hypotheses(mfx, equivalence = c(-.1, .1)) #' #' cmp <- avg_comparisons(mod, variables = "gear", hypothesis = "pairwise") #' hypotheses(cmp, equivalence = c(0, 10)) #' #' # joint hypotheses: character vector #' model <- lm(mpg ~ as.factor(cyl) * hp, data = mtcars) #' hypotheses(model, joint = c("as.factor(cyl)6:hp", "as.factor(cyl)8:hp")) #' #' # joint hypotheses: regular expression #' hypotheses(model, joint = "cyl") #' #' # joint hypotheses: integer indices #' hypotheses(model, joint = 2:3) #' #' # joint hypotheses: different null hypotheses #' hypotheses(model, joint = 2:3, hypothesis = 1) #' hypotheses(model, joint = 2:3, hypothesis = 1:2) #' #' # joint hypotheses: marginaleffects object #' cmp <- avg_comparisons(model) #' hypotheses(cmp, joint = "cyl") #' #' @export hypotheses <- function( model, hypothesis = NULL, vcov = NULL, conf_level = 0.95, df = NULL, equivalence = NULL, joint = FALSE, joint_test = "f", numderiv = "fdforward", ...) { if (isTRUE(attr(model, "hypotheses_call"))) { msg <- "The `hypotheses()` function cannot be called twice on the same object." insight::format_error(msg) } dots <- list(...) # deprecation if ("FUN" %in% names(dots)) { msg <- "`FUN` is deprecated. Please supply your function to the `hypothesis` argument instead." if (is.null(hypothesis)) { hypothesis <- dots[["FUN"]] insight::format_warning(msg) } else { insight::format_error(msg) } } call_attr <- c(list( name = "hypotheses", model = model, hypothesis = hypothesis, vcov = vcov, conf_level = conf_level, df = df, equivalence = equivalence, joint = joint, joint_test = joint_test, numderiv = numderiv), dots) if ("modeldata" %in% names(dots)) { call_attr[["modeldata"]] <- dots[["modeldata"]] } call_attr <- do.call("call", call_attr) ## Bootstrap # restore an already sanitized hypothesis if necessary hypothesis <- if(is.null(attr(hypothesis, "label"))){ hypothesis } else{ attr(hypothesis, "label") } # Apply inferences method out <- inferences_dispatch( INF_FUN = hypotheses, model=model, hypothesis = hypothesis, vcov = vcov, conf_level = conf_level, df = df, equivalence = equivalence, joint = joint, joint_test = joint_test, numderiv = numderiv, ...) if (!is.null(out)) { return(out) } ## Done with Bootstrap if (!isFALSE(joint)) { out <- joint_test(object = model, joint_index = joint, joint_test = joint_test, hypothesis = hypothesis, df = df) return(out) } # after joint_test if (is.null(df)) df <- Inf args <- list( conf_level = conf_level, vcov = vcov, df = df, equivalence = equivalence) # keep this NULL in case `hypothesis` was used in the previous call args[["hypothesis"]] <- hypothesis if (length(dots) > 0) { args <- c(args, dots) } xcall <- substitute(model) if (is.symbol(xcall)) { model <- eval(xcall, envir = parent.frame()) } else if (is.call(xcall)) { internal <- c( "predictions", "avg_predictions", "comparisons", "avg_comparisons", "slopes", "avg_slopes", "marginal_means") # mfx object if (as.character(xcall)[[1]] %in% internal) { args[["x"]] <- model out <- do.call(recall, args) if (!is.null(out)) { class(out) <- c("hypotheses", class(out)) return(out) } # non-mfx object } else { model <- eval(xcall, envir = parent.frame()) } } # marginaleffects objects: recall() if (inherits(model, c("predictions", "comparisons", "slopes", "marginalmeans"))) { args[["x"]] <- attr(model, "call") out <- do.call(recall, args) if (!is.null(out)) { class(out) <- c("hypotheses", class(out)) return(out) } } numderiv = sanitize_numderiv(numderiv) # after re-evaluation tmp <- sanitize_hypothesis(hypothesis, ...) hypothesis <- tmp$hypothesis hypothesis_null <- tmp$hypothesis_null vcov_false <- isFALSE(vcov) vcov <- get_vcov(model = model, vcov = vcov) vcov.type <- get_vcov_label(vcov = vcov) FUNouter <- function(model, hypothesis) { if (inherits(model, c("predictions", "slopes", "comparisons"))) { out <- model } else if (isTRUE(checkmate::check_numeric(model))) { out <- data.frame(term = seq_along(out), estimate = out) } else if (inherits(model, "data.frame")) { out <- model if (any(!c("term", "estimate") %in% colnames(out))) { msg <- "`hypothesis` function must return a data.frame with two columns named `term` and `estimate`." insight::format_error(msg) } # unknown model } else if (!is.function(hypothesis)) { out <- insight::get_parameters(model, ...) idx <- intersect(colnames(model), c("term", "group", "estimate")) colnames(out)[1:2] <- c("term", "estimate") # unknown model but user-supplied hypothesis function } else { out <- model } tmp <- get_hypothesis(out, hypothesis = hypothesis) out <- tmp$estimate if (!is.null(attr(tmp, "label"))) { attr(out, "label") <- attr(tmp, "label") } else { attr(out, "label") <- tmp$term } if ("group" %in% colnames(tmp)) { attr(out, "grouplab") <- tmp[["group"]] } return(out) } b <- FUNouter(model = model, hypothesis = hypothesis) # bayesian posterior if (!is.null(attr(b, "posterior_draws"))) { draws <- attr(b, "posterior_draws") J <- NULL se <- rep(NA, length(b)) # standard errors via delta method } else if (!vcov_false && isTRUE(checkmate::check_matrix(vcov))) { args <- list(model = model, vcov = vcov, hypothesis = hypothesis, FUN = FUNouter, numderiv = numderiv) args <- c(args, dots) se <- do.call("get_se_delta", args) J <- attr(se, "jacobian") attr(se, "jacobian") <- NULL draws <- NULL # no standard error } else { J <- draws <- NULL se <- rep(NA, length(b)) } hyplab <- attr(b, "label") if (!is.null(hypothesis)) { if (is.null(hyplab)) { hyplab <- attr(hypothesis, "label") } if (!is.null(hyplab)) { out <- data.frame( term = hyplab, estimate = b, std.error = se) } else { out <- data.frame( term = "custom", estimate = b, std.error = se) } } else { if (!is.null(hyplab) && length(hyplab) == length(b)) { out <- data.frame( term = hyplab, estimate = b, std.error = se) } else { out <- data.frame( term = paste0("b", seq_along(b)), estimate = b, std.error = se) } } # Remove std.error column when not computing st.errors and in bootstrap if(vcov_false) { out$std.error <- NULL } out[["group"]] <- attr(b, "grouplab") out <- get_ci( out, conf_level = conf_level, vcov = vcov, draws = draws, estimate = "estimate", null_hypothesis = hypothesis_null, df = df, model = model, ...) if (!is.null(equivalence)) { out <- equivalence( out, df = df, equivalence = equivalence) } out <- sort_columns(out) class(out) <- c("hypotheses", "deltamethod", class(out)) attr(out, "posterior_draws") <- draws attr(out, "model") <- model attr(out, "model_type") <- class(model)[1] attr(out, "jacobian") <- J attr(out, "call") <- call_attr attr(out, "vcov") <- vcov attr(out, "vcov.type") <- vcov.type attr(out, "conf_level") <- conf_level # Issue #1102: hypotheses() should not be called twice on the same object attr(out, "hypotheses_call") <- TRUE return(out) } marginaleffects/R/complete_levels.R0000644000176200001440000000352714631452335017127 0ustar liggesusers#' Create a data.frame with all factor or character levels #' #' `model.matrix` breaks when `newdata` includes a factor #' variable, but not all levels are present in the data. This is bad for us #' because we often want to get predictions with one (or few) rows, where some #' factor levels are inevitably missing. #' @keywords internal complete_levels <- function(x, character_levels = NULL) { checkmate::assert_data_frame(x) # fixest returned an empty list() if (is.null(character_levels) || length(character_levels) == 0) { return(data.frame()) } # store variables with missing factors or characters vault <- list() for (v in colnames(x)) { if (is.factor(x[[v]])) { if (!all(levels(x[[v]]) %in% x[[v]])) { vault[[v]] <- factor(levels(x[[v]]), levels = levels(x[[v]])) } } else if (is.character(x[[v]])) { if (v %in% names(character_levels)) { vault[[v]] <- character_levels[[v]] } } } # create padding if (length(vault) > 0) { # HACK: Some models use a character variable with many levels (e.g., # mixed-effects groups). This creates a massive padding dataset, and making # predictions can become very expensive. if (isTRUE(sum(sapply(vault, length)) > 100)) return(data.frame()) padding <- utils::head(x, 1) data.table::setDT(padding) for (v in names(vault)) { padding[[v]] <- NULL } fun <- data.table::CJ gr <- do.call("fun", vault) padding <- cjdt(list(padding, gr)) to_keep <- colnames(x) padding[, ..to_keep] setcolorder(padding, to_keep) data.table::setDF(padding) } else { padding <- data.frame() } padding$rowid <- -1 * padding$rowid return(padding) } marginaleffects/R/get_model_matrix_attribute.R0000644000176200001440000000177014631452335021351 0ustar liggesusersget_model_matrix_attribute <- function(model, newdata = NULL) { # supported models (no inheritance) if (!isTRUE(class(model)[1] %in% c("lm", "glm", "rq"))) { return(newdata) } # stats::model.matrix creates all-0 columns with splines::bs() and other functions # this may be too aggressive, but it avoids all functions flag <- any(grepl("\\(", setdiff(names(get_coef(model)), "(Intercept)"))) if (isTRUE(flag)) { return(newdata) } # we don't support offsets, so revert to stats::predict() if (!is.null(model[["offset"]])) { return(newdata) } # subset variables for listwise deletion vars <- unlist(insight::find_predictors(model), use.names = FALSE) vars <- c(vars, unlist(insight::find_response(model), use.names = FALSE)) vars <- intersect(vars, colnames(newdata)) MM <- hush(get_model_matrix(model, newdata = data.frame(newdata)[, vars])) attr(newdata, "marginaleffects_model_matrix") <- MM return(newdata) } marginaleffects/R/ci.R0000644000176200001440000001615214631452335014336 0ustar liggesusersget_ci <- function( x, conf_level, df = NULL, draws = NULL, vcov = TRUE, null_hypothesis = 0, p_adjust = NULL, model = NULL, ...) { checkmate::assert_number(null_hypothesis) if (!is.null(draws)) { out <- get_ci_draws( x, conf_level = conf_level, draws = draws, model = model) return(out) } required <- c("estimate", "std.error") if (!inherits(x, "data.frame") || any(!required %in% colnames(x))) { return(x) } normal <- FALSE if (!"df" %in% colnames(x)) { if (identical(df, Inf)) { normal <- TRUE # 1 or matching length } else if (length(df) %in% c(1, nrow(x))) { x[["df"]] <- df normal <- FALSE # multiple, such as rbind() contrast terms } else if (length(df) < nrow(x) && "rowid" %in% colnames(x)) { rowids <- unique(x$rowid) if (length(rowids) == length(df)) { rowids <- data.table(rowid = rowids, df = df) x <- merge(x, rowids, all.x = TRUE, by = "rowid", sort = FALSE) } else { insight::format_error("The degrees of freedom argument was ignored.") } # mismatch } else { insight::format_error("Satterthwaite and Kenward-Roger corrections are not supported in this command.") } } p_overwrite <- !"p.value" %in% colnames(x) || null_hypothesis != 0 || identical(vcov, "satterthwaite") || identical(vcov, "kenward-roger") z_overwrite <- !"statistic" %in% colnames(x) || null_hypothesis != 0 || p_overwrite ci_overwrite <- !"conf.low" %in% colnames(x) && "std.error" %in% colnames(x) && is.null(p_adjust) if (z_overwrite) { x[["statistic"]] <- (x[["estimate"]] - null_hypothesis) / x[["std.error"]] if (normal) { x[["p.value"]] <- 2 * stats::pnorm(-abs(x$statistic)) } else { x[["p.value"]] <- 2 * stats::pt(-abs(x$statistic), df = x[["df"]]) } } if (ci_overwrite) { alpha <- 1 - conf_level if (normal) { critical <- abs(stats::qnorm(alpha / 2)) } else { critical <- abs(stats::qt(alpha / 2, df = x[["df"]])) } x[["conf.low"]] <- x[["estimate"]] - critical * x[["std.error"]] x[["conf.high"]] <- x[["estimate"]] + critical * x[["std.error"]] } if (!is.null(p_adjust) && "p.value" %in% colnames(x)) { x$p.value <- stats::p.adjust(x$p.value, method = p_adjust) } # s-value if ("p.value" %in% colnames(x)) { x$s.value <- -log2(x$p.value) } return(x) } get_ci_draws <- function(x, conf_level, draws, model = NULL) { checkmate::check_number(conf_level, lower = 1e-10, upper = 1 - 1e-10) critical <- (1 - conf_level) / 2 # faster known case if (inherits(model, "inferences_simulation")) { insight::check_if_installed("collapse", minimum_version = "1.9.0") CIs <- collapse::dapply(draws, MARGIN = 1, FUN = collapse::fquantile, probs = c(critical, 1 - critical)) x$std.error <- collapse::dapply(draws, MARGIN = 1, FUN = collapse::fsd) x$conf.low <- CIs[, 1] x$conf.high <- CIs[, 2] return(x) } else if (identical("eti", getOption("marginaleffects_posterior_interval", default = "eti")) && identical("median", getOption("marginaleffects_posterior_center", default = "median"))) { insight::check_if_installed("collapse", minimum_version = "1.9.0") # Issue #1017 if (nrow(draws) > 0) { CIs <- collapse::dapply(draws, MARGIN = 1, FUN = collapse::fquantile, probs = c(critical, .5, 1 - critical)) x$estimate <- CIs[, 2] x$conf.low <- CIs[, 1] x$conf.high <- CIs[, 3] } return(x) } # faster known case if (identical("eti", getOption("marginaleffects_posterior_interval", default = "eti")) && identical("mean", getOption("marginaleffects_posterior_center", default = "median"))) { insight::check_if_installed("collapse", minimum_version = "1.9.0") Bs <- collapse::dapply(draws, MARGIN = 1, FUN = collapse::fmean) CIs <- collapse::dapply(draws, MARGIN = 1, FUN = collapse::fquantile, probs = c(critical, 1 - critical)) x$estimate <- Bs x$conf.low <- CIs[, 1] x$conf.high <- CIs[, 2] return(x) } # option name change FUN_INTERVAL <- getOption("marginaleffects_posterior_interval") if (is.null(FUN_INTERVAL)) { FUN_INTERVAL <- getOption("marginaleffects_credible_interval", default = "eti") } checkmate::assert_choice(FUN_INTERVAL, choices = c("eti", "hdi")) if (FUN_INTERVAL == "hdi") { FUN_INTERVAL <- get_hdi } else { FUN_INTERVAL <- get_eti } FUN_CENTER <- getOption("marginaleffects_posterior_center", default = stats::median) checkmate::assert( checkmate::check_choice(FUN_CENTER, choices = c("mean", "median")), checkmate::check_function(FUN_CENTER) ) if (identical(FUN_CENTER, "mean")) { FUN_CENTER <- mean } else if (identical(FUN_CENTER, "median")) { FUN_CENTER <- stats::median } CIs <- t(apply(draws, 1, FUN_INTERVAL, credMass = conf_level)) Bs <- apply(draws, 1, FUN_CENTER) # comparison returns a single value if (nrow(x) < nrow(CIs)) { CIs <- unique(CIs) Bs <- unique(Bs) } x[["estimate"]] <- Bs x[["conf.low"]] <- CIs[, "lower"] x[["conf.high"]] <- CIs[, "upper"] return(x) } get_eti <- function(object, credMass = 0.95, ...) { checkmate::assert_numeric(object) checkmate::assert_number(credMass) checkmate::assert_true(credMass > 0) checkmate::assert_true(credMass < 1) critical <- (1 - credMass) / 2 out <- stats::quantile(object, probs = c(critical, 1 - critical)) out <- stats::setNames(out, c("lower", "upper")) return(out) } # this is only used for tests to match emmeans. we use ETI as default for bayesian models. get_hdi <- function(object, credMass = 0.95, ...) { result <- c(NA_real_, NA_real_) if (is.numeric(object)) { attributes(object) <- NULL x <- sort.int(object, method = "quick") # removes NA/NaN, but not Inf n <- length(x) if (n > 0) { # exclude <- ceiling(n * (1 - credMass)) # Not always the same as... exclude <- n - floor(n * credMass) # Number of values to exclude low.poss <- x[1:exclude] # Possible lower limits... upp.poss <- x[(n - exclude + 1):n] # ... and corresponding upper limits best <- which.min(upp.poss - low.poss) # Combination giving the narrowest interval if (length(best)) { result <- c(low.poss[best], upp.poss[best]) } else { tmp <- range(x) if (length(tmp) == 2) { result <- tmp } } } } names(result) <- c("lower", "upper") return(result) } marginaleffects/R/get_group_names.R0000644000176200001440000000062214631452335017114 0ustar liggesusers#' Get levels of the outcome variable in grouped or multivariate models #' #' @inheritParams slopes #' @return A character vector #' @rdname get_group_names #' @keywords internal #' @export get_group_names <- function (model, ...) { UseMethod("get_group_names", model) } #' @rdname get_group_names #' @export get_group_names.default <- function(model, ...) { return("main_marginaleffect") } marginaleffects/R/methods_gamlss.R0000644000176200001440000002400214631452335016745 0ustar liggesusers#' @include get_coef.R #' @rdname get_coef #' @export get_coef.gamlss <- function(model, ...){ dots <- list(...) if (is.null(dots$what)) stop("Argument `what` indicating the parameter of interest is missing.") out <- stats::coef(model, what = dots$what) return(out) } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.gamlss <- function( model, newdata = insight::get_data(model), type = "response", ...) { # Get predictions dots <- list(...) if (is.null(dots$what)) { msg <- sprintf( "Please specifiy a `what` argument with one of these values: %s", paste(model$parameter, collapse = ", ")) stop(msg, call. = FALSE) } # if (!isTRUE(checkmate::check_flag(vcov, null.ok = TRUE))) { # msg <- "The `vcov` argument is not supported for models of this class." # stop(msg, call. = FALSE) # } # predict.gamlss() breaks when `newdata` includes unknown variables origindata <- insight::get_data(model) originvars <- colnames(origindata) data.table::setDF(newdata) index <- which(colnames(newdata) %in% originvars) tmp <- newdata[, index] hush(out <- predict_gamlss(model, newdata = tmp, type = type, data = origindata, ...)) if ("rowid" %in% colnames(newdata)) { out <- data.frame(rowid = newdata$rowid, estimate = out) } else { out <- data.frame(rowid = seq_along(out), estimate = out) } return(out) } #' @include get_vcov.R #' @rdname get_vcov #' @export get_vcov.gamlss <- function(model, ...){ dots <- list(...) if (is.null(dots$what)) { msg <- sprintf( "Please specifiy a `what` argument with one of these values: %s", paste(model$parameter, collapse = ", ")) stop(msg, call. = FALSE) } p <- match(dots$what, model$parameters) vc <- stats::vcov(model, what = dots$what) index <- which(cumsum(rownames(vc) == "(Intercept)") == p) out <- vc[index, index, drop = FALSE] return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.gamlss <- function(model, coefs, ...){ dots <- list(...) if (is.null(dots$what)) stop("Argument `what` indicating the parameter of interest is missing.") p <- paste0(dots$what, ".coefficients") model[[p]][names(coefs)] <- coefs out <- model return(out) } # Modified predict method from the R-package gamlss # Renamed with underscore to avoid conflict with exported method from the package. predict_gamlss <- function(object, what = c("mu", "sigma", "nu", "tau"), parameter = NULL, newdata = NULL, type = c("link", "response", "terms"), safe = TRUE, terms = NULL, se.fit = FALSE, data = NULL, ...) { concat <- function(..., names = NULL) { tmp <- list(...) if (is.null(names)) { names <- names(tmp) } if (is.null(names)) { names <- sapply(as.list(match.call()), deparse)[-1] } if (any(sapply(tmp, is.matrix) | sapply(tmp, is.data.frame))) { len <- sapply(tmp, function(x) c(dim(x), 1)[1]) len[is.null(len)] <- 1 data <- rbind(...) } else { len <- sapply(tmp, length) data <- unlist(tmp) } namelist <- factor(rep(names, len), levels = names) return(data.frame(data, source = namelist)) } if (is.null(newdata)) { predictor <- gamlss::lpred(object, what = what, type = type, terms = terms, se.fit = se.fit, ...) return(predictor) } if (se.fit) { warning(" se.fit = TRUE is not supported for new data values at the moment \n") } if (!(inherits(newdata, "data.frame"))) { stop("newdata must be a data frame ") } what <- if (!is.null(parameter)) { match.arg(parameter, choices = c( "mu", "sigma", "nu", "tau")) } else { match.arg(what) } type <- match.arg(type) Call <- object$call data <- data1 <- if (is.null(data)) { if (!is.null(Call$data)) { eval(Call$data) } else { stop("define the original data using the option data") } } else { data } data <- data[match(names(newdata), names(data))] data <- concat(data, newdata) parform <- stats::formula(object, what) if (length(parform) == 3) { parform[2] <- NULL } Terms <- terms(parform) offsetVar <- if (!is.null(off.num <- attr(Terms, "offset"))) { eval(attr(Terms, "variables")[[off.num + 1]], data) } m <- stats::model.frame(Terms, data, xlev = object[[paste(what, "xlevels", sep = ".")]]) X <- stats::model.matrix(Terms, data, contrasts = object$contrasts) y <- object[[paste(what, "lp", sep = ".")]] w <- object[[paste(what, "wt", sep = ".")]] onlydata <- data$source == "data" smo.mat <- object[[paste(what, "s", sep = ".")]] if (!is.null(off.num)) { y <- (y - offsetVar[onlydata]) } if (!is.null(smo.mat)) { n.smooths <- dim(smo.mat)[2] y <- (y - smo.mat %*% rep(1, n.smooths)) } # Modified from the original prediction function. if (safe) { refit <- stats::lm.wfit(X[onlydata, , drop = FALSE], y, w) if (abs(sum(stats::resid(refit))) > 0.1 || abs(sum(stats::coef(object, what = what) - stats::coef(refit), na.rm = TRUE)) > 1e-05) { warning(paste("There is a discrepancy between the original and the re-fit", " \n used to achieve 'safe' predictions \n ", sep = "")) } coef <- refit$coef } else { coef <- stats::coef(object, what = what) } nX <- dimnames(X) rownames <- nX[[1]][!onlydata] nrows <- sum(!onlydata) nac <- is.na(coef) assign.coef <- attr(X, "assign") collapse <- type != "terms" Xpred <- X[!onlydata, ] Xpred <- matrix(Xpred, nrow = nrows) if (!collapse) { aa <- attr(X, "assign") ll <- attr(Terms, "term.labels") if (attr(Terms, "intercept") > 0) { ll <- c("(Intercept)", ll) } aaa <- factor(aa, labels = ll) asgn <- split(order(aa), aaa) hasintercept <- attr(Terms, "intercept") > 0 p <- refit$qr$rank p1 <- seq(len = p) piv <- refit$qr$pivot[p1] if (hasintercept) { asgn$"(Intercept)" <- NULL avx <- colMeans(X[onlydata, ]) termsconst <- sum(avx[piv] * coef[piv]) } nterms <- length(asgn) pred <- matrix(ncol = nterms, nrow = nrows) dimnames(pred) <- list(rownames(newdata), names(asgn)) if (hasintercept) { Xpred <- sweep(Xpred, 2, avx) } unpiv <- rep.int(0, NCOL(Xpred)) unpiv[piv] <- p1 for (i in seq(1, nterms, length = nterms)) { iipiv <- asgn[[i]] ii <- unpiv[iipiv] iipiv[ii == 0] <- 0 pred[, i] <- if (any(iipiv > 0)) { Xpred[, iipiv, drop = FALSE] %*% coef[iipiv] } else { 0 } } attr(pred, "constant") <- if (hasintercept) { termsconst } else { 0 } if (!is.null(terms)) { pred <- pred[, terms, drop = FALSE] } } else { pred <- drop(Xpred[, !nac, drop = FALSE] %*% coef[!nac]) if (!is.null(off.num) && collapse) { pred <- pred + offsetVar[!onlydata] } } if (!is.null(smo.mat)) { cat("new prediction", "\n") smooth.labels <- dimnames(smo.mat)[[2]] pred.s <- array(0, c(nrows, n.smooths), list( names(pred), dimnames(smo.mat)[[2]])) smooth.calls <- lapply(m[smooth.labels], attr, "call") data <- subset(m, onlydata, drop = FALSE) attr(data, "class") <- NULL new.m <- subset(m, !onlydata, drop = FALSE) attr(new.m, "class") <- NULL residuals <- if (!is.null(off.num)) { object[[paste(what, "wv", sep = ".")]] - object[[paste(what, "lp", sep = ".")]] + offsetVar[onlydata] } else { object[[paste(what, "wv", sep = ".")]] - object[[paste(what, "lp", sep = ".")]] } for (TT in smooth.labels) { if (is.matrix(m[[TT]])) { nm <- names(attributes(m[[TT]])) attributes(data[[TT]]) <- attributes(m[[TT]])[nm[-c( 1, 2)]] } else { attributes(data[[TT]]) <- attributes(m[[TT]]) } Call <- smooth.calls[[TT]] Call$xeval <- substitute(new.m[[TT]], list(TT = TT)) z <- residuals + smo.mat[, TT] pred.s[, TT] <- eval(Call) } if (type == "terms") { pred[, smooth.labels] <- pred[, smooth.labels] + pred.s[, smooth.labels] } else { pred <- drop(pred + pred.s %*% rep(1, n.smooths)) } } if (type == "response") { if (methods::is(eval(parse(text = object$family[[1]])), "gamlss.family")) { pred <- eval(parse(text = object$family[[1]]))[[paste(what, "linkinv", sep = ".")]](pred) } else { pred <- gamlss.dist::gamlss.family(eval(parse(text = paste(stats::family(object)[1], "(", what, ".link=", eval(parse(text = (paste("object$", what, ".link", sep = "")))), ")", sep = ""))))[[paste(what, "linkinv", sep = ".")]](pred) } } pred } marginaleffects/R/get_contrast_data_character.R0000644000176200001440000000566114631452335021447 0ustar liggesusersget_contrast_data_character <- function(model, newdata, variable, cross, first_cross, modeldata, ...) { # factors store all levels, but characters do not, so we need to extract the # original data from the model. tmp <- modeldata # unsupported by insight (e.g., numpyro) if (is.null(tmp)) { tmp <- newdata } levs <- sort(unique(tmp[[variable$name]])) # 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 (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]] levs_idx <- data.table::data.table( lo = as.character(variable$value[1]), hi = as.character(variable$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]] 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) } marginaleffects/R/sanitize_conf_level.R0000644000176200001440000000077114631452335017765 0ustar liggesuserssanitize_conf_level <- function(conf_level, ...) { # periods in arg name are bad style because of s3, but we want to accept both because `broom` uses `conf_level` dots <- list(...) if ("conf.level" %in% names(dots)) { conf_level <- dots[["conf.level"]] } checkmate::assert( checkmate::check_numeric(conf_level, len = 1), checkmate::check_true(conf_level > 0), checkmate::check_true(conf_level < 1), combine = "and") return(conf_level) } marginaleffects/R/get_predict.R0000644000176200001440000000737614631452335016244 0ustar liggesusers#' Get predicted values from a model object (internal function) #' #' @return A data.frame of predicted values with a number of rows equal to the #' number of rows in `newdata` and columns "rowid" and "estimate". A "group" #' column is added for multivariate models or models with categorical outcomes. #' @rdname get_predict #' @inheritParams slopes #' @keywords internal #' @export get_predict <- function(model, newdata, type, ...) { UseMethod("get_predict", model) } #' @rdname get_predict #' @export get_predict.default <- function(model, newdata = insight::get_data(model), type = "response", ...) { dots <- list(...) if (is.null(type)) { type <- sanitize_type(model = model, type = type) } # some predict methods raise warnings on unused arguments unused <- c("normalize_dydx", "eps", "numDeriv_method", "internal_call", "draw", "modeldata", "transform_pre", "transform_post", "flag") dots <- dots[setdiff(names(dots), unused)] # first argument in the predict methods is not always named "x" or "model" dots[["newdata"]] <- newdata dots[["type"]] <- type args <- c(list(model), dots) # `pred` is a secret argument called by `predict.lm` to turn a numeric vector into a data frame with correct `rowid` if ("pred" %in% names(dots)) { pred <- dots[["pred"]] } else { fun <- stats::predict pred <- suppressWarnings(do.call(fun, args)) } # 1-d array to vector (e.g., {mgcv}) if (is.array(pred) && length(dim(pred)) == 1) { pred <- as.vector(pred) } # 1-d array to vector (e.g., Gam from {gam}) if (is.array(pred) && length(dim(pred)) == 3 && dim(pred)[1] == 1 && dim(pred)[2] == 1 && dim(pred)[3] > 1) { pred <- as.vector(pred) } # phylolm if (isTRUE(checkmate::check_matrix(pred, ncols = 1))) { pred <- drop(pred) } # atomic vector if (isTRUE(checkmate::check_atomic_vector(pred))) { # strip weird attributes added by some methods (e.g., predict.svyglm) if (length(pred) == nrow(newdata)) { # as.numeric is slow with large objects and we can't use is.numeric # to run it conditionally because objects of class "svystat" are # already numeric class(pred) <- "numeric" if ("rowid" %in% colnames(newdata)) { out <- list( rowid = newdata$rowid, estimate = pred) } else { out <- list(rowid = seq_len(length(pred)), estimate = pred) } } # matrix with outcome levels as columns } else if (is.matrix(pred)) { if (is.null(colnames(pred))) { colnames(pred) <- seq_len(ncol(pred)) } # internal calls always includes "rowid" as a column in `newdata` if ("rowid" %in% colnames(newdata)) { out <- list( rowid = rep(newdata[["rowid"]], times = ncol(pred)), group = rep(colnames(pred), each = nrow(pred)), estimate = c(pred)) } else { out <- list( rowid = rep(seq_len(nrow(pred)), times = ncol(pred)), group = rep(colnames(pred), each = nrow(pred)), estimate = c(pred)) } out$group <- group_to_factor(out$group, model) } else { stop(sprintf("Unable to extract predictions of type %s from a model of class %s. Please report this problem, along with reproducible code and data on Github: https://github.com/vincentarelbundock/marginaleffects/issues", type, class(model)[1]), call. = FALSE) } data.table::setDF(out) return(out) } marginaleffects/R/bootstrap_simulation.R0000644000176200001440000000205714631452335020223 0ustar liggesusersbootstrap_simulation <- function(model, INF_FUN, vcov, ...) { insight::check_if_installed("MASS") dots <- list(...) B <- get_coef(model) V <- get_vcov(model, vcov = vcov) R <- attr(model, "inferences_R") coefmat <- MASS::mvrnorm(R, mu = B, Sigma = V) # avoid recursion args <- dots args[["vcov"]] <- FALSE attr(model, "inferences_method") <- NULL delta <- do.call(INF_FUN, c(list(model), args)) inner_fun <- function(i = NULL) { args <- dots mod_tmp <- set_coef(model, coefmat[i, ]) attr(mod_tmp, "inferences_method") <- NULL args[["model"]] <- mod_tmp args[["vcov"]] <- FALSE do.call(INF_FUN, args)$estimate } draws <- lapply(seq_len(nrow(coefmat)), inner_fun) draws <- do.call("cbind", draws) out <- get_ci(delta, draws = draws, conf_level = args$conf_level) # do not use simulation mean as point estimate # https://doi.org/10.1017/psrm.2023.8 out$estimate <- delta$estimate attr(out, "posterior_draws") <- draws return(out) } marginaleffects/R/get_contrasts.R0000644000176200001440000004413214631452335016621 0ustar liggesusersget_contrasts <- function(model, newdata, type, variables, original, lo, hi, wts = FALSE, marginalmeans, by = NULL, hypothesis = NULL, cross = FALSE, verbose = TRUE, deltamethod = FALSE, ...) { settings_init() # some predict() methods need data frames and will convert data.tables # internally, which can be very expensive if done many times. we do it once # here. data.table::setDF(lo) data.table::setDF(hi) data.table::setDF(original) # brms models need to be combined to use a single seed when sample_new_levels="gaussian" if (inherits(model, c("brmsfit", "bart"))) { if (!"rowid" %in% colnames(lo)) { lo$rowid <- hi$rowid <- seq_len(nrow(lo)) } both <- rbindlist(list(lo, hi)) pred_both <- myTryCatch(get_predict( model, type = type, newdata = both, ...)) # informative error in case of allow.new.levels level breakage if (inherits(pred_both[["error"]], "simpleError")) { insight::format_error(pred_both[["error"]][["message"]]) } else { pred_both <- pred_both[["value"]] } data.table::setDT(pred_both) pred_both[, "lo" := seq_len(.N) <= .N / 2, by = "group"] pred_lo <- pred_both[pred_both$lo, .(rowid, group, estimate), drop = FALSE] pred_hi <- pred_both[!pred_both$lo, .(rowid, group, estimate), drop = FALSE] data.table::setDF(pred_lo) data.table::setDF(pred_hi) draws <- attr(pred_both, "posterior_draws") draws_lo <- draws[pred_both$lo, , drop = FALSE] draws_hi <- draws[!pred_both$lo, , drop = FALSE] attr(pred_lo, "posterior_draws") <- draws_lo attr(pred_hi, "posterior_draws") <- draws_hi } else { pred_lo <- myTryCatch(get_predict( model, type = type, newdata = lo, ...)) # tidymodels if (inherits(pred_lo$error, "rlang_error") && isTRUE(grepl("the object should be", pred_lo$error$message))) { insight::format_error(pred_lo$error$message) } else { pred_lo <- pred_lo[["value"]] } pred_hi <- myTryCatch(get_predict( model, type = type, newdata = hi, ...)) # otherwise we keep the full error object instead of extracting the value if (inherits(pred_hi$value, "data.frame")) { pred_hi <- pred_hi$value } else { pred_hi <- pred_hi$error } } # predict() takes up 2/3 of the wall time. This call is only useful when we # compute elasticities, or for the main estimate, not for standard errors, # so we probably save 1/3 of that 2/3. elasticities <- c( # "dydx", # useless and expensive "eyex", "eydx", "dyex", # "dydxavg", # useless and expensive "eyexavg", "eydxavg", "dyexavg") fun <- function(x) { out <- checkmate::check_choice(x$comparison, choices = elasticities) isTRUE(out) } tmp <- Filter(fun, variables) if (!isTRUE(deltamethod) || length(tmp) > 0) { pred_or <- myTryCatch(get_predict( model, type = type, newdata = original, ...))[["value"]] } else { pred_or <- NULL } # lots of indexing later requires a data.table data.table::setDT(original) if (!inherits(pred_hi, "data.frame") || !inherits(pred_lo, "data.frame") || !inherits(pred_or, c("data.frame", "NULL")) || all(is.na(pred_lo$estimate))) { msg <- "Unable to compute predicted values with this model. This error can arise when `insight::get_data()` is unable to extract the dataset from the model object, or when the data frame was modified since fitting the model. You can try to supply a different dataset to the `newdata` argument." if (inherits(pred_hi, c("try-error", "error"))) { msg <-c(msg, "", "In addition, this error message was raised:", "", as.character(pred_hi)) } msg <- c(msg, "", "Bug Tracker: https://github.com/vincentarelbundock/marginaleffects/issues") insight::format_error(msg) } # output data.frame out <- pred_lo data.table::setDT(out) # univariate outcome: # original is the "composite" data that we constructed by binding terms and # compute predictions. It includes a term column, which we need to # replicate for each group. out[, "marginaleffects_wts_internal" := NA_real_] # default (probably almost always overwritten) mult <- nrow(out) / nrow(original) regex <- "^term$|rowid_dedup|^group$|^contrast|^marginaleffects_wts_internal$" if (isTRUE(mult == 1)) { for (v in grep(regex, colnames(original), value = TRUE)) { out[, (v) := original[[v]]] } # group or multivariate outcomes } else if (isTRUE(mult > 1)) { for (v in grep(regex, colnames(original), value = TRUE)) { out[, (v) := rep(original[[v]], times = mult)] } # cross-contrasts or weird cases } else { out <- merge(out, newdata, by = "rowid", all.x = TRUE, sort = FALSE) if (isTRUE(nrow(out) == nrow(lo))) { tmp <- data.table(lo)[, .SD, .SDcols = patterns("^contrast|marginaleffects_wts_internal")] out <- cbind(out, tmp) idx <- c("rowid", grep("^contrast", colnames(out), value = TRUE), colnames(out)) idx <- unique(idx) out <- out[, ..idx] } } if (!"term" %in% colnames(out)) { out[, "term" := "cross"] } # by if (isTRUE(checkmate::check_data_frame(by))) { bycols <- "by" data.table::setDT(by) tmp <- setdiff(intersect(colnames(out), colnames(by)), "by") if (length(tmp) == 0) { if (all(colnames(by) %in% c("by", colnames(newdata)))) { nd <- c("rowid", setdiff(colnames(by), "by")) nd <- newdata[, nd, drop = FALSE] out <- merge(out, nd, by = "rowid", sort = FALSE) tmp <- setdiff(intersect(colnames(out), colnames(by)), "by") } else { insight::format_error("The column in `by` must be present in `newdata`.") } } # harmonize column types for (v in colnames(by)) { if (isTRUE(is.character(out[[v]])) && isTRUE(is.numeric(by[[v]]))) { by[[v]] <- as.character(by[[v]]) } else if (isTRUE(is.numeric(out[[v]])) && isTRUE(is.character(by[[v]]))) { by[[v]] <- as.numeric(by[[v]]) } } out[by, by := by, on = tmp] by <- "by" } else if (isTRUE(by)) { regex <- "^term$|^contrast_?|^group$" by <- grep(regex, colnames(out), value = TRUE) by <- unique(by) } else if (isTRUE(checkmate::check_character(by))) { regex <- "^term$|^contrast_?|^group$" by <- c(by, grep(regex, colnames(out), value = TRUE)) by <- unique(by) } # comparison function could be different for different terms # sanitize_variables() ensures all functions are identical when there are cross fun_list <- sapply(names(variables), function(x) variables[[x]][["function"]]) fun_list[["cross"]] <- fun_list[[1]] # elasticity requires the original (properly aligned) predictor values # this will discard factor variables which are duplicated, so in principle # it should be the "correct" size # also need `x` when `x` is in the signature of the `comparison` custom function FUN <- function(z) { (is.character(z$comparison) && z$comparison %in% elasticities) || (is.function(z$comparison) && "x" %in% names(formals(z$comparison))) } elasticities <- Filter(FUN, variables) elasticities <- lapply(elasticities, function(x) x$name) if (length(elasticities) > 0) { # assigning a subset of "original" to "idx1" takes time and memory # better to do this here for most columns and add the "v" column only # in the loop if (!is.null(original)) { idx1 <- c("rowid", "rowidcf", "term", "group", grep("^contrast", colnames(original), value = TRUE)) idx1 <- intersect(idx1, colnames(original)) idx1 <- original[, ..idx1] } for (v in names(elasticities)) { idx2 <- unique(c("rowid", "term", "group", by, grep("^contrast", colnames(out), value = TRUE))) idx2 <- intersect(idx2, colnames(out)) # discard other terms to get right length vector idx2 <- out[term == v, ..idx2] # original is NULL when cross=TRUE if (!is.null(original)) { # if not first iteration, need to remove previous "v" and "elast" if (v %in% colnames(idx1)) { idx1[, (v) := NULL] } if ("elast" %in% colnames(idx1)) { idx1[, elast := NULL] } idx1[, (v) := original[[v]]] setnames(idx1, old = v, new = "elast") on_cols <- intersect(colnames(idx1), colnames(idx2)) idx2 <- unique(merge(idx2, idx1, by = on_cols, sort = FALSE)[, elast := elast]) } elasticities[[v]] <- idx2$elast } } draws <- attr(pred_lo, "posterior_draws") # frequentist if (is.null(draws)) { draws_lo <- draws_hi <- draws_or <- NULL # bayes } else { draws_lo <- attr(pred_lo, "posterior_draws") draws_hi <- attr(pred_hi, "posterior_draws") draws_or <- attr(pred_or, "posterior_draws") } data.table::setDT(pred_hi) out[, predicted_lo := pred_lo[["estimate"]]] out[, predicted_hi := pred_hi[["estimate"]]] if (!is.null(pred_or)) { data.table::setDT(pred_or) out[, predicted := pred_or[["estimate"]]] } else { out[, predicted := NA_real_] } idx <- grep("^contrast|^group$|^term$|^type$|^comparison_idx$", colnames(out), value = TRUE) # when `by` is a character vector, we sometimes modify the comparison # function on the fly to use the `avg` version. this is important and # convenient because some of the statistics are non-collapsible, so we can't # average them at the very end. when `by` is a data frame, we do this only # at the very end. # TODO: What is the UI for this? Doesn't make sense to have different functions. if (isTRUE(checkmate::check_character(by))) { tmp <- intersect(colnames(newdata), c(by, colnames(out))) if (length(tmp) > 1) { tmp <- subset(newdata, select = tmp) out <- merge(out, tmp, all.x = TRUE, sort = FALSE) idx <- unique(c(idx, by)) } } # we feed these columns to safefun(), even if they are useless for categoricals if (!"marginaleffects_wts_internal" %in% colnames(out)) out[, "marginaleffects_wts_internal" := NA] if (isTRUE(marginalmeans)) { out <- out[, .( predicted_lo = mean(predicted_lo), predicted_hi = mean(predicted_hi), predicted = mean(predicted), marginaleffects_wts_internal = mean(marginaleffects_wts_internal)), keyby = idx] } # safe version of comparison # unknown arguments # singleton vs vector # different terms use different functions safefun <- function(hi, lo, y, n, term, cross, wts, tmp_idx, newdata) { tn <- term[1] eps <- variables[[tn]]$eps # when cross=TRUE, sanitize_comparison enforces a single function if (isTRUE(cross)) { fun <- fun_list[[1]] } else { fun <- fun_list[[tn]] } args <- list( "hi" = hi, "lo" = lo, "y" = y, "eps" = eps, "w" = wts, "newdata" = newdata) # sometimes x is exactly the same length, but not always args[["x"]] <- elasticities[[tn]][tmp_idx] args <- args[names(args) %in% names(formals(fun))] con <- try(do.call("fun", args), silent = TRUE) if (!isTRUE(checkmate::check_numeric(con, len = n)) && !isTRUE(checkmate::check_numeric(con, len = 1))) { msg <- sprintf("The function supplied to the `comparison` argument must accept two numeric vectors of predicted probabilities of length %s, and return a single numeric value or a numeric vector of length %s, with no missing value.", n, n) #nolint insight::format_error(msg) } if (length(con) == 1) { con <- c(con, rep(NA_real_, length(hi) - 1)) settings_set("marginaleffects_safefun_return1", TRUE) } return(con) } # need a temp index for group-by operations when elasticities is a vector of length equal to full rows of `out` tmp <- grep("^term$|^contrast|^group$", colnames(out), value = TRUE) if (length(tmp) > 0) { out[, tmp_idx := 1:.N, by = tmp] } else { out[, tmp_idx := 1:.N] } # bayesian if (!is.null(draws)) { # drop missing otherwise get_averages() fails when trying to take a # simple mean idx_na <- !is.na(out$predicted_lo) out <- stats::na.omit(out, cols = "predicted_lo") # TODO: performance is probably terrrrrible here, but splitting is # tricky because grouping rows are not always contiguous, and the order # of rows is **extremely** important because draws don't have the # indices that would allow us to align them back with `out` draws <- draws[idx_na, , drop = FALSE] if (isTRUE(checkmate::check_character(by, min.len = 1))) { by_idx <- subset(out, select = intersect(by, colnames(out))) by_idx <- do.call(paste, c(by_idx, sep = "|")) } else { by_idx <- out$term } # loop over columns (draws) and term names because different terms could use different functions for (tn in unique(by_idx)) { for (i in seq_len(ncol(draws))) { idx <- by_idx == tn draws[idx, i] <- safefun( hi = draws_hi[idx, i], lo = draws_lo[idx, i], y = draws_or[idx, i], n = sum(idx), term = out$term[idx], cross = cross, wts = out$marginaleffects_wts_internal[idx], tmp_idx = out$tmp_idx[idx], newdata = newdata) } } # function returns unique value idx <- !is.na(draws[, 1]) draws <- draws[idx, , drop = FALSE] # if comparison returns a single value, then we padded with NA. That # also means we don't want `rowid` otherwise we will merge and have # useless duplicates. if (any(!idx)) { if (settings_equal("marginaleffects_safefun_return1", TRUE)) { out[, "rowid" := NULL] } out <- out[idx, , drop = FALSE] } FUN_CENTER <- getOption("marginaleffects_posterior_center", default = stats::median) out[, "estimate" := apply(draws, 1, FUN_CENTER)] # frequentist } else { out <- stats::na.omit(out, cols = "predicted_lo") # We want to write the "estimate" column in-place because it safer # than group-merge; there were several bugs related to this in the past. # safefun() returns 1 value and NAs when the function retunrs a # singleton. idx <- intersect(idx, colnames(out)) out[, "estimate" := safefun( hi = predicted_hi, lo = predicted_lo, y = predicted, n = .N, term = term, cross = cross, wts = marginaleffects_wts_internal, tmp_idx = tmp_idx, newdata = newdata), keyby = idx] out[, tmp_idx := NULL] # if comparison returns a single value, then we padded with NA. That # also means we don't want `rowid` otherwise we will merge and have # useless duplicates. if (anyNA(out$estimate)) { if (settings_equal("marginaleffects_safefun_return1", TRUE)) { out[, "rowid" := NULL] } } out <- stats::na.omit(out, cols = "estimate") } # clean if ("rowid_dedup" %in% colnames(out)) { out[, "rowid_dedup" := NULL] } # averaging by groups # sometimes this work is already done # if `by` is a column name, then we have merged-in a data frame earlier auto_mean_fun_sub <- any(grepl("^mean\\(", unique(out$contrast))) if (nrow(out) > 1) { if (!auto_mean_fun_sub && !(is.null(by) || isFALSE(by)) && any(grepl("^contrast[_]?", colnames(out)))) { out <- get_by( out, draws = draws, newdata = newdata, by = by, verbose = verbose) draws <- attr(out, "posterior_draws") } else { bycols <- c(by, "group", "term", "^contrast[_]?") bycols <- paste(bycols, collapse = "|") bycols <- grep(bycols, colnames(out), value = TRUE) } } # issue #531: uncertainty estimates from get_predict() sometimes get retained, but they are not overwritten later by get_ci() # drop by reference for speed bad <- intersect( colnames(out), c("conf.low", "conf.high", "std.error", "statistic", "p.value")) if (length(bad) > 0) { out[, (bad) := NULL] } # before get_hypothesis attr(out, "posterior_draws") <- draws # hypothesis tests using the delta method out <- get_hypothesis(out, hypothesis, by = by, newdata = original, draws = draws) # reset settings settings_rm("marginaleffects_safefun_return1") # output attr(out, "original") <- original return(out) } marginaleffects/R/get_jacobian.R0000644000176200001440000000760614631452335016354 0ustar liggesusersget_jacobian <- function(func, x, numderiv) { numDeriv_options <- getOption("marginaleffects_numDeriv", default = NULL) if (is.null(numDeriv_options)) { method <- numderiv[[1]] numderiv[[1]] <- NULL numderiv[["func"]] <- func numderiv[["x"]] <- x if (identical(method, "richardson")) { df <- do.call(get_jacobian_richardson, numderiv) } else if (identical(method, "fdforward")) { df <- do.call(get_jacobian_fdforward, numderiv) } else if (identical(method, "fdcenter")) { df <- do.call(get_jacobian_fdcenter, numderiv) } } else { insight::check_if_installed("numDeriv") numDeriv_options[["func"]] <- func numDeriv_options[["x"]] <- x ndFUN <- get("jacobian", asNamespace("numDeriv")) df <- do.call(ndFUN, numDeriv_options) } return(df) } get_jacobian_fdforward <- function(func, x, eps = NULL) { # old version. probably not optimal. Keep for posterity. # h <- max(1e-8, 1e-4 * min(abs(x), na.rm = TRUE)) baseline <- func(x) inner_loop <- function(i, ...) { if (is.null(eps)) { h <- max(abs(x[i]) * sqrt(.Machine$double.eps), 1e-10) } else { h <- eps } dx <- x dx[i] <- dx[i] + h out <- (func(dx) - baseline) / h return(out) } if (isTRUE(getOption("marginaleffects_parallel", default = FALSE))) { insight::check_if_installed("future.apply") df <- future.apply::future_lapply(seq_along(x), inner_loop, future.seed = TRUE) df <- do.call("cbind", df) } else { df <- matrix(NA_real_, length(baseline), length(x)) for (i in seq_along(x)) { df[, i] <- inner_loop(i) } } return(df) } get_jacobian_fdcenter <- function(func, x, eps = NULL) { baseline <- func(x) inner_loop <- function(i, ...) { if (is.null(eps)) { h <- max(abs(x[i]) * sqrt(.Machine$double.eps), 1e-10) } else { h <- eps } dx_hi <- dx_lo <- x dx_hi[i] <- dx_hi[i] + h / 2 dx_lo[i] <- dx_lo[i] - h / 2 out <- (func(dx_hi) - func(dx_lo)) / h } if (isTRUE(getOption("marginaleffects_parallel", default = FALSE))) { insight::check_if_installed("future.apply") df <- future.apply::future_lapply(seq_along(x), inner_loop, future.seed = TRUE) df <- do.call("cbind", df) } else { df <- matrix(NA_real_, length(baseline), length(x)) for (i in seq_along(x)) { df[, i] <- inner_loop(i) } } return(df) } # Code adapted from the `numDeriv` package by Paul Gilbert and Ravi Varadhan # GPL-3: https://cran.r-project.org/package=numDeriv get_jacobian_richardson <- function( func, x, eps = 1e-4, d = 1e-4, zero_tol = sqrt(.Machine$double.eps / 7e-7), side = NULL, r = 4, v = 2, ...) { n <- length(x) f <- func(x, ...) a <- array(NA, c(length(f), r, n)) h <- abs(d * x) + eps * (abs(x) < zero_tol) pna <- (side == 1) & !is.na(side) # double these on plus side mna <- (side == -1) & !is.na(side) # double these on minus side for (k in 1:r) { # successively reduce h ph <- mh <- h ph[pna] <- 2 * ph[pna] ph[mna] <- 0 mh[mna] <- 2 * mh[mna] mh[pna] <- 0 for (i in 1:n) { a[, k, i] <- (func(x + ph * (i == seq(n)), ...) - func(x - mh * (i == seq(n)), ...)) / (2 * h[i]) # if((k != 1)) a[,(abs(a[,(k-1),i]) < 1e-20)] <- 0 #some func are unstable near zero } h <- h / v # Reduced h by 1/v. } for (m in 1:(r - 1)) { a <- (a[, 2:(r + 1 - m), , drop = FALSE] * (4^m) - a[, 1:(r - m), , drop = FALSE]) / (4^m - 1) } # drop second dim of a, which is now 1 (but not other dim's even if they are 1 return(array(a, dim(a)[c(1, 3)])) } marginaleffects/R/sanitize_variables.R0000644000176200001440000004243314631452335017622 0ustar liggesusers# input: character vector or named list # output: named list of lists where each element represents a variable with: name, value, function, label sanitize_variables <- function(variables, model, newdata, # need for NumPyro where `find_variables()`` does not work modeldata, comparison = NULL, by = NULL, cross = FALSE, calling_function = "comparisons", eps = NULL) { checkmate::assert( checkmate::check_null(variables), checkmate::check_character(variables, min.len = 1, names = "unnamed"), checkmate::check_list(variables, min.len = 1, names = "unique"), combine = "or") # extensions with no `get_data()` if (is.null(modeldata) || nrow(modeldata) == 0) { modeldata <- set_variable_class(newdata, model) no_modeldata <- TRUE } else { no_modeldata <- FALSE } # variables is NULL: get all variable names from the model if (is.null(variables)) { # mhurdle names the variables weirdly if (inherits(model, "mhurdle")) { predictors <- insight::find_predictors(model, flatten = TRUE) predictors <- list(conditional = predictors) } else { predictors <- insight::find_variables(model) } # unsupported models like pytorch if (length(predictors) == 0 || (length(predictors) == 1 && names(predictors) == "response")) { dv <- hush(unlist(insight::find_response(model, combine = FALSE), use.names = FALSE)) predictors <- setdiff(hush(colnames(newdata)), c(dv, "rowid")) } else { known <- c("fixed", "conditional", "zero_inflated", "scale", "nonlinear") if (any(known %in% names(predictors))) { predictors <- predictors[known] # sometimes triggered by multivariate brms models where we get nested # list: predictors$gear$hp } else { predictors <- unlist(predictors, recursive = TRUE, use.names = FALSE) predictors <- unique(predictors) } # flatten predictors <- unique(unlist(predictors, recursive = TRUE, use.names = FALSE)) } } else { predictors <- variables } # character -> list if (isTRUE(checkmate::check_character(predictors))) { predictors <- stats::setNames(rep(list(NULL), length(predictors)), predictors) } # reserved keywords # Issue #697: we used to allow "group", as long as it wasn't in # `variables`, but this created problems with automatic `by=TRUE`. Perhaps # I could loosen this, but there are many interactions, and the lazy way is # just to forbid entirely. reserved <- c( "rowid", "group", "term", "contrast", "estimate", "std.error", "statistic", "conf.low", "conf.high", "p.value", "p.value.nonsup", "p.value.noninf", "by") # if no modeldata is available, we use `newdata`, but that often has a # `rowid` column. This used to break the extensions.Rmd vignette. if (no_modeldata) { reserved <- setdiff(reserved, "rowid") } bad <- unique(intersect(c(names(predictors), colnames(modeldata)), reserved)) if (length(bad) > 0) { msg <- c( "These variable names are forbidden to avoid conflicts with the outputs of `marginaleffects`:", sprintf("%s", paste(sprintf('"%s"', bad), collapse = ", ")), "Please rename your variables before fitting the model.") insight::format_error(msg) } # when comparisons() only inludes one focal predictor, we don't need to specify it in `newdata` # when `variables` is numeric, we still need to include it, because in # non-linear model the contrast depend on the starting value of the focal # variable. found <- colnames(newdata) if (calling_function == "comparisons") { v <- NULL if (isTRUE(checkmate::check_string(variables))) { v <- variables } else if (isTRUE(checkmate::check_list(variables, len = 1, names = "named"))) { v <- names(variables)[1] } flag <- get_variable_class(modeldata, variable = v, compare = "categorical") if (!is.null(v) && isTRUE(flag)) { found <- c(found, v) } } # matrix predictors mc <- attr(newdata, "newdata_matrix_columns") if (length(mc) > 0 && any(names(predictors) %in% mc)) { predictors <- predictors[!names(predictors) %in% mc] insight::format_warning("Matrix columns are not supported. Use the `variables` argument to specify valid predictors, or use a function like `drop()` to convert your matrix columns into vectors.") } # missing variables miss <- setdiff(names(predictors), found) predictors <- predictors[!names(predictors) %in% miss] if (length(miss) > 0) { msg <- sprintf( "These variables were not found: %s. Try specifying the `newdata` argument explicitly and make sure the missing variable is included.", paste(miss, collapse = ", ")) insight::format_warning(msg) } # sometimes `insight` returns interaction component as if it were a constituent variable idx <- !grepl(":", names(predictors)) predictors <- predictors[idx] # anything left? if (length(predictors) == 0) { msg <- "There is no valid predictor variable. Please change the `variables` argument or supply a new data frame to the `newdata` argument." insight::format_error(msg) } # functions to values # only for predictions; get_contrast_data_numeric handles this for comparisons() # do this before NULL-to-defaults so we can fill it in with default in case of failure if (calling_function == "predictions") { for (v in names(predictors)) { if (is.function(predictors[[v]])) { tmp <- hush(predictors[[v]](modeldata[[v]])) if (is.null(tmp)) { msg <- sprintf("The `%s` function produced invalid output when applied to the dataset used to fit the model.", v) insight::format_warning(msg) } predictors[[v]] <- hush(predictors[[v]](modeldata[[v]])) } } } # NULL to defaults for (v in names(predictors)) { if (is.null(predictors[[v]])) { if (get_variable_class(modeldata, v, "binary")) { predictors[[v]] <- 0:1 } else if (get_variable_class(modeldata, v, "numeric")) { if (calling_function == "comparisons") { predictors[[v]] <- 1 } else if (calling_function == "predictions") { v_unique <- unique(modeldata[[v]]) if (length(v_unique) < 6) { predictors[[v]] <- v_unique } else { predictors[[v]] <- stats::fivenum(modeldata[[v]]) } } } else { if (calling_function == "comparisons") { predictors[[v]] <- "reference" } else if (calling_function == "predictions") { # TODO: warning when this is too large. Here or elsewhere? predictors[[v]] <- unique(modeldata[[v]]) } } } } # shortcuts and validity for (v in names(predictors)) { if (isTRUE(checkmate::check_data_frame(predictors[[v]], nrows = nrow(newdata)))) { # do nothing, but don't take the other validity check branches } else if (get_variable_class(modeldata, v, "binary")) { if (!isTRUE(checkmate::check_numeric(predictors[[v]])) || !is_binary(predictors[[v]])) { msg <- sprintf("The `%s` variable is binary. The corresponding entry in the `variables` argument must be 0 or 1.", v) insight::format_error(msg) } # get_contrast_data requires both levels if (calling_function == "comparisons") { if (length(predictors[[v]]) != 2) { msg <- sprintf("The `%s` variable is binary. The corresponding entry in the `variables` argument must be a vector of length 2.", v) insight::format_error(msg) } } } else if (get_variable_class(modeldata, v, "numeric")) { if (calling_function == "comparisons") { # For comparisons(), the string shortcuts are processed in contrast_data_* functions because we need fancy labels. # Eventually it would be nice to consolidate, but that's a lot of work. valid_str <- c("iqr", "minmax", "sd", "2sd") flag <- isTRUE(checkmate::check_numeric(predictors[[v]], min.len = 1, max.len = 2)) || isTRUE(checkmate::check_choice(predictors[[v]], choices = valid_str)) || isTRUE(checkmate::check_function(predictors[[v]])) if (!isTRUE(flag)) { msg <- "The %s element of the `variables` argument is invalid." msg <- sprintf(msg, v) insight::format_error(msg) } } else if (calling_function == "predictions") { # string shortcuts if (identical(predictors[[v]], "iqr")) { predictors[[v]] <- stats::quantile(modeldata[[v]], probs = c(.25, .75), na.rm = TRUE) } else if (identical(predictors[[v]], "minmax")) { predictors[[v]] <- c(min(modeldata[[v]], na.rm = TRUE), max(modeldata[[v]], na.rm = TRUE)) } else if (identical(predictors[[v]], "sd")) { s <- stats::sd(modeldata[[v]], na.rm = TRUE) m <- mean(modeldata[[v]], na.rm = TRUE) predictors[[v]] <- c(m - s / 2, m + s / 2) } else if (identical(predictors[[v]], "2sd")) { s <- stats::sd(modeldata[[v]], na.rm = TRUE) m <- mean(modeldata[[v]], na.rm = TRUE) predictors[[v]] <- c(m - s, m + s) } else if (identical(predictors[[v]], "threenum")) { s <- stats::sd(modeldata[[v]], na.rm = TRUE) m <- mean(modeldata[[v]], na.rm = TRUE) predictors[[v]] <- c(m - s, m, m + s) } else if (identical(predictors[[v]], "fivenum")) { predictors[[v]] <- stats::fivenum } else if (is.character(predictors[[v]])) { msg <- sprintf('%s is a numeric variable. The summary shortcuts supported by the variables argument are: "iqr", "minmax", "sd", "2sd", "threenum", "fivenum".', v) insight::format_error(msg) } } } else { if (calling_function == "comparisons") { valid <- c("reference", "sequential", "pairwise", "all", "revpairwise", "revsequential", "revreference") # minmax needs an actual factor in the original data to guarantee correct order of levels. if (is.factor(modeldata[[v]])) { valid <- c(valid, "minmax") } flag1 <- checkmate::check_choice(predictors[[v]], choices = valid) flag2 <- checkmate::check_vector(predictors[[v]], len = 2) flag3 <- checkmate::check_data_frame(predictors[[v]], nrows = nrow(newdata), ncols = 2) flag4 <- checkmate::check_function(predictors[[v]]) flag5 <- checkmate::check_data_frame(predictors[[v]]) if (!isTRUE(flag1) && !isTRUE(flag2) && !isTRUE(flag3) && !isTRUE(flag4) && !isTRUE(flag5)) { msg <- "The %s element of the `variables` argument must be a vector of length 2 or one of: %s" msg <- sprintf(msg, v, paste(valid, collapse = ", ")) insight::format_error(msg) } } else if (calling_function == "predictions") { if (is.character(predictors[[v]]) || is.factor(predictors[[v]])) { if (!all(as.character(predictors[[v]]) %in% as.character(modeldata[[v]]))) { invalid <- intersect( as.character(predictors[[v]]), c("pairwise", "reference", "sequential", "revpairwise", "revreference", "revsequential")) if (length(invalid) > 0) { msg <- "These values are only supported by the `variables` argument in the `comparisons()` function: %s" msg <- sprintf(msg, paste(invalid, collapse = ", ")) } else { msg <- "Some elements of the `variables` argument are not in their original data. Check this variable: %s" msg <- sprintf(msg, v) } insight::format_error(msg) } } } } } # sometimes weights don't get extracted by `find_variables()` w <- tryCatch(insight::find_weights(model), error = function(e) NULL) w <- intersect(w, colnames(newdata)) others <- w # goals: # allow multiple function types: slopes() uses both difference and dydx # when comparison is defined, use that if it works or turn back to defaults # predictors list elements: name, value, function, label if (is.null(comparison)) { fun_numeric <- fun_categorical <- comparison_function_dict[["difference"]] lab_numeric <- lab_categorical <- comparison_label_dict[["difference"]] } else if (is.function(comparison)) { fun_numeric <- fun_categorical <- comparison lab_numeric <- lab_categorical <- "custom" } else if (is.character(comparison)) { # switch to the avg version when there is a `by` function if ((isTRUE(checkmate::check_character(by)) || isTRUE(by)) && !isTRUE(grepl("avg$", comparison))) { comparison <- paste0(comparison, "avg") } # weights if user requests `avg` or automatically switched if (isTRUE(grepl("avg$", comparison)) && "marginaleffects_wts_internal" %in% colnames(newdata)) { comparison <- paste0(comparison, "wts") } fun_numeric <- fun_categorical <- comparison_function_dict[[comparison]] lab_numeric <- lab_categorical <- comparison_label_dict[[comparison]] if (isTRUE(grepl("dydxavgwts|eyexavgwts|dyexavgwts|eydxavgwts", comparison))) { fun_categorical <- comparison_function_dict[["differenceavgwts"]] lab_categorical <- comparison_label_dict[["differenceavgwts"]] } else if (isTRUE(grepl("dydxavg|eyexavg|dyexavg|eydxavg", comparison))) { fun_categorical <- comparison_function_dict[["differenceavg"]] lab_categorical <- comparison_label_dict[["differenceavg"]] } else if (isTRUE(grepl("dydx$|eyex$|dyex$|eydx$", comparison))) { fun_categorical <- comparison_function_dict[["difference"]] lab_categorical <- comparison_label_dict[["difference"]] } } for (v in names(predictors)) { if (get_variable_class(modeldata, v, "numeric") && !get_variable_class(modeldata, v, "binary")) { fun <- fun_numeric lab <- lab_numeric } else { fun <- fun_categorical lab <- lab_categorical } predictors[[v]] <- list( "name" = v, "function" = fun, "label" = lab, "value" = predictors[[v]], "comparison" = comparison) } # epsilon for finite difference for (v in names(predictors)) { if (!is.null(eps)) { predictors[[v]][["eps"]] <- eps } else if (is.numeric(modeldata[[v]])) { predictors[[v]][["eps"]] <- 1e-4 * diff(range(modeldata[[v]], na.rm = TRUE, finite = TRUE)) } else { predictors[[v]]["eps"] <- list(NULL) } } # can't take the slope of an outcome, except in weird brms models (issue #1006) if (!inherits(model, "brmsfit") || !isTRUE(length(model$formula$forms) > 1)) { dv <- hush(unlist(insight::find_response(model, combine = FALSE), use.names = FALSE)) # sometimes insight doesn't work if (length(dv) > 0) { predictors <- predictors[setdiff(names(predictors), dv)] } } if (length(predictors) == 0) { insight::format_error("There is no valid predictor variable. Please make sure your model includes predictors and use the `variables` argument.") } # interaction: get_contrasts() assumes there is only one function when interaction=TRUE if (isTRUE(interaction)) { for (p in predictors) { flag <- !identical(p[["function"]], predictors[[1]][["function"]]) if (flag) { stop("When `interaction=TRUE` all variables must use the same contrast function.", call. = FALSE) } } } # sort variables alphabetically predictors <- predictors[sort(names(predictors))] others <- others[sort(names(others))] # output out <- list(conditional = predictors, others = others) return(out) } marginaleffects/R/get_coef.R0000644000176200001440000000113314631452335015507 0ustar liggesusers#' Get a named vector of coefficients from a model object (internal function) #' #' @inheritParams slopes #' @return A named vector of coefficients. The names must match those of the variance matrix. #' @rdname get_coef #' @keywords internal #' @export get_coef <- function (model, ...) { UseMethod("get_coef", model) } #' @rdname get_coef #' @export get_coef.default <- function(model, ...) { ## faster # out <- stats::coef(model) # more general out <- insight::get_parameters(model, component = "all") out <- stats::setNames(out$Estimate, out$Parameter) return(out) } marginaleffects/R/settings.R0000644000176200001440000000305314631452335015577 0ustar liggesusersmarginaleffects_settings <- new.env() settings_cache <- function(setti) { out <- list() for (s in setti) { out[[s]] <- settings_get(s) } return(out) } settings_restore <- function(cache) { for (n in names(cache)) { settings_set(n, cache[[n]]) } } settings_init <- function(settings = NULL) { settings_rm() default_settings <- list( marginaleffects_safefun_return1 = FALSE ) checkmate::assert_list(settings, null.ok = TRUE, names = "unique") if (!is.null(settings)) { settings <- c(settings, default_settings) } for (i in seq_along(settings)) { settings_set(names(settings)[i], settings[[i]]) } } settings_get <- function(name) { if (name %in% names(marginaleffects_settings)) { get(name, envir = marginaleffects_settings) } else { NULL } } settings_set <- function(name, value) { assign(name, value = value, envir = marginaleffects_settings) } settings_rm <- function(name = NULL) { if (is.null(name)) { rm(list = names(marginaleffects_settings), envir = marginaleffects_settings) } else if ("name" %in% names(marginaleffects_settings)) { rm(list = name, envir = marginaleffects_settings) } } settings_equal <- function(name, comparison) { k <- settings_get(name) if (!is.null(k) && length(comparison) == 1 && k == comparison) { out <- TRUE } else if (!is.null(k) && length(comparison) > 1 && k %in% comparison) { out <- TRUE } else { out <- FALSE } return(out) } marginaleffects/R/hypothesis_helper.R0000644000176200001440000001177014631452335017502 0ustar liggesusers#' (EXPERIMENTAL) This experimental function will soon be deprecated. Please supply a formula or function to the `hypothesis` argument to conduct (group-wise) hypothesis tests. #' #' @param hypothesis String or Function. Compute a test statistic. #' - String: "reference" or "sequential" #' - Function: Accepts a single argument named `estimate` and returns a numeric vector. #' @param by Character vector. Variable names which indicate subgroups in which the `hypothesis` function should be applied. #' @param label Function. Accepts a vector of row labels and combines them to create hypothesis labels. #' @param label_columns Character vector. Column names to use for hypothesis labels. Default is `c("group", "term", "rowid", attr(x, "variables_datagrid"), attr(x, "by"))`. #' @param comparison String. "ratio" or "difference" #' @param internal Logical. Raises a deprecation warning when FALSE. #' @return `specify_hypothesis()` is a "function factory", which means that executing it will return a function suitable for use in the `hypothesis` argument of a `marginaleffects` function. #' @export specify_hypothesis <- function( hypothesis = "reference", comparison = "difference", label = NULL, label_columns = NULL, by = c("term", "group", "contrast"), internal = FALSE) { if (!isTRUE(internal)) { insight::format_warning("The `specify_hypothesis()` function was marked as experimental and will be deprecate. Use the formula interface to the `hypothesis` argument to specify group-wise hypothesis tests.") } checkmate::assert_choice(comparison, c("ratio", "difference")) checkmate::assert_character(by, null.ok = TRUE) checkmate::assert_function(label, null.ok = TRUE) checkmate::assert_character(label_columns, null.ok = TRUE) checkmate::assert( checkmate::check_function(hypothesis), checkmate::check_choice(hypothesis, choices = c("reference", "sequential", "meandev")) ) if (is.null(label)) label <- function(x) "custom" if (identical(hypothesis, "reference")) { if (comparison == "difference") { hypothesis <- function(x) (x - x[1])[2:length(x)] label <- function(x) sprintf("(%s) - (%s)", x, x[1])[2:length(x)] } else { hypothesis <- function(x) (x / x[1])[2:length(x)] label <- function(x) sprintf("(%s) / (%s)", x, x[1])[2:length(x)] } } else if (identical(hypothesis, "sequential")) { if (comparison == "difference") { hypothesis <- function(x) (x - data.table::shift(x))[2:length(x)] label = function(x) sprintf("(%s) - (%s)", x, data.table::shift(x))[2:length(x)] } else { hypothesis <- function(x) (x / data.table::shift(x))[2:length(x)] label = function(x) sprintf("(%s) / (%s)", x, data.table::shift(x))[2:length(x)] } } else if (identical(hypothesis, "meandev")) { hypothesis <- function(x) x - mean(x) label <- function(x) sprintf("(%s) - Avg", x) } fun <- function(x) { x <- data.table::copy(x) estimate <- x$estimate # automatic by argument if (is.null(by)) { by <- grep("^term$|^contrast|^group$", colnames(x), value = TRUE) if (length(by) == 0) by <- NULL } else { bad <- setdiff(by, c(colnames(x), "term", "group", "contrast", "rowid")) if (length(bad) > 0) { msg <- sprintf("Missing column(s): %s", paste(bad, collapse = ", ")) insight::format_error(msg) } by <- intersect(by, colnames(x)) if (length(by) == 0) by <- NULL } # row labels if (!"rowid" %in% colnames(x)) x[, "rowid" := seq_len(.N)] if (is.null(label_columns)) { # TODO check if we need rowid # label_columns <- c("group", "term", "rowid", attr(x, "variables_datagrid"), attr(x, "by")) label_columns <- c("group", "term", attr(x, "variables_datagrid"), attr(x, "by")) } label_columns <- setdiff(label_columns, setdiff(by, "rowid")) label_columns <- intersect(label_columns, colnames(x)) if (length(label_columns) == 0) label_columns <- "rowid" tmp <- x[, ..label_columns] for (col in label_columns) { if (length(unique(tmp[[col]])) == 1) { tmp[, (col) := NULL] } else { tmp[, (col) := sprintf("%s[%s]", col, tmp[[col]])] } } tmp <- apply(tmp, 1, paste, collapse = ", ") x[, marginaleffects_internal_label := tmp] if (is.null(by)) { out <- x[, list( hypothesis = label(marginaleffects_internal_label), estimate = hypothesis(estimate))] } else { out <- x[, list( hypothesis = label(marginaleffects_internal_label), estimate = hypothesis(estimate)), by = by] } attr(out, "hypothesis_function_by") <- by return(out) } return(fun) } marginaleffects/R/methods_mlr3.R0000644000176200001440000000115714631452335016342 0ustar liggesusers#' @include get_predict.R #' @rdname get_predict #' @keywords internal #' @export get_predict.Learner <- function(model, newdata, type = NULL, ...) { if (!is.null(type) && !type %in% model$predict_types) { msg <- sprintf("Valid `type` values: %s", paste(model$predict_types, collapse = ", ")) insight::format_error(msg) } out <- drop(stats::predict(model, newdata = newdata, predict_type = type)) out <- data.frame(rowid = seq_along(out), estimate = out) return(out) } #' @include get_vcov.R #' @rdname get_vcov #' @export get_vcov.Learner <- function(model, ...) { return(FALSE) }marginaleffects/R/plot_comparisons.R0000644000176200001440000001502614631452335017335 0ustar liggesusers#' 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, additional_variables = c(names(condition), by), 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) } marginaleffects/R/methods_robustlmm.R0000644000176200001440000000155514631452335017513 0ustar liggesusers#' @rdname set_coef #' @export set_coef.rlmerMod <- function(model, coefs, ...) { model@beta <- coefs model } #' @rdname get_predict #' @export get_predict.rlmerMod <- function(model, newdata = insight::get_data(model), type = "response", ...) { args <- list(...) # some predict methods raise warnings on unused arguments unused <- c("type", "normalize_dydx", "step_size", "numDeriv_method", "conf_level", "conf.level", "internal_call", "return_format", "vcov", "eps", "modeldata") args <- args[setdiff(names(args), unused)] args[["object"]] <- model args[["newdata"]] <- newdata out <- data.frame(rowid = seq_len(nrow(newdata)), estimate = do.call("predict", args)) return(out) } marginaleffects/R/get_model_matrix.R0000644000176200001440000000134114631452335017260 0ustar liggesusers#' Get a named model matrix #' #' @inheritParams slopes #' @rdname get_model_matrix #' @keywords internal #' @export get_model_matrix <- function(model, newdata) { UseMethod("get_model_matrix", model) } #' @rdname get_model_matrix #' @keywords internal #' @export get_model_matrix.default <- function(model, newdata) { # faster if (class(model)[1] %in% c("lm", "glm")) { out <- hush(stats::model.matrix(model, data = newdata)) # more general } else { out <- hush(insight::get_modelmatrix(model, data = newdata)) } beta <- get_coef(model) if (!isTRUE(nrow(out) == nrow(newdata)) || !isTRUE(ncol(out) == length(beta))) { return(NULL) } else { return(out) } }marginaleffects/R/imputation.R0000644000176200001440000000413614631452335016133 0ustar liggesusersprocess_imputation <- function(x, call_attr, marginal_means = FALSE) { insight::check_if_installed("mice") if (inherits(x, "mira")) { x <- x$analyses } else if (inherits(x, "amest")) { x <- x } mfx_list <- vector("list", length(x)) for (i in seq_along(x)) { calltmp <- call_attr calltmp[["model"]] <- x[[i]] # not sure why but this breaks marginal_means on "modeldata specified twice" if (isFALSE(marginal_means)) { calltmp[["modeldata"]] <- get_modeldata( x[[i]], additional_variables = FALSE) } mfx_list[[i]] <- evalup(calltmp) if (i == 1) { out <- mfx_list[[1]] } mfx_list[[i]]$term <- seq_len(nrow(mfx_list[[i]])) class(mfx_list[[i]]) <- c("marginaleffects_mids", class(mfx_list[[i]])) } mipool <- mice::pool(mfx_list) for (col in c("estimate", "statistic", "p.value", "conf.low", "conf.high")) { if (col %in% colnames(out) && col %in% colnames(mipool$pooled)) { out[[col]] <- mipool$pooled[[col]] } else { out[[col]] <- NULL } } if ("df" %in% colnames(mipool$pooled)) { out$df <- mipool$pooled$df } out$std.error <- sqrt(mipool$pooled$t) out <- get_ci( out, vcov = call_attr[["vcov"]], conf_level = call_attr[["conf_level"]], df = mipool$pooled$df) attr(out, "inferences") <- mipool attr(out, "model") <- mice::pool(lapply(mfx_list, attr, "model")) return(out) } #' tidy helper #' #' @noRd #' @export tidy.marginaleffects_mids <- function(x, ...) { if (!"std.error" %in% colnames(x)) { insight::format_error('The output does not include a `std.error` column. Some models do not generate standard errors when estimates are backtransformed (e.g., GLM models). One solution is to use `type="response"` for those models.') } out <- as.data.frame(x[, c("estimate", "std.error")]) out$term <- seq_len(nrow(out)) return(out) } #' glance helper #' #' @noRd #' @export glance.marginaleffects_mids <- function(x, ...) { data.frame() } marginaleffects/R/type_dictionary.R0000644000176200001440000000532314631452335017147 0ustar liggesusers #' internal function to build the type dictionary #' #' @noRd type_dictionary_build <- function() { text <- 'class,type other,response other,class other,link bam,response bam,link bart,ev bart,ppd betareg,response betareg,link betareg,precision betareg,quantile betareg,variance bife,response bife,link bracl,probs brglmFit,response brglmFit,link brmsfit,response brmsfit,link brmsfit,prediction brmsfit,average brmultinom,probs brmultinom,class clm,prob clm,cum.prob clm,linear.predictor clogit,expected clogit,lp clogit,risk clogit,survival coxph,expected coxph,lp coxph,risk coxph,survival crch,response crch,location crch,scale crch,density hetprob,pr hetprob,xb hxlr,location hxlr,cumprob hxlr,scale hxlr,density ivpml,pr ivpml,xb flexsurvreg,response, flexsurvreg,mean, flexsurvreg,link, flexsurvreg,lp, flexsurvreg,linear, flexsurvreg,rmst, flexsurvreg,survival, flexsurvreg,hazard, flexsurvreg,cumhaz, fixest,invlink(link) fixest,response fixest,link hurdle,response hurdle,prob hurdle,count hurdle,zero iv_robust,response lm,response gam,response gam,link Gam,invlink(link) Gam,response Gam,link geeglm,response geeglm,link Gls,lp glimML,response glimML,link glm,invlink(link) glm,response glm,link glmerMod,response glmerMod,link glmrob,response glmrob,link glmmTMB,response glmmTMB,link glmmTMB,conditional glmmTMB,zprob glmmTMB,zlink glmmTMB,disp glmmPQL,response glmmPQL,link glmx,response ivreg,response lmerMod,response lmerModLmerTest,response lmrob,response lm_robust,response lrm,fitted lrm,lp lrm,mean mblogit,response mblogit,latent mblogit,link mclogit,response mclogit,latent mclogit,link MCMCglmm,response model_fit,numeric model_fit,prob model_fit,class workflow,numeric workflow,prob workflow,class multinom,probs multinom,latent mhurdle,E mhurdle,Ep mhurdle,p mlogit,response mvgam,response mvgam,link mvgam,expected mvgam,detection mvgam,latent_N negbin,invlink(link) negbin,response negbin,link ols,lp "oohbchoice", "probability", "oohbchoice", "utility", orm,fitted orm,mean orm,lp polr,probs rendo.base,response rendo.base,link rlm,response selection,response selection,link selection,unconditional speedlm,response speedglm,response speedglm,link stanreg,response stanreg,link survreg,response survreg,link survreg,quantile svyglm,response svyglm,link svyolr,probs tobit,response tobit1,expvalue tobit1,linpred tobit1,prob zeroinfl,response zeroinfl,prob zeroinfl,count zeroinfl,zero' out <- utils::read.csv( text = text, colClasses = c("character", "character")) for (i in 1:2) { out[[i]] <- trimws(out[[i]]) } return(out) } #' type dictionary #' #' insight::get_predict accepts a `predict` argument #' stats::predict accepts a `type` argument #' this dictionary converts #' @noRd type_dictionary <- type_dictionary_build() marginaleffects/R/methods_mlogit.R0000644000176200001440000000265614631452335016765 0ustar liggesusers#' @rdname get_predict #' @export get_predict.mlogit <- function(model, newdata, ...) { mat <- stats::predict(model, newdata = as.data.frame(newdata)) if (isTRUE(checkmate::check_atomic_vector(mat))) { out <- data.table(rowid = seq_along(mat), group = names(mat), estimate = mat) } else { out <- data.table(rowid = rep(seq_len(nrow(mat)), rep = ncol(mat)), group = rep(colnames(mat), each = nrow(mat)), estimate = as.vector(mat)) } setkey(out, rowid, group) if ("term" %in% colnames(newdata)) { out[, "term" := newdata[["term"]]] } # do not convert to factor because DV is often "yes" or "no" while the "group" is outcome levels. # out$group <- group_to_factor(out$group, model) return(out) } #' @include sanity_model.R #' @rdname sanitize_model_specific #' @keywords internal sanitize_model_specific.mlogit <- function(model, newdata, ...) { if (!is.null(newdata)) { nchoices <- length(unique(model$model$idx[, 2])) if (!isTRUE(nrow(newdata) %% nchoices == 0)) { msg <- sprintf("The `newdata` argument for `mlogit` models must be a data frame with a number of rows equal to a multiple of the number of choices: %s.", nchoices) stop(msg, call. = FALSE) } } return(model) } marginaleffects/R/get_contrast_data.R0000644000176200001440000001604614631452335017432 0ustar liggesusersget_contrast_data <- function(model, newdata, variables, cross, modeldata = NULL, ...) { lo <- hi <- ter <- lab <- original <- rowid <- list() # after variable class assignment if (is.null(modeldata)) { modeldata <- attr(newdata, "newdata_modeldata") } # sometimes needed for extensions when get_data doesn't work if (is.null(modeldata) || nrow(modeldata) == 0) { modeldata <- newdata } # safety need for extensions not supported by `insight` variable_classes <- attr(newdata, "newdata_variable_class") if (length(variable_classes) == 0) { newdata <- set_variable_class(newdata, model) variable_classes <- attr(newdata, "marginaleffects_variable_class") } if (length(attr(modeldata, "marginaleffects_variable_class")) == 0) { modeldata <- set_variable_class(modeldata, model) } if (any(c("factor", "character") %in% variable_classes)) { first_cross <- names(variable_classes[variable_classes %in% c("factor", "character")])[1] } else { first_cross <- NULL } # must use `as.data.table()` because `setDT()` does not handle columns with # more dimensions (e.g., "idx" in {mlogit}) newdata <- as.data.table(newdata) for (v in variables) { args <- list( model = model, newdata = newdata, variable = v, cross = cross, first_cross = identical(v$name, first_cross), modeldata = modeldata) args <- append(args, list(...)) # logical and character before factor used to be important; but I don't think so anymore if (get_variable_class(modeldata, v$name, "logical")) { fun <- get_contrast_data_logical } else if (get_variable_class(modeldata, v$name, "character")) { fun <- get_contrast_data_character } else if (get_variable_class(modeldata, v$name, "categorical")) { fun <- get_contrast_data_factor } else if (get_variable_class(modeldata, v$name, "numeric")) { fun <- get_contrast_data_numeric } else { msg <- sprintf("Class of the `%s` variable is class is not supported.", v$name) stop(msg, call. = FALSE) } tmp <- do.call("fun", args) lo[[v$name]] <- tmp$lo if (isTRUE(cross)) { lo[[v$name]][[paste0("null_contrast_", v$name)]] <- tmp$contrast_null } hi[[v$name]] <- tmp$hi ter[[v$name]] <- tmp$ter lab[[v$name]] <- tmp$lab original[[v$name]] <- tmp$original rowid[[v$name]] <- tmp$rowid } clean <- function(x) { for (col in colnames(x)) { # tobit1 introduces AsIs columns if (inherits(x[[col]], "AsIs")) { x[[col]] <- as.numeric(x[[col]]) } # plm creates c("pseries", "numeric"), but when get_contrast_data # assigns +1 numeric, we lose the inheritance if (inherits(x[[col]], "pseries")) { x[[col]] <- as.numeric(x[[col]]) } # strip labelled data which break rbindlist() cl <- class(x[[col]]) if (length(cl) == 2 && cl[1] == "labelled") { class(x[[col]]) <- class(x[[col]])[2] } # mlogit uses a `newdata` with one row per unit-choice and returns # an `idx` column with the choice label in second position if (inherits(model, "mlogit") && inherits(x[[col]], "idx")) { x[[col]] <- NULL } } return(x) } lo <- lapply(lo, clean) hi <- lapply(hi, clean) original <- lapply(original, clean) # single contrast if (!isTRUE(cross)) { lo <- rbindlist(lo, fill = TRUE) hi <- rbindlist(hi, fill = TRUE) original <- rbindlist(original, fill = TRUE) # long names to avoid user-supplied colname conflict marginaleffects_ter <- unlist(ter, use.names = FALSE) marginaleffects_lab <- unlist(lab, use.names = FALSE) lo[, "term" := marginaleffects_ter] hi[, "term" := marginaleffects_ter] original[, "term" := marginaleffects_ter] lo[, "contrast" := marginaleffects_lab] hi[, "contrast" := marginaleffects_lab] original[, "contrast" := marginaleffects_lab] # cross contrast } else { # drop variables for which we have contrasts for (i in seq_along(lo)) { if (i == 1) { # keep rowid and original data only in one of the datasets idx_lo <- setdiff(names(variables), names(lo)[i]) idx_hi <- setdiff(names(variables), names(hi)[i]) idx_or <- setdiff(names(variables), names(hi)[i]) } else { # exclude rowid and variables excluded from `variables`, for # which we do not compute cross-contrasts contrast_null <- grep("rowid|^null_contrast_", colnames(lo[[i]]), value = TRUE) idx_lo <- c(setdiff(names(lo[[i]]), c(contrast_null, names(variables))), setdiff(names(variables), names(lo)[[i]])) idx_hi <- c(setdiff(names(hi[[i]]), c(contrast_null, names(variables))), setdiff(names(variables), names(hi)[[i]])) idx_or <- c(setdiff(names(original[[i]]), c(contrast_null, names(variables))), setdiff(names(variables), names(original)[[i]])) } lo[[i]] <- data.table(lo[[i]])[, !..idx_lo] hi[[i]] <- data.table(hi[[i]])[, !..idx_hi] original[[i]] <- data.table(original[[i]])[, !..idx_or] lo[[i]][[paste0("contrast_", names(lo)[i])]] <- lab[[i]] hi[[i]][[paste0("contrast_", names(hi)[i])]] <- lab[[i]] original[[i]][[paste0("contrast_", names(original)[i])]] <- lab[[i]] } fun <- function(x, y) merge(x, y, all = TRUE, allow.cartesian = TRUE, sort = FALSE) lo <- Reduce("fun", lo) hi <- Reduce("fun", hi) original <- Reduce("fun", original) # faster to rbind, but creates massive datasets. need cartesian join on rowid # lo <- cjdt(lo) # hi <- cjdt(hi) # if there are fewer null_contrast_* columns, then there is at least # one always non-null variable type, so we keep everything idx <- grepl("^null_contrast_", colnames(lo)) idx_df <- lo[, ..idx] lo <- lo[, !..idx] if (sum(idx) == length(variables)) { idx <- rowSums(idx_df) < ncol(idx_df) lo <- lo[idx] hi <- hi[idx] original <- original[idx] } } # get_predict() is much faster if we only build the model matrix once lo <- get_model_matrix_attribute(model, lo) hi <- get_model_matrix_attribute(model, hi) original <- get_model_matrix_attribute(model, original) out <- list(lo = lo, hi = hi, original = original) return(out) } marginaleffects/R/methods_glmmTMB.R0000755000176200001440000000405214631452335016764 0ustar liggesusers#' @include get_predict.R #' @rdname get_predict #' @keywords internal #' @export get_predict.glmmTMB <- function(model, newdata = insight::get_data(model), type = "response", newparams = NULL, ...) { if (inherits(vcov, "vcov.glmmTMB")) { vcov <- vcov[[1]] } # hack to avoid re-optimization # see https://github.com/vincentarelbundock/marginaleffects/issues/1064 b_vec <- model$obj$env$parList()$b if (length(b_vec)>0) { model$modelInfo$map$b <- factor(rep(NA,length(b_vec))) } np <- model$fit$par if (!is.null(newparams)) { np[1:length(newparams)] <- newparams } out <- get_predict.default( model = model, newdata = newdata, type = type, allow.new.levels = TRUE, # otherwise we get errors in marginal_means() newparams = np, ...) return(out) } #' @include get_vcov.R #' @rdname get_vcov #' @export get_vcov.glmmTMB <- function(model, ...) { out <- stats::vcov(model, full = TRUE) return(out) } #' @include get_coef.R #' @rdname get_coef #' @export get_coef.glmmTMB <- function(model, ...) { glmmTMB::fixef(model)$cond } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.glmmTMB <- function(model, coefs, ...) { # use predict(`newparams`) for this kind of model return(model) } #' @rdname sanitize_model_specific sanitize_model_specific.glmmTMB <- function(model, vcov = TRUE, re.form = NULL, ...) { # re.form=NA if (!isTRUE(checkmate::check_flag(vcov))) { msg <- "For this model type, `vcov` must be `TRUE` or `FALSE`." insight::format_error(msg) } if (!isTRUE(is.na(re.form))) { msg <- "For this model type, `marginaleffects` only takes into account the uncertainty in fixed-effect parameters. You can use the `re.form=NA` argument to acknowledge this explicitly and silence this warning." insight::format_warning(msg) } return(model) } marginaleffects/R/get_se_delta.R0000644000176200001440000001124714631452335016362 0ustar liggesusersget_se_delta_marginalmeans <- function(model, variables, newdata, type, cross = FALSE, ...) { get_marginalmeans( model = model, variables = variables, newdata = newdata, type = type, cross = cross, ... )$estimate } get_se_delta_contrasts <- function(model, variables, newdata, type, hypothesis, lo, hi, original, cross, ...) { get_contrasts(model, newdata = newdata, variables = variables, type = type, hypothesis = hypothesis, lo = lo, hi = hi, original = original, cross = cross, verbose = FALSE, deltamethod = TRUE, ... )$estimate } #' Compute standard errors using the delta method #' #' @inheritParams slopes #' @param FUN a function which accepts a `model` and other inputs and returns a #' vector of estimates (marginal effects, marginal means, etc.) #' @param index data.frame over which we aggregate J_mean (matches tidy() output) #' @return vector of standard errors #' @noRd get_se_delta <- function(model, vcov, FUN, type = NULL, newdata = NULL, index = NULL, eps = NULL, J = NULL, hypothesis = NULL, numderiv = NULL, ...) { # delta method does not work for these models bad <- c("brmsfit", "stanreg", "bart") if (any(bad %in% class(model))) { return(NULL) } coefs <- get_coef(model, ...) # TODO: this is a terrible sanity check # some vcov methods return an unnamed matrix, some have duplicate names flag <- anyDuplicated(colnames(vcov)) == 0 || anyDuplicated(names(coefs)) == 0 if (flag && !is.null(dimnames(vcov)) && all(names(coefs) %in% colnames(vcov))) { bnames <- intersect(names(coefs), colnames(vcov)) vcov <- vcov[bnames, bnames, drop = FALSE] colnames(vcov) <- row.names(vcov) <- names(coefs) coefs <- coefs[bnames] } # input: named vector of coefficients # output: gradient inner <- function(x) { names(x) <- names(coefs) model_tmp <- set_coef(model, x, ...) # do not pass NULL arguments. Important for `deltam` to allow users to supply FUN without ... args <- c(list(model = model_tmp, hypothesis = hypothesis), list(...)) if (inherits(model, "gamlss")) args[["safe"]] <- FALSE if (!is.null(eps)) args[["eps"]] <- eps if (!is.null(type)) args[["type"]] <- type if (!is.null(newdata)) args[["newdata"]] <- newdata if (!is.null(J)) args[["J"]] <- J if (!is.null(eps)) args[["eps"]] <- eps if (inherits(model, "glmmTMB")) { args$newparams <- x } g <- do.call("FUN", args) return(g) } if (is.null(J) || !is.null(hypothesis)) { args <- list( func = inner, x = coefs, numderiv = numderiv) J <- do.call("get_jacobian", args) colnames(J) <- names(get_coef(model, ...)) } # align J and V: This might be a problematic hack, but I have not found examples yet. V <- vcov if (!isTRUE(ncol(J) == ncol(V))) { beta <- get_coef(model, ...) # Issue #718: ordinal::clm in test-pkg-ordinal.R if (anyNA(beta) && anyDuplicated(names(beta)) && ncol(J) > ncol(V) && ncol(J) == length(beta) && length(stats::na.omit(beta)) == ncol(V)) { J <- J[, !is.na(beta), drop = FALSE] } else { cols <- intersect(colnames(J), colnames(V)) if (length(cols) == 0) { insight::format_error("The jacobian does not match the variance-covariance matrix.") } V <- V[cols, cols, drop = FALSE] J <- J[, cols, drop = FALSE] } } # Var(dydx) = J Var(beta) J' # computing the full matrix is memory-expensive, and we only need the diagonal # algebra trick: https://stackoverflow.com/a/42569902/342331 se <- sqrt(rowSums(tcrossprod(J, V) * J)) se[se == 0] <- NA_real_ attr(se, "jacobian") <- J return(se) } marginaleffects/R/methods_sampleSelection.R0000644000176200001440000000142014631452335020605 0ustar liggesusers# TODO: heckit standard errors are not available because `vcov` is block diagonal with NAs #' @rdname get_coef #' @export get_coef.selection <- function(model, ...) { # sampleSelection::selection if (as.list(model$call)[[1]] == "selection") { out <- model$estimate # sampleSelection::heckit } else if (as.list(model$call)[[1]] == "heckit") { out <- model$coefficients } return(out) } #' @rdname set_coef #' @export set_coef.selection <- function(model, coefs, ...) { # sampleSelection::selection if (as.list(model$call)[[1]] == "selection") { model[["estimate"]] <- coefs # sampleSelection::heckit } else if (as.list(model$call)[[1]] == "heckit") { model[["coefficients"]] <- coefs } return(model) } marginaleffects/R/methods_MCMCglmm.R0000644000176200001440000000167114631452335017062 0ustar liggesusers#' @rdname get_predict #' @export get_predict.MCMCglmm <- function( model, newdata, type = "response", ndraws = 1000, ...) { ndraws_mod <- nrow(model$VCV) if (ndraws < ndraws_mod) { idx <- sample(seq_len(ndraws_mod), ndraws) } else { idx <- seq_len(ndraws_mod) } draws <- lapply(idx, function(i) stats::predict(model, newdata = newdata, it = i, ...)) draws <- do.call("cbind", draws) out <- data.frame( rowid = seq_len(nrow(newdata)), estimate = apply(draws, MARGIN = 1, FUN = stats::median)) attr(out, "posterior_draws") <- draws return(out) } #' @rdname get_vcov #' @export get_vcov.MCMCglmm <- function(model, vcov = NULL, ...) { if (!is.null(vcov) && !is.logical(vcov)) { insight::format_warning("The `vcov` argument is not supported for models of this class.") } return(NULL) }marginaleffects/R/methods_crch.R0000644000176200001440000000422014631452335016376 0ustar liggesusers#' @include set_coef.R #' @rdname set_coef #' @export set_coef.crch <- function(model, coefs, ...) { # coefs are split between location coefs (which can be length 0) and scale coefs # (which must be length > 0 and always start with "(scale)_" due to get_parameters(), # to match with get_varcov(., component = "all") output). In crch object, these # are stored as two elements in a list, with scale coefs lacking the "(scale)_" # prefix, so we remove it. location_coefs <- coefs[!startsWith(names(coefs), "(scale)_")] scale_coefs <- coefs[startsWith(names(coefs), "(scale)_")] names(scale_coefs) <- sub("(scale)_", "", names(scale_coefs), fixed = TRUE) if (length(location_coefs) > 0) { model[["coefficients"]]$location[names(location_coefs)] <- location_coefs } model[["coefficients"]]$scale[names(scale_coefs)] <- scale_coefs model } #' @include get_predict.R #' @rdname get_predict #' @export get_predict.crch <- function(model, newdata = NULL, type = "location", ...) { pred <- stats::predict(model, newdata = newdata, type = type) sanity_predict_vector(pred = pred, model = model, newdata = newdata, type = type) sanity_predict_numeric(pred = pred, model = model, newdata = newdata, type = type) out <- data.frame( rowid = 1:nrow(newdata), estimate = pred) return(out) } #' @include set_coef.R #' @rdname set_coef #' @export set_coef.hxlr <- function(model, coefs, ...) { # in crch::hxlr, coefficients are held in: # model$coefficients$intercept # model$coefficients$location # model$coefficients$scale # # note: there are no prefixes in coef() output, so coefs may have same name out <- model idx_int <- length(model$coefficients$intercept) idx_loc <- length(model$coefficients$location) out$coefficients$intercept[] <- coefs[1:idx_int] out$coefficients$location[] <- coefs[idx_int + 1:idx_loc] out$coefficients$scale[] <- coefs[(idx_int + idx_loc + 1):length(coefs)] return(out) } #' @export #' @noRd get_predict.hxlr <- get_predict.crch marginaleffects/R/get_vcov.R0000644000176200001440000001130314631452335015550 0ustar liggesusers#' Get a named variance-covariance matrix from a model object (internal function) #' #' @inheritParams slopes #' @return A named square matrix of variance and covariances. The names must match the coefficient names. #' @rdname get_vcov #' @keywords internal #' @export get_vcov <- function(model, ...) { UseMethod("get_vcov", model) } #' @rdname get_vcov #' @export get_vcov.default <- function(model, vcov = NULL, ...) { if (isFALSE(vcov)) { return(NULL) } vcov <- sanitize_vcov(model = model, vcov = vcov) if (isTRUE(checkmate::check_matrix(vcov))) { return(vcov) } # {insight} args <- get_varcov_args(model, vcov) args[["x"]] <- model args[["component"]] <- "all" # 1st try: with arguments fun <- get("get_varcov", asNamespace("insight")) out <- myTryCatch(do.call("fun", args)) # 2nd try: without arguments if (!isTRUE(checkmate::check_matrix(out$value, min.rows = 1))) { out <- myTryCatch(insight::get_varcov(model)) if (isTRUE(checkmate::check_matrix(out$value, min.rows = 1))) { msg <- "Unable to extract a variance-covariance matrix using this `vcov` argument. Standard errors are computed using the default variance instead. Perhaps the model or argument is not supported by the `sandwich` ('HC0', 'HC3', ~clusterid, etc.) or `clubSandwich` ('CR0', etc.) packages. If you believe that the model is supported by one of these two packages, you can open a feature request on Github." insight::format_warning(msg) } } if (!isTRUE(checkmate::check_matrix(out$value, min.rows = 1))) { msg <- "Unable to extract a variance-covariance matrix from this model." warning(msg, call. = FALSE) return(NULL) # valid matrix with warning } else if (!is.null(out$warning)) { warning(out$warning$message, call. = FALSE) } out <- out[["value"]] # problem: no row.names if (is.null(row.names(out))) { coefs <- get_coef(model) if (ncol(out) == length(coefs)) { termnames <- names(stats::coef(model)) if (length(termnames) == ncol(out)) { colnames(out) <- termnames row.names(out) <- termnames } } else { return(NULL) } } # problem: duplicate colnames if (anyDuplicated(colnames(out)) == 0) { coefs <- get_coef(model, ...) # 1) Check above is needed for `AER::tobit` and others where `out` # includes Log(scale) but `coef` does not Dangerous for `oridinal::clm` # and others where there are important duplicate column names in # `out`, and selecting with [,] repeats the first instance. # 2) Sometimes out has more columns than coefs if (all(names(coefs) %in% colnames(out))) { out <- out[names(coefs), names(coefs), drop = FALSE] } } return(out) # NOTES: # survival::coxph with 1 regressor produces a vector } #' Take a `summary()` style `vcov` argument and convert it to #' `insight::get_varcov()` #' #' @keywords internal get_varcov_args <- function(model, vcov) { if (is.null(vcov) || isTRUE(checkmate::check_matrix(vcov))) { out <- list() return(out) } if (isTRUE(checkmate::check_formula(vcov))) { out <- list("vcov" = "vcovCL", "vcov_args" = list("cluster" = vcov)) return(out) } if (isTRUE(vcov == "satterthwaite") || isTRUE(vcov == "kenward-roger")) { if (!isTRUE(inherits(model, "lmerMod")) && !isTRUE(inherits(model, "lmerModTest"))) { msg <- 'Satterthwaite and Kenward-Roger corrections are only available for linear mixed effects models.' stop(msg, call. = FALSE) } if (isTRUE(vcov == "satterthwaite")) { return(list()) } else { return(list(vcov = "kenward-roger")) } } out <- switch(vcov, "stata" = list(vcov = "HC2"), "robust" = list(vcov = "HC3"), "bootstrap" = list(vcov = "BS"), "outer-product" = list(vcov = "OPG"), list(vcov = vcov)) return(out) } get_vcov_label <- function(vcov) { if (is.null(vcov)) vcov <- "" if (!is.character(vcov)) return(NULL) out <- switch(vcov, "stata" = "Stata", "robust" = "Robust", "kenward-roger" = "Kenward-Roger", "satterthwaite" = "Satterthwaite", "HC" = , "HC0" = , "HC1" = , "HC2" = , "HC3" = , "HC4" = , "HC4m" = , "HC5" = , "HAC" = , "OPG" = vcov, "NeweyWest" = "Newey-West", "kernHAC" = "Kernel HAC", vcov ) return(out) } marginaleffects/R/get_contrast_data_numeric.R0000644000176200001440000001067014631452335021151 0ustar liggesusersget_contrast_data_numeric <- function(model, newdata, variable, modeldata, ...) { h <- variable[["eps"]] s <- m <- NA if (is.numeric(modeldata[[variable$name]])) { s <- stats::sd(modeldata[[variable$name]], na.rm = TRUE) m <- mean(modeldata[[variable$name]], na.rm = TRUE) } x <- newdata[[variable$name]] xmd <- modeldata[[variable$name]] make_label <- function(lab, val) { if (identical(lab, "custom")) return(lab) args <- append(list(lab), as.list(val)) out <- tryCatch( do.call("sprintf", args), error = function(e) lab) return(out) } # slope # by default variable$value, so we need to check this first slopes <- c( "dY/dX", "eY/eX", "eY/dX", "dY/eX", "mean(dY/dX)", "mean(eY/eX)", "mean(eY/dX)", "mean(dY/eX)") # manual high if (isTRUE(checkmate::check_data_frame(variable$value))) { if (all(c("low", "high") %in% colnames(variable$value))) { low <- variable$value$low high <- variable$value$high } else { low <- variable$value[[1]] high <- variable$value[[2]] } lab <- "manual" } else if (isTRUE(variable$label %in% slopes)) { low <- x - h / 2 high <- x + h / 2 lab <- variable$label } else if (identical(variable$label, "exp(dY/dX)")) { low <- x - h / 2 high <- x + h / 2 lab <- "exp(dY/dX)" # contrast_label is designed for categorical predictors # numeric contrasts first } else if (isTRUE(checkmate::check_numeric(variable$value, len = 1))) { direction <- getOption("marginaleffects_contrast_direction", default = "forward") if (isTRUE(direction == "center")) { low <- x - variable$value / 2 high <- x + variable$value / 2 } else if (isTRUE(direction == "backward")) { low <- x - variable$value high <- x } else { low <- x high <- x + variable$value } # wrap in parentheses, unless mean() because there are already parentheses # important to display ratios of x+1, etc. # label should not be `(mpg+1) - mpg` because that is misleading for centered contrast if (!isTRUE(grepl("mean", variable$label))) { lab <- sprintf("+%s", variable$value) } else { lab <- sprintf("mean(+%s)", variable$value) } } else if (isTRUE(checkmate::check_numeric(variable$value, len = 2))) { variable$value <- sort(variable$value) low <- variable$value[1] high <- variable$value[2] gap <- diff(variable$value) lab <- make_label(variable$label, rev(variable$value)) # character contrasts } else if (identical(variable$value, "sd")) { low <- m - s / 2 high <- m + s / 2 lab <- c("x + sd/2", "x - sd/2") if (!isTRUE(grepl("mean", variable$label))) { lab <- sprintf("(%s)", lab) } lab <- make_label(variable$label, lab) } else if (identical(variable$value, "2sd")) { low <- m - s high <- m + s lab <- c("x + sd", "x - sd") if (!isTRUE(grepl("mean", variable$label))) { lab <- sprintf("(%s)", lab) } lab <- make_label(variable$label, lab) } else if (identical(variable$value, "iqr")) { low <- stats::quantile(xmd, probs = .25, na.rm = TRUE) high <- stats::quantile(xmd, probs = .75, na.rm = TRUE) lab <- make_label(variable$label, c("Q3", "Q1")) } else if (identical(variable$value, "minmax")) { low <- min(xmd, na.rm = TRUE) high <- max(xmd, na.rm = TRUE) lab <- make_label(variable$label, c("Max", "Min")) } else if (isTRUE(checkmate::check_function(variable$value))) { tmp <- variable$value(x) low <- tmp[, 1] high <- tmp[, 2] lab <- "custom" } lo <- hi <- newdata lo[[variable$name]] <- low hi[[variable$name]] <- high out <- list(rowid = seq_len(nrow(newdata)), lo = lo, hi = hi, original = newdata, ter = rep(variable$name, nrow(newdata)), lab = rep(lab, nrow(newdata))) return(out) } marginaleffects/R/methods_flexsurv.R0000644000176200001440000000217414631452335017343 0ustar liggesusers#' @rdname set_coef #' @export set_coef.flexsurvreg <- function(model, coefs, ...) { out <- model out$res[, 1] <- coefs out$coefficients <- coefs return(out) } #' @rdname get_predict #' @export get_predict.flexsurvreg <- function(model, newdata, type, ...) { preds <- stats::predict( object = model, newdata = newdata, type = type, ... ) if (ncol(preds) == 1L) { if (names(preds) == '.pred') { gp <- unlist(lapply(preds$.pred, function(x) {x[, 1, drop = TRUE]})) val <- unlist(lapply(preds$.pred, function(x) {x[, 2, drop = TRUE]})) out <- data.frame( rowid = seq_len(nrow(preds)), group = as.vector(gp), estimate = as.vector(val) ) out$group <- group_to_factor(out$group, model) return(out) } out <- data.frame( rowid = seq_len(nrow(preds)), estimate = as.vector(preds[, 1, drop = TRUE]) ) return(out) } out <- data.frame( rowid = seq_len(nrow(preds)), group = as.vector(preds[, 1, drop = TRUE]), estimate = as.vector(preds[, 2, drop = TRUE]) ) out$group <- group_to_factor(out$group, model) out } marginaleffects/R/sanitize_numderiv.R0000644000176200001440000000176414631452335017505 0ustar liggesuserssanitize_numderiv <- function(numderiv) { checkmate::assert( checkmate::check_choice(numderiv, c("richardson", "fdforward", "fdcenter")), checkmate::check_list(numderiv, min.len = 1) ) if (isTRUE(checkmate::check_string(numderiv))) { numderiv <- list(numderiv) } if (length(numderiv) > 1) { if (numderiv[[1]] %in% c("fdforward", "fdcenter")) { if (any(!names(numderiv)[2:length(numderiv)] %in% "eps")) { stop("The only valid argument for this numeric differentiation method is `eps`.") } } else if (numderiv[[1]] == "richardson") { valid <- c("eps", "d", "zero_tol", "size", "r", "v") if (any(!names(numderiv)[2:length(numderiv)] %in% valid)) { stop(sprintf("The only valid arguments for this numeric differentiation method are: %s. See `?numDeriv::grad` for details.", paste(valid, collapse = ", ")), call. = FALSE) } } } return(numderiv) }marginaleffects/R/sanitize_newdata.R0000644000176200001440000002707014632657667017315 0ustar liggesuserssanitize_newdata_call <- function(scall, newdata = NULL, model, by = NULL) { if (rlang::quo_is_call(scall)) { if (grepl("^datagrid", rlang::call_name(scall))) { if (!"model" %in% rlang::call_args_names(scall)) { scall <- rlang::call_modify(scall, model = model) } } else if (isTRUE(rlang::call_name(scall) == "subset")) { argnames <- rlang::call_args_names(scall) if (!"x" %in% argnames && length(argnames) == 1) { tmp <- get_modeldata(model, additional_variables = by) scall <- rlang::call_modify(scall, x = tmp) } } else if (isTRUE(rlang::call_name(scall) == "filter")) { argnames <- rlang::call_args_names(scall) if (!".data" %in% argnames && length(argnames) == 1) { tmp <- get_modeldata(model, additional_variables = by) scall <- rlang::call_modify(scall, .data = tmp) } } else if (rlang::call_name(scall) %in% "visualisation_matrix") { if (!"x" %in% rlang::call_args_names(scall)) { scall <- rlang::call_modify(scall, x = get_modeldata) } } out <- rlang::eval_tidy(scall) } else { out <- newdata } return(out) } build_newdata <- function(model, newdata, by, modeldata) { if (isTRUE(checkmate::check_data_frame(by))) { by <- setdiff(colnames(by), "by") } else if (isTRUE(checkmate::check_flag(by))) { by <- NULL } args <- list(model = model) for (b in by) { args[[b]] <- unique } newdata_explicit <- TRUE # NULL -> modeldata if (is.null(newdata)) { newdata <- modeldata newdata_explicit <- FALSE # string -> datagrid() } else if (identical(newdata, "mean")) { newdata <- do.call("datagrid", args) } else if (identical(newdata, "median")) { args[["FUN_numeric"]] <- args[["FUN_integer"]] <- args[["FUN_logical"]] <- function(x) stats::median(x, na.rm = TRUE) newdata <- do.call("datagrid", args) } else if (identical(newdata, "tukey")) { args[["FUN_numeric"]] <- function(x) stats::fivenum(x, na.rm = TRUE) newdata <- do.call("datagrid", args) } else if (identical(newdata, "grid")) { args[["FUN_numeric"]] <- function(x) stats::fivenum(x, na.rm = TRUE) args[["FUN_factor"]] <- args[["FUN_character"]] <- args[["FUN_logical"]] <- unique newdata <- do.call("datagrid", args) # grid with all unique values of categorical variables, and numerics at their means } else if (isTRUE(newdata %in% c("balanced", "marginalmeans"))) { args[["grid_type"]] <- "balanced" newdata <- do.call("datagrid", args) # Issue #580: outcome should not duplicate grid rows dv <- hush(insight::find_response(model)) if (isTRUE(dv %in% colnames(newdata))) { newdata[[dv]] <- get_mean_or_mode(newdata[[dv]]) newdata <- unique(newdata) } } if (!inherits(newdata, "data.frame")) { msg <- "Unable to extract the data from model of class `%s`. This can happen in a variety of cases, such as when a `marginaleffects` package function is called from inside a user-defined function, or using an `*apply()`-style operation on a list. Please supply a data frame explicitly via the `newdata` argument." msg <- sprintf(msg, class(model)[1]) insight::format_error(msg) } # otherwise we get a warning in setDT() if (inherits(model, "mlogit") && isTRUE(inherits(modeldata[["idx"]], "idx"))) { modeldata$idx <- NULL } # required by some model-fitting functions data.table::setDT(modeldata) # required for the type of column indexing to follow data.table::setDF(newdata) out <- list( "newdata" = newdata, "newdata_explicit" = newdata_explicit, "modeldata" = modeldata ) return(out) } add_wts_column <- function(wts, newdata, model) { # weights must be available in the `comparisons()` function, NOT in # `tidy()`, because comparisons will often duplicate newdata for # multivariate outcomes and the like. We need to track which row matches # which. if (isFALSE(wts)) { return(newdata) } else if (isTRUE(wts)) { wtsname <- insight::find_weights(model) if (!is.character(wtsname) || length(wtsname) != 1 || !wtsname %in% colnames(newdata)) { msg <- "Unable to retrieve weights automatically from the model. Please specify `wts` argument explicitly." insight::format_error(msg) } else { newdata[["marginaleffects_wts_internal"]] <- newdata[[wtsname]] return(newdata) } } if (!isFALSE(wts)) { flag1 <- isTRUE(checkmate::check_string(wts)) && isTRUE(wts %in% colnames(newdata)) flag2 <- isTRUE(checkmate::check_numeric(wts, len = nrow(newdata))) if (!flag1 && !flag2) { msg <- sprintf("The `wts` argument must be a numeric vector of length %s, or a string which matches a column name in `newdata`. If you did not supply a `newdata` explicitly, `marginaleffects` extracted it automatically from the model object, and the `wts` variable may not have been available. The easiest strategy is often to supply a data frame such as the original data to `newdata` explicitly, and to make sure that it includes an appropriate column of weights, identified by the `wts` argument.", nrow(newdata)) stop(msg, call. = FALSE) } } # weights: before sanitize_variables if (!isFALSE(wts) && isTRUE(checkmate::check_string(wts))) { newdata[["marginaleffects_wts_internal"]] <- newdata[[wts]] } else { newdata[["marginaleffects_wts_internal"]] <- wts } return(newdata) } set_newdata_attributes <- function(model, modeldata, newdata, newdata_explicit) { attr(newdata, "newdata_explicit") <- newdata_explicit # column classes mc <- Filter(function(x) is.matrix(modeldata[[x]]), colnames(modeldata)) cl <- Filter(function(x) is.character(modeldata[[x]]), colnames(modeldata)) cl <- lapply(modeldata[, ..cl], unique) vc <- attributes(modeldata)$marginaleffects_variable_class column_attributes <- list( "matrix_columns" = mc, "character_levels" = cl, "variable_class" = vc) newdata <- set_marginaleffects_attributes(newdata, column_attributes, prefix = "newdata_") # {modelbased} sometimes attaches useful attributes exclude <- c("class", "row.names", "names", "data", "reference") modelbased_attributes <- get_marginaleffects_attributes(newdata, exclude = exclude) newdata <- set_marginaleffects_attributes(newdata, modelbased_attributes, prefix = "newdata_") # original data attr(newdata, "newdata_modeldata") <- modeldata if (is.null(attr(newdata, "marginaleffects_variable_class"))) { newdata <- set_variable_class(newdata, model = model) } return(newdata) } clean_newdata <- function(model, newdata) { # rbindlist breaks on matrix columns idx <- sapply(newdata, function(x) class(x)[1] == "matrix") if (any(idx)) { # Issue #363 # unpacking matrix columns works with {mgcv} but breaks {mclogit} if (inherits(model, "gam")) { newdata <- unpack_matrix_cols(newdata) } else { newdata <- newdata[, !idx, drop = FALSE] } } # we will need this to merge the original data back in, and it is better to # do it in a centralized upfront way. if (!"rowid" %in% colnames(newdata)) { newdata$rowid <- seq_len(nrow(newdata)) } # mlogit: each row is an individual-choice, but the index is not easily # trackable, so we pre-sort it here, and the sort in `get_predict()`. We # need to cross our fingers, but this probably works. if (inherits(model, "mlogit") && isTRUE(inherits(newdata[["idx"]], "idx"))) { idx <- list(newdata[["idx"]][, 1], newdata[["idx"]][, 2]) newdata <- newdata[order(newdata[["idx"]][, 1], newdata[["idx"]][, 2]), ] } # placeholder response resp <- insight::find_response(model) if (isTRUE(checkmate::check_character(resp, len = 1)) && !resp %in% colnames(newdata)) { y <- hush(insight::get_response(model)) # protect df or matrix response if (isTRUE(checkmate::check_atomic_vector(y))) { newdata[[resp]] <- y[1] } } return(newdata) } sanitize_newdata <- function(model, newdata, by, modeldata, wts) { if (!identical(newdata, "marginalmeans")) { # soft deprecation trick checkmate::assert( checkmate::check_data_frame(newdata, null.ok = TRUE), checkmate::check_choice(newdata, choices = c("mean", "median", "tukey", "grid", "balanced")), combine = "or") } tmp <- build_newdata(model = model, newdata = newdata, by = by, modeldata = modeldata) newdata <- tmp[["newdata"]] modeldata <- tmp[["modeldata"]] newdata_explicit <- tmp[["newdata_explicit"]] newdata <- clean_newdata(model, newdata) if (is.null(wts)) wts <- FALSE newdata <- add_wts_column(newdata = newdata, wts = wts, model = model) newdata <- set_newdata_attributes( model = model, modeldata = modeldata, newdata = newdata, newdata_explicit = newdata_explicit) # # sort rows of output when the user explicitly calls `by` or `datagrid()` # # otherwise, we return the same data frame in the same order, but # # here it makes sense to sort for a clean output. # sortcols <- attr(newdata, "newdata_variables_datagrid") # if (isTRUE(checkmate::check_character(by))) { # sortcols <- c(by, sortcols) # } # sortcols <- intersect(sortcols, colnames(newdata)) out <- data.table::copy(newdata) # if (length(sortcols) > 0) { # data.table::setorderv(out, cols = sortcols) # } # return(out) } dedup_newdata <- function(model, newdata, by, wts, comparison = "difference", cross = FALSE, byfun = NULL) { # issue #1113: elasticities should skip dedup because it is difficult to align x and y elasticities <- c("eyexavg", "eydxavg", "dyexavg") if (isTRUE(checkmate::check_choice(comparison, elasticities))) { return(data.table(newdata)) } elasticities <- c("eyex", "eydx", "dyex") if (!isFALSE(by) && isTRUE(checkmate::check_choice(comparison, elasticities))) { return(data.table(newdata)) } flag <- isTRUE(checkmate::check_string(comparison, pattern = "avg")) if (!flag && ( isFALSE(by) || # weights only make sense when we are marginalizing !isFALSE(wts) || !is.null(byfun) || !isFALSE(cross) || isFALSE(getOption("marginaleffects_dedup", default = TRUE)))) { return(newdata) } vclass <- attr(newdata, "marginaleffects_variable_class") # copy to allow mod by reference later without overwriting newdata out <- data.table(newdata) dv <- hush(unlist(insight::find_response(model), use.names = FALSE)) if (isTRUE(checkmate::check_string(dv)) && dv %in% colnames(out)) { out[, (dv) := NULL] vclass <- vclass[names(vclass) != dv] } if ("rowid" %in% colnames(out)) { out[, "rowid" := NULL] } categ <- c("factor", "character", "logical", "strata", "cluster", "binary") if (!all(vclass %in% categ)) { return(newdata) } cols <- colnames(out) out <- out[, .("marginaleffects_wts_internal" = .N), by = cols] data.table::setDF(out) out[["rowid_dedup"]] <- seq_len(nrow(out)) attr(out, "marginaleffects_variable_class") <- vclass return(out) } marginaleffects/R/bootstrap_rsample.R0000644000176200001440000000473514631452335017507 0ustar liggesusersbootstrap_rsample <- function(model, INF_FUN, ...) { # attached by `inferences()` conf_type <- attr(model, "inferences_conf_type") checkmate::assert_choice(conf_type, choices = c("perc", "bca")) # attached by `inferences()` conf_type <- attr(model, "inferences_conf_type") checkmate::assert_choice(conf_type, choices = c("perc", "norm", "basic", "bca")) # bootstrap using the original data and call modcall <- insight::get_call(model) modeldata <- get_modeldata(model, additional_variables = FALSE) data.table::setDF(modeldata) # evaluate the {marginaleffects} call to get output without inferences() # use ... because arguments are not the same for different {marginaleffects} functions dots <- list(...) dots[["vcov"]] <- FALSE # avoid recursion attr(model, "inferences_method") <- NULL out <- do.call(INF_FUN, c(list(model), dots)) # default confidence level may be implicit in original call, but we need numeric if (is.null(dots[["conf_level"]])) { conf_level <- 0.95 } else { conf_level <- dots[["conf_level"]] } bootfun <- function(data, ...) { modcall[["data"]] <- data modboot <- eval(modcall) modboot <- eval(modboot) args <- c(list(modboot, modeldata = data$data), dots) out <- do.call(INF_FUN, args) out <- tidy(out) # `rsample` averages by `term` columns; we don't use it anyway and assume things line up out$term <- seq_len(nrow(out)) return(out) } args <- attr(model, "inferences_dots") args[["data"]] <- modeldata args[["apparent"]] <- TRUE # require for "bca" splits <- do.call(rsample::bootstraps, args) splits$estimates <- lapply(splits$splits, bootfun) if (isTRUE(conf_type == "bca")) { ci <- rsample::int_bca( splits, statistics = estimates, .fn = bootfun, alpha = 1 - conf_level) } else { ci <- rsample::int_pctl( splits, statistics = estimates, alpha = 1 - conf_level) } out$conf.low <- ci$.lower out$conf.high <- ci$.upper attr(out, "inferences") <- splits draws <- lapply(splits$estimates, function(x) as.matrix(x[, "estimate", drop = FALSE])) draws[[length(draws)]] <- NULL # apparent=TRUE appended the original estimates to the end draws <- do.call("cbind", draws) colnames(draws) <- NULL attr(out, "posterior_draws") <- draws return(out) } marginaleffects/R/conformal.R0000755000176200001440000001003314631452335015716 0ustar liggesusersget_conformal_score <- function(x, score) { response_name <- insight::find_response(attr(x, "model")) response <- x[[response_name]] if (!is.numeric(response) && score != "softmax") { insight::format_error('The response must be numeric. Did you want to use `conformal_score="softmax"`?') } if (score == "residual_abs") { out <- abs(response - x$estimate) } else if (score == "residual_sq") { out <- (response - x$estimate)^2 } else if(score == "softmax") { model <- attr(x, "model") response <- x[[insight::find_response(model)]] if (is.numeric(response) && is_binary(response)) { # See p.4 of Angelopoulos, Anastasios N., and Stephen Bates. 2022. “A # Gentle Introduction to Conformal Prediction and Distribution-Free # Uncertainty Quantification.” arXiv. # https://doi.org/10.48550/arXiv.2107.07511. # 1 minus the softmax output of the true class out <- ifelse(response == 1, 1 - x$estimate, x$estimate) } else if ("group" %in% colnames(x)) { # HACK: is this fragile? I think `group` should always be character. idx <- as.character(response) == as.character(x$group) out <- 1 - x$estimate[idx] } else { insight::format_error("Failed to compute the conformity score.") } } return(out) } get_conformal_bounds <- function(x, score, conf_level) { model <- attr(x, "model") response_name <- insight::find_response(model) response <- x[[response_name]] d <- min(score[score > stats::quantile(score, probs = conf_level)]) if ("group" %in% colnames(x)) { q <- stats::quantile(score, probs = (length(score) + 1) * conf_level / length(score)) out <- x[x$estimate > (1 - q),] data.table::setDT(out) out <- out[, .(pred.set = list(unique(group))), by = c("rowid", response_name)] setorder(out, rowid) data.table::setDF(out) class(out) <- c("predictions", class(out)) attr(out, "variables_datagrid") <- response_name return(out) } else { # continuous outcome: conformity half-width x$pred.low <- x$estimate - d x$pred.high <- x$estimate + d } return(x) } conformal_split <- function(x, test, calibration, score, conf_level, ...) { # calibration # use original model---fitted on the training set---to make predictions in the calibration set # p_calib is the `predictions()` call, which we re-evaluate on newdata=calibration p_calib <- attr(x, "call") p_calib[["newdata"]] <- calibration p_calib[["vcov"]] <- FALSE # faster p_calib <- eval(p_calib) score <- get_conformal_score(p_calib, score = score) # test # use original model to make predictions in the test set p_test <- attr(x, "call") p_test[["newdata"]] <- test p_test <- eval(p_test) # bounds out <- get_conformal_bounds(p_test, score = score, conf_level = conf_level) return(out) } conformal_cv_plus <- function(x, test, R, score, conf_level, ...) { # cross-validation train <- get_modeldata(attr(x, "model")) idx <- sample(seq_len(nrow(train)), nrow(train)) idx <- split(idx, ceiling(seq_along(idx) / (length(idx) / R))) scores <- NULL for (i in idx) { data_cv <- train[-i,] # re-fit the original model on training sets withholding the CV fold model_cv <- stats::update(attr(x, "model"), data = data_cv) # use the updated model to make out-of-fold predictions # call_cv is the `predictions()` call, which we re-evaluate in-fold: newdata=train[i,] call_cv <- attr(x, "call") call_cv[["model"]] <- model_cv call_cv[["newdata"]] <- train[i,] call_cv[["vcov"]] <- FALSE # faster pred_cv <- eval(call_cv) # save the scores form each fold scores <- c(scores, get_conformal_score(pred_cv, score = score)) } # test out <- attr(x, "call") out[["newdata"]] <- test out <- eval(out) # bounds out <- get_conformal_bounds(out, score = scores, conf_level = conf_level) return(out) } marginaleffects/src/0000755000176200001440000000000014632712537014205 5ustar liggesusersmarginaleffects/src/RcppExports.cpp0000644000176200001440000000224014632674171017201 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include #include using namespace Rcpp; #ifdef RCPP_USE_GLOBAL_ROSTREAM Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif // eigenMatMult SEXP eigenMatMult(const Eigen::Map A, Eigen::Map B); RcppExport SEXP _marginaleffects_eigenMatMult(SEXP ASEXP, SEXP BSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const Eigen::Map >::type A(ASEXP); Rcpp::traits::input_parameter< Eigen::Map >::type B(BSEXP); rcpp_result_gen = Rcpp::wrap(eigenMatMult(A, B)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_marginaleffects_eigenMatMult", (DL_FUNC) &_marginaleffects_eigenMatMult, 2}, {NULL, NULL, 0} }; RcppExport void R_init_marginaleffects(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } marginaleffects/src/eigen.cpp0000644000176200001440000000033214631452336015773 0ustar liggesusers// [[Rcpp::depends(RcppEigen)]] #include // [[Rcpp::export]] SEXP eigenMatMult(const Eigen::Map A, Eigen::Map B){ Eigen::VectorXd C = A * B; return Rcpp::wrap(C); } marginaleffects/NAMESPACE0000644000176200001440000001316314631452335014635 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(coef,comparisons) S3method(coef,hypotheses) S3method(coef,marginalmeans) S3method(coef,predictions) S3method(coef,slopes) S3method(get_coef,afex_aov) S3method(get_coef,betareg) S3method(get_coef,bracl) S3method(get_coef,brmsfit) S3method(get_coef,brmultinom) S3method(get_coef,data.frame) S3method(get_coef,default) S3method(get_coef,gam) S3method(get_coef,gamlss) S3method(get_coef,glmmTMB) S3method(get_coef,lmerMod) S3method(get_coef,lmerModLmerTest) S3method(get_coef,mblogit) S3method(get_coef,merMod) S3method(get_coef,mlm) S3method(get_coef,multinom) S3method(get_coef,nls) S3method(get_coef,polr) S3method(get_coef,scam) S3method(get_coef,selection) S3method(get_coef,svyolr) S3method(get_coef,workflow) S3method(get_group_names,bracl) S3method(get_group_names,brmsfit) S3method(get_group_names,clm) S3method(get_group_names,default) S3method(get_group_names,hurdle) S3method(get_group_names,mblogit) S3method(get_group_names,mlm) S3method(get_group_names,multinom) S3method(get_group_names,polr) S3method(get_group_names,svyolr) S3method(get_mean_or_mode,character) S3method(get_mean_or_mode,data.frame) S3method(get_mean_or_mode,default) S3method(get_mean_or_mode,factor) S3method(get_mean_or_mode,logical) S3method(get_model_matrix,default) S3method(get_predict,Learner) S3method(get_predict,MCMCglmm) S3method(get_predict,afex_aov) S3method(get_predict,bart) S3method(get_predict,betareg) S3method(get_predict,bife) S3method(get_predict,biglm) S3method(get_predict,brmsfit) S3method(get_predict,brmultinom) S3method(get_predict,clm) S3method(get_predict,coxph) S3method(get_predict,crch) S3method(get_predict,default) S3method(get_predict,fixest) S3method(get_predict,flexsurvreg) S3method(get_predict,gamlss) S3method(get_predict,glimML) S3method(get_predict,glm) S3method(get_predict,glmmPQL) S3method(get_predict,glmmTMB) S3method(get_predict,hxlr) S3method(get_predict,lm) S3method(get_predict,lmerMod) S3method(get_predict,lmerModLmerTest) S3method(get_predict,lrm) S3method(get_predict,mblogit) S3method(get_predict,merMod) S3method(get_predict,mhurdle) S3method(get_predict,mlogit) S3method(get_predict,model_fit) S3method(get_predict,multinom) S3method(get_predict,ols) S3method(get_predict,orm) S3method(get_predict,polr) S3method(get_predict,rlmerMod) S3method(get_predict,rms) S3method(get_predict,rq) S3method(get_predict,stanreg) S3method(get_predict,svyglm) S3method(get_predict,svyolr) S3method(get_predict,tobit1) S3method(get_predict,workflow) S3method(get_vcov,Learner) S3method(get_vcov,MCMCglmm) S3method(get_vcov,afex_aov) S3method(get_vcov,bart) S3method(get_vcov,biglm) S3method(get_vcov,brmsfit) S3method(get_vcov,default) S3method(get_vcov,gamlss) S3method(get_vcov,glimML) S3method(get_vcov,glmmTMB) S3method(get_vcov,mhurdle) S3method(get_vcov,model_fit) S3method(get_vcov,orm) S3method(get_vcov,scam) S3method(get_vcov,workflow) S3method(glance,comparisons) S3method(glance,hypotheses) S3method(glance,marginaleffects_mids) S3method(glance,marginalmeans) S3method(glance,predictions) S3method(glance,slopes) S3method(knitr::knit_print,comparisons) S3method(knitr::knit_print,hypotheses) S3method(knitr::knit_print,marginaleffects) S3method(knitr::knit_print,predictions) S3method(knitr::knit_print,slopes) S3method(plot,comparisons) S3method(plot,predictions) S3method(plot,slopes) S3method(print,comparisons) S3method(print,hypotheses) S3method(print,marginaleffects) S3method(print,predictions) S3method(print,slopes) S3method(sanitize_model_specific,bart) S3method(sanitize_model_specific,brmsfit) S3method(sanitize_model_specific,mblogit) S3method(sanitize_model_specific,merMod) S3method(sanitize_model_specific,svyglm) S3method(sanitize_model_specific,svyolr) S3method(set_coef,afex_aov) S3method(set_coef,betareg) S3method(set_coef,crch) S3method(set_coef,data.frame) S3method(set_coef,default) S3method(set_coef,flexsurvreg) S3method(set_coef,gamlss) S3method(set_coef,glimML) S3method(set_coef,glm) S3method(set_coef,glmmPQL) S3method(set_coef,glmmTMB) S3method(set_coef,glmx) S3method(set_coef,hetprob) S3method(set_coef,hurdle) S3method(set_coef,hxlr) S3method(set_coef,ivpml) S3method(set_coef,lm) S3method(set_coef,lme) S3method(set_coef,lmerMod) S3method(set_coef,lmerModLmerTest) S3method(set_coef,merMod) S3method(set_coef,mlm) S3method(set_coef,model_fit) S3method(set_coef,multinom) S3method(set_coef,nls) S3method(set_coef,polr) S3method(set_coef,rlmerMod) S3method(set_coef,scam) S3method(set_coef,selection) S3method(set_coef,survreg) S3method(set_coef,svyolr) S3method(set_coef,tobit) S3method(set_coef,workflow) S3method(set_coef,zeroinfl) S3method(tidy,comparisons) S3method(tidy,hypotheses) S3method(tidy,marginaleffects_mids) S3method(tidy,marginalmeans) S3method(tidy,predictions) S3method(tidy,slopes) S3method(vcov,comparisons) S3method(vcov,hypotheses) S3method(vcov,marginalmeans) S3method(vcov,predictions) S3method(vcov,slopes) export(avg_comparisons) export(avg_predictions) export(avg_slopes) export(comparisons) export(datagrid) export(datagridcf) export(deltamethod) export(expect_margins) export(expect_predictions) export(expect_slopes) export(get_coef) export(get_group_names) export(get_model_matrix) export(get_predict) export(get_vcov) export(glance) export(hypotheses) export(inferences) export(marginal_means) export(marginaleffects) export(marginalmeans) export(meffects) export(plot_comparisons) export(plot_predictions) export(plot_slopes) export(posterior_draws) export(posteriordraws) export(predictions) export(set_coef) export(slopes) export(specify_hypothesis) export(tidy) import(data.table) importFrom(Rcpp,evalCpp) importFrom(generics,glance) importFrom(generics,tidy) useDynLib(marginaleffects) marginaleffects/NEWS.md0000644000176200001440000011734614632712433014523 0ustar liggesusers# News ## 0.21.0 New: * `hypothesis` accepts formulas like: `ratio ~ sequential | group` * Allow reverse binary contrasts: comparisons(mod, variables = list(am = 1:0, vs = 0:1)). Thanks to K. Henry for report #1137. * `options(marginaleffects_safe = FALSE)` disables some safety checks and allows unadvisable (but potentially) useful features like *many* pairwise comparisons. Thanks to D.Locke for the feature request. * `newdata="balanced"` is a shortcut to produce estimates at combinations of all categorical predictors, holding numeric predictors at their means. Equivalent to `datagrid(grid_type="balanced")` Misc: * Deprecation warning for `specify_hypothesis()`. This function was clearly marked as experimental, and has been available only for one release. It was a bad idea. Users should supply a custom function or a formula to the `hypothesis` argument. The new formula interface, in particular, makes it very easy to conduct group-wise hypothesis tests. Bugs: * Fix regression in `mlogit` models due to factor conversion. This raised an error before returning result, so there is no numerical danger. * `survey` package models work when `row.names(model)` is not coercible to integers. Thanks to @ngreifer for report #1131. ## 0.20.1 * The `comparison` argument of the `comparisons()` function is automatically switched to the `avg` version of the function shortcuts when calling `avg_comparisons()` or setting `by=TRUE`. For example, `comparison="ratio"` becomes `comparison="ratioavg"` when calling `avg_comparisons()`. Note that `comparison="ratioavg"` is equivalent to: `comparison = \(hi,lo) mean(hi)/mean(lo)` * Fixed a bug ("non-conformable arguments") when using `survreg` objects from `survival::survreg()`. * Fixed bug in `inferences()` for GLM models and `type="invlink"`, where the wrong scale would be reported for confidence intervals. ## 0.20.0 Breaking changes: * The order of the `group` column is preserved when the original variable is a factor. This may change the order of output, which could have an effect on hypothesis tests using the `hypothesis="b1=b3"` syntax. New: * New *experimental* function: `specify_hypothesis()` returns functions to be used in the `hypothesis` argument of `predictions()`, `comparisons()`, and `slopes()`. This convenience function can be used to specify complex aggregations and estimands for hypothesis tests (ex: by subgroups or with custom functions). * `hypothesis` argument accepts "meandev" and "meanotherdev" to compute deviations from the mean estimate. * Do not raise extraneous warning for `survey` package models when the `by` argument is not used. * Informative error when `hypotheses()` is called twice on the same object. * `print("tinytable")` adds footnotes to the table with columns and type information. Bugs: * `mlogit` `predict()` method does not play well with `data.table`. Thanks to @andrewheiss for report #1086. * Avoid merging `newdata` in `predictions()` when `hypothesis` can change the meaning of rows. Avoid Issue #1105 reported by @strengejacke. * `inferences()` did not work with the `transform` argument. Thanks to Demetri Pananos for report #1115. ## 0.19.0 Breaking changes: * `datagrid()` no longer includes the response variable by default when it is not explicitly specified by the user. Use the new `response` argument to include it. * `datagrid(grid_type="balanced")` returns factors ordered by level rather than by order of appearance in the original data. * Order of some rows in the output may differ when using `datagrid()`. Necessary to fix issue #1079 (see below). New modeling packages supported: * `flexsurv`: Thanks to @mattwarkentin for code contributions in PR #781. https://cran.r-project.org/web/packages/flexsurv/index.html * `REndo`: https://cran.r-project.org/web/packages/REndo/index.html New: * `wts=TRUE` tries to retrieves weights used in a weighted fit such as `lm()` with the `weights` argument or a model fitted using the `survey` package. Thanks to @ngreifer for feature request * `print.marginaleffects()` supports `style="tinytable"`, which returns a `tinytable` object. Call `print(avg_slopes(model))` to get a nice printed table in Quarto or Rmarkdown documents, via Typst, LaTeX or HTML. Default print format can be set using: `options(marginaleffects_print_style="tinytable")` * `hypothesis` argument accepts a function which takes a `marginaleffects` data frame and returns a transformed data frame with `term` and `estimate` columns. * `datagrid()` gets a `response` argument (default is `FALSE`) to control if the response variable is included or excluded from the grid-building process. * The `base::subset()` and `dplyr::filter()` functions can be called with a single argument in `newdata` to select a subset of rows from the dataset used to fit the model. - Ex: avg_comparisons(fit, variables = "treatment", newdata = subset(treatment == 1))` * Better warning for unsupported arguments. * `df` argument in `hypotheses()` accepts a vector of length 2 to control degrees of freedom in F tests. * `nlme::lme()` objects raise a warning about degrees of freedom. Thanks to and @stefgehrig and @huftis for discussion in Issue #960. Major bugs: * Some results could be mislabelled with factor variables used in combination with `datagrid()` or `condition`. Thanks to @snhansen for report #1079. * `glmmTMB` models now report correct standard errors, and raise a warning that these standard errors only account for uncertainty in fixed effect parameters. Thanks to contributors to Issue #1024 and especially to @bbolker for discussion and solution. Minor bugs: * Uninformative error when a custom `comparison` function returns `NA` in bayesian models. Thanks to @Sandhu-SS for report #1017. * `datagrid()` returns an object with full attributes when `by` is used. Thanks to @Sandhu-SS for report #1058. * `inferences(method="simulation")` with `inferences()` function. Thanks to @davidarmstrong for report #1054. ## 0.18.0 This release represents a major step towards 1.0.0. Some functions are renamed and now raise deprecation warnings. After 1.0.0, the API will become much more stable, and any change will have to be very deliberate with much lead time and deprecation delays. Breaking changes: * `tidy()` no longer takes the average of estimates in the original model object. Users who want an aggregate estimate should call the relevant `avg_*()` function, or use the `by` argument explicitly. The previous behavior led to unexpected behavior and increased code complexity a lot. * `summary()` methods are removed. These have never provided any additional information; they just reprinted the output already available with the standard print method. At least the default `summary()` for data frames (which is now triggered on `marginaleffects` object) provides a different view on the content of the object. * `plot_cco()`, `plot_cme()`, and `plot_cap()` were renamed in version 0.9.0, one year ago. They are now fully removed from the package. New: * `datagrid(grid_type = "balanced")` creates a balanced grid with all unique values of categorical predictors. This can be used with `predictions()` to compute marginal means as in the `emmeans` package. * `mvgam` package support (multivariate generalized additive models) Deprecation warnings: * `deltamethod()` has been named `hypotheses()` for a while. We now issue a deprecation warning and it will be removed eventually. * `datagridcf()` will eventually be deprecated and removed from the package. We will raise a warning for at least one year before removing the function. Identical results can be obtained with `datagrid(..., grid_type="counterfactual")` * `marginal_means()` will eventually be deprecated and removed from the package. We will raise a warning for at least one year before removing the function. Identical results can be obtained using the `predictions()` function and the `grid_type="balanced"` argument of `datagrid()`. Examples are in the marginal means vignette on the website. Minor: * Better warning messages for unsupported matrix columns, enhancing user experience and troubleshooting. * Various improvements to documentation. * Typos * Repository hosts model objects for easier testing. Bug fixes: * Error on `hypotheses(joint = "string")` for `comparisons()` objects (no result was returned). Thanks to @BorgeJorge for report #981. * Enhanced support for multi-equation Bayesian models with `brms` models. Thanks to @winterstat for report #1006. * Parameter names with spaces could break standard errors. Thanks to @Lefty2021 for report #1005. ## 0.17.0 Breaking changes: * The `comparisons()` now uses "forward contrasts" by default for numeric predictors, instead of "centered contrasts". This can lead to small numerical differences in non-linear models. * The `variables` argument of the `comparisons()` function no longer accepts numeric vectors unless they are of length 2, specifying the low and high contrast values. This is to avoid ambiguity between the two vector version. Users should supply a data frame or a function instead. This is nearly as easy, and removes ambiguity. New supported packages: * `dbarts`: https://cran.r-project.org/package=dbarts * `mvgam`: https://nicholasjclark.github.io/mvgam/ Not available on CRAN yet, but this package maintains its own `marginaleffects` support function. * `rms::Gls`: https://cran.r-project.org/package=rms Misc: * `comparisons()`: The `variables` argument now accepts functions and data frames for factor, character, and logical variables. * Deprecation warning for: `plot_cap()`, `plot_cme()`, and `plot_cco()`. These function names will be removed in version 1.0.0. * `options(modelsummary_factory_default=...)` is respected in Quarto and Rmarkdown documents. Bugs: * `wts` argument now respected in `avg_slopes()` for binary variables. Thanks to @trose64 for report #961 * Custom functions in the `comparison` argument of `comparisons()` did not supply the correct `x` vector length for bayesian models when the `by` argument is used. Thanks to @Sandhu-SS for report #931. * Add support for two facet variables (through `facet_grid`) when plotting using `condition` * `comparisons()`: When `variables` is a vector of length two and `newdata` has exactly two columns, there was ambiguity between custom vectors and length two vector of contrasts. Bug reported by C. Rainey on Twitter. * Superfluous warning with `fixest::fenegbin`. ## 0.16.0 Machine learning support: * `tidymodels` package * `mlr3` package Misc: * New vignettes: - Inverse Probability Weighting - Machine Learning - Matching * Add support for `hypotheses()` to `inferences()`. Thanks to @Tristan-Siegfried for code contribution #908. * Support `survival::survreg()`. Thanks to Carlisle Rainey for Report #911. * `column_names` argument in `print.marginaleffects()` to suppress the printed column names at the bottom of the printout. * The function supplied to the `comparison` argument of the `comparisons()` function can now operate on `x` and on `newdata` directly (e.g., to check the number of observations). * More informative errors from `predict()`. Bugs: * Some `gamlss` models generated an error related to the `what` argument. Thanks to @DHLocke for Issue #933 ## 0.15.1 * `hypotheses()`: The `FUN` argument handles `group` columns gracefully. * Native support for `Amelia` for multiple imputation. Documentation: * New section on "Complex aggregations" in the Hypothesis testing vignette. Bug fix: * Results of the `predictions()` function could be inaccurate when (a) running version 0.15.0, (b) `type` is `NULL` or `invlink(link)`, (c) model is `glm()`, and (d) the `hypothesis` argument is non-numeric. Thanks to @strengejacke for report [#903](https://github.com/vincentarelbundock/marginaleffects/issues/903) ## 0.15.0 New: * Conformal prediction via `inferences()` * `hypothesis` argument now accepts multiple string formulas. * The `type` argument now accepts an explicit `invlink(link)` value instead of silently back-transforming. Users are no longer pointed to `type_dictionary`. Instead, they should call their function with a bad `type` value, and they will obtain a list of valid types. The default `type` value is printed in the output. This is useful because the default `type` value is `NULL`, so the user often does not explicitly decide. * Allow install with Rcpp 1.0.0 and greater. Support new models: * `survey::svyolr()` Misc: * `inferences(method="simulation")` uses the original point estimate rather than the mean of the simulation distribution. Issue #851. * Better documentation and error messages for `newdata=NULL` * Some performance improvements for `predictions()` and `marginalmeans()` (#880, #882, @etiennebacher). Bug fix: * `newdata="median"` returned mean of binary variables. Thanks to @jkhanson1970 for report #896. ## 0.14.0 Breaking changes: * Row order of the output changes for some objects. Rows are not sorted alphabetically by `term`, `by`, and variables explicitly supplied to `datagrid`. This can affect hypothesis tests computed using the b1, b2, b3, and other indices. * New procedure `numderiv` argument use a different procedure to select the step size used in the finite difference numeric derivative used to compute standard errors: abs(x) * sqrt(.Machine$double.eps). The numerical results may not be exactly identical to previous versions of `marginaleffects`, but the step size should be adequate in a broader variety of cases. Note that users can use the `numderiv` argument for more control on numeric differentiation, as documented. * `bife` models are no longer supported pending investigation in weird results in the tests. Looking for volunteers write more thorough tests. New: * Support: `logistf` package. * Support: `DCchoice` package. * Support: `stats::nls` * `hypotheses()` can now accept raw data frame, which gives a lot of flexibility for custom contrasts and functions. See the Hypothesis vignette for an example. * `numderiv` argument allows users to use finite difference (center or forward) or Richardson's method to compute the numerical derivatives used in the calculation of standard errors. Bug fixes: * `inferences()` supports the `cross` argument for `comparisons()` objects. Thanks to Kirill Solovev for report #856. * `splines::bs()` in formulas could produce incorrect results due to weirdness in `stats::model.matrix()`. Thanks to @chiungming for report #831. * `mgcv` with `ocat` are now supported. Thanks to Lorenzo Fabbri for Issue #844. * `quantreg` problem with `rowid` merge did not affect estimates but did not return the full original data. Issue #829. * `get_modeldata()` extracts weights variable when available. * `predictions()` is no longer broken in some `inferences()` calls. Issue #853 * Inaccurate averaging with `comparison=differenceavg` some models where all predictors are categorical. Thanks to Karl Ove Hufthammer for report #865. Misc: * Major refactor to simplify the code base and make maintenance easier. ## 0.13.0 Breaking change: * `glmmTMB`: Standard errors are no longer supported because they may have been erroneous. Follow Issue #810 on Github for developments: https://github.com/vincentarelbundock/marginaleffects/issues/810 New: * `hypothesis` argument accepts wildcards: `hypothesis = "b*=b1"` * `s.value` column in all output: Shannon transforms for p values. See Greenland (2019). * `marginal_means` supports `mira` (`mice` objects). * `comparisons()`: The `variables` arguments now accepts arbitrary numeric vectors of length equal to the number of rows in `newdata`. This allows users to specify fully custom treatment sizes. In the documentation examples, we show how to estimate the difference for a 1 standard deviation shift in a regressor, where the standard deviation is calculated on a group-wise basis. * `comparisons()`: the `variables` argument now accepts "revpairwise", "revsequential", "revreference" for factor and character variables. * `comparisons()`: the `comparison` argument now accept "lift" and "liftavg". Performance: * Computing elasticities for linear models is now up to 30% faster (#787, @etiennebacher). Bug fixes: * Better handling of environments when `newdata` is a function call. Thanks to @jcccf for report #814 and to @capnrefsmmat for the proposed fix using the `rlang` package. * Degrees of freedom mismatch for joint hypothesis tests. Thanks to @snhansen for report #789. ## 0.12.0 Breaking change: * Row order of output has changed for many calls, especially those using the `by` argument. This may break hypothesis tests conducted by indexing `b1`, `b2`, etc. This was necessary to fix Issue #776. Thanks to @marcora for the report. New: * `hypotheses()`: Joint hypothesis tests (F and Chi-square) with the `joint` and `joint_test` arguments. * `vcov.hypotheses` method. * `wts` is now available in `plot_predictions()`, `plot_comparisons()`, and `plot_slopes()`. Bug: * Wrong order of rows in bayesian models with `by` argument. Thanks to @shirdekel for report #782. ## 0.11.2 * `vcov()` and `coef()` methods for `marginaleffects` objects. * Strings in `wts` are accepted with the `by` argument. * `predictions()` and `avg_predictions()` no longer use an automatic backtransformation for GLM models unless `hypothesis` is `NULL`. * `vcov()` can be used to retrieve a full variance-covariance matrix from objects produced by `comparisons()`, `slopes()`, `predictions()`, or `marginal_means()` objects. * When processing objects obtained using `mice` multiple imputation, the pooled model using `mice::pool` is attached to the `model` attribute of the output. This means that functions like `modelsummary::modelsummary()` will not erroneously report goodness-of-fit statistics from just a single model and will instead appropriately report the statistics for the pooled model. Thanks to @Tristan-Siegfried for PR #740. * More informative error messages on some prediction problems. Thanks to @andymilne for Report #751. Performance: * `inferences()` is now up to 17x faster and much more memory-efficient when `method` is `"boot"` or `"rsample"` (#770, #771, @etiennebacher). Bugs: * `brms` models with `nl=TRUE` and a single predictor generated an error. Thanks to @Tristan-Siegried for Report #759. * `avg_predictions()`: Incorrect group-wise averaging when all predictors are categorical, the `variables` variable is used, and we are averaging with `avg_` or the `by` argument. Thanks to BorgeJorge for report #766. * Bug when `datagrid()` when called inside a user-written function. Thanks to @NickCH-K for report #769 and to @capnrefsmmat for the diagnostics. ## 0.11.1 Breaking change: * Row orders are now more consistent, but may have changed from previous version. This could affect results from `hypothesis` with `b1`, `b2`, ... indexing. Support new models: * `nlme::lme()` * `phylolm::phylolm()` * `phylolm::phyloglm()` New: * Vignette on 2x2 experimental designs. Thanks to Demetri Pananos. * `comparisons()` accepts data frames with two numeric columns ("low" and "high") to specify fully customizable contrasts. * `datagrid()` gets a new `by` argument to create apply grid-making functions within groups. * `plot_*()` gain a `newdata` argument for use with `by`. Bug: * `comparisons(comparison = "lnratioavg")` ignored `wts` argument. Thanks to Demetri Pananos for report #737. * `ordinal::clm()`: incorrect standard errors when location and scale parameters are the same. Thanks to MrJerryTAO for report #718. * Incorrect label for "2sd" comparisons. Thanks to Andy Milne for report #720. * Invalid factor levels in `datagrid()` means `newdata` argument gets ignored. Thanks to Josh Errickson for report #721. * Error in models with only categorical predictors and the `by` argument. Thanks to Sam Brilleman for report #723. * Elasticities are now supported for `ordinal::clm()` models. Thanks to MrJerryTAO for report #729. * `glmmTMB` models with zero-inflated components are supported. Thanks to @Helsinki-Ronan and @strengejacke for report #734. ## 0.11.0 Breaking changes: * `type` column is replaced by `type` attribute. * `predictions()` only works with officially supported model types (same list as `comparisons()` and `slopes()`). Renamed arguments (backward compatibility is preserved): * `transform_pre` -> `comparison` * `transform_post` -> `transform` New: * `p_adjust` argument: Adjust p-values for multiple comparisons. * `equivalence` argument available everywhere. Performance: * Much faster results in `avg_*()` functions for models with only categorical predictors and many rows of data, using deduplication and weights instead of unit-level estimates. * Faster predictions in `lm()` and `glm()` models using `RcppEigen`. * Bayesian models with many rows. Thanks to Etienne Bacher. #694 * Faster predictions, especially with standard errors and large datasets. Bugs: * Multiple imputation with `mira` objects was not pooling all datasets. Thanks to @Generalized for report #711. * Support for more models with offsets. Thanks to @mariofiorini for report #705. * Error on `predictions()` with `by` and `wts`. Thanks to Noah Greifer for report #695. * `afex`: some models generated errors. Thanks to Daniel Lüdecke for report #696. * `group` column name is always forbidden. Thanks to Daniel Lüdecke for report #697. * Blank graphs in `plot_comparisons()` with a list in `variables`. * `type="link"` produced an error with some categorical `brms` models. Thanks to @shirdekel for report #703. * Error on `predictions(variables = ...)` for `glmmTMB` models. Thanks to Daniel Lüdecke for report #707. * `by` with user-specified function in `comparison` and factor predictor did not aggregate correctly. Thanks to @joaotedde for report #715. * `ordinal::clm`: Support `cum.prob` and `linear.predictor` prediction types. Thanks to @MrJerryTAO for report #717. ## 0.10.0 Performance: * 2-4x faster execution for many calls. Thanks to Etienne Bacher. New models supported: * `MCMCglmm::MCMCglmm` * `Rchoice::hetprob` * `Rchoice::ivpml` * Multiple imputation using `mice` and any package which can return a list of imputed data frames (e.g., `Amelia`, `missRanger`, etc.) Plot improvements: * New `by` argument to display marginal estimates by subgroup. * New `rug` argument to display tick marks in the margins. * New `points` argument in `plot_predictions()` to display a scatter plot. * New `gray` argument to plot in grayscale using line types and shapes instead of color. * The `effect` argument is renamed to `variables` in `plot_slopes()` and `plot_comparisons()`. This improves consistency with the analogous `slopes()` and `comparisons()` functions. * The plotting vignette was re-written. Other: * Support multiple imputation with `mice` `mira` objects. The multiple imputation vignette was rewritten. * The `variables_grid` argument in `marginal_means()` is renamed `newdata`. Backward compatibility is maintained. * `avg_*()` returns an informative error when `vcov` is "satterthwaite" or "kenward-roger" * "satterthwaite" and "kenward-roger" are now supported when `newdata` is not `NULL` * Informative error when `hypothesis` includes a `b#` larger than the available number of estimates. * `avg_predictions(model, variables = "x")` computes average counterfactual predictions by subgroups of `x` * `datagrid()` and `plot_*()` functions are faster in datasets with many extraneous columns. * In `predictions(type = NULL)` with `glm()` and `Gam()` we first make predictions on the link scale and then backtransform them. Setting `type="response"` explicitly makes predictions directly on the response scale without backtransformation. * Standard errors now supported for more `glmmTMB` models. * Use the `numDeriv` package for numeric differentiation in the calculation of delta method standard error. A global option can now be passed to `numDeriv::jacobian`: - `options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))` - `options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-6)))` - `options(marginaleffects_numDeriv = NULL)` * Print: - Print fewer significant digits. - `print.marginaleffects` now prints all columns supplied to `newdata` - Less redundant labels when using `hypothesis` * Many improvements to documentation. Bugfixes: * Standard errors could be inaccurate in models with non-linear components (and interactions) when some of the coefficients were very small. This was related to the step size used for numerical differentiation for the delta method. Issue #684. * `avg_predictions(by =)` did not work when the dataset included a column named `term`. Issue #683. * `brms` models with multivariate outcome collapsed categories in `comparisons()`. Issue #639. * `hypotheses()` now works on lists and in calls to `lapply()`, `purrr::map()`, etc. Issue #660. ## 0.9.0 Breaking changes: * All functions return an `estimate` column instead of the function-specific `predicted`, `comparisons`, `dydx`, etc. This change only affects unit-level estimates, and not average estimates, which already used the `estimate` column name. * The `transform_avg` argument in `tidy()` deprecated. Use `transform_post` instead. * `plot_*(draw=FALSE)` now return the actual variable names supplied to the `condition` argument, rather than the opaque "condition1", "condition2", etc. New models supported: * `blme` package. New features: * New functions: `avg_predictions()`, `avg_comparisons()`, `avg_slopes()` * Equivalence, non-inferiority, and non-superiority tests with the `hypotheses()` function and `equivalence` argument. * New experimental `inferences()` function: simulation-based inferences and bootstrap using the `boot`, `rsample`, and `fwb` package. * New `df` argument to set degrees of freedom manually for p and CI. * Pretty `print()` for all objects. * `by` argument - `TRUE` returns average (marginal) predictions, comparisons, or slopes. - Supports bayesian models. * `hypothesis` argument - Numeric value sets the null used in calculating Z and p. - Example: `comparisons(mod, transform_pre = "ratio", hypothesis = 1)` * All arguments from the main functions are now available through `tidy()`, and `summary()`: `conf_level`, `transform_post`, etc. * Bayesian posterior distribution summaries (median, mean, HDI, quantiles) can be customized using global options. See `?comparisons` Renamed functions (backward-compatibility is maintained by keeping the old function names as aliases): * `marginaleffects()` -> `slopes()` * `posteriordraws()` -> `posterior_draws()` * `marginalmeans()` -> `marginal_means()` * `plot_cap()` -> `plot_predictions()` * `plot_cme()` -> `plot_slopes()` * `plot_cco()` -> `plot_comparisons()` Bug fixes: * Incorrect results: In 0.8.1, `plot_*()` the `threenum` and `minmax` labels did not correspond to the correct numeric values. * Fix corner case for slopes when the dataset includes infinite values. * `mlogit` error with factors. * The `vcov` argument now accepts functions for most models. Other: * Removed major performance bottleneck for `slopes()` ## 0.8.1 * `deltamethod()` can run hypothesis tests on objects produced by the `comparisons()`, `marginaleffects()`, `predictions()`, and `marginalmeans()` functions. This feature relies on `match.call()`, which means it may not always work when used programmatically, inside functions and nested environments. It is generally safer and more efficient to use the `hypothesis` argument. * `plot_cme()` and `plot_cco()` accept lists with user-specified values for the regressors, and can display nice labels for shortcut string-functions like "threenum" or "quartile". * `posterior_draws`: new `shape` argument to return MCMC draws in various formats, including the new `rvar` structure from the `posterior` package. * `transform_avg` function gets printed in `summary()` output. * `transform_post` and `transform_avg` support string shortcuts: "exp" and "ln" * Added support for `mlm` models from `lm()`. Thanks to Noah Greifer. Bug fixes: * `hypothesis` argument with bayesian models and `tidy()` used to raise an error. * Missing values for some regressors in the `comparisons()` output for `brms` models. ## 0.8.0 Breaking change: * The `interaction` argument is deprecated and replaced by the `cross` argument. This is to reduce ambiguity with respect to the `interaction` argument in `emmeans`, which does something completely different, akin to the difference-in-differences illustrated in the Interactions vignette. 71 classes of models supported, including the new: * `rms::ols` * `rms::lrm` * `rms::orm` New features: * Plots: `plot_cme()`, `plot_cap()`, and `plot_cco()` are now much more flexible in specifying the comparisons to display. The `condition` argument accepts lists, functions, and shortcuts for common reference values, such as "minmax", "threenum", etc. * `variables` argument of the `comparisons()` function is more flexible: - Accepts functions to specify custom differences in numeric variables (e.g., forward and backward differencing). - Can specify pairs of factors to compare in the `variables` argument of the `comparisons` function. * `variables` argument of the `predictions()` function is more flexible: - Accepts shortcut strings, functions, and vectors of arbitrary length. * Integrate out random effects in bayesian `brms` models (see Bayesian analysis vignette) New vignettes: * Experiments * Extending marginal effects * Integrating out random effects in bayesian models Bug fixes and minor improvements: * The default value of `conf_level` in `summary()` and `tidy()` is now `NULL`, which inherits the `conf_level` value in the original `comparisons`/`marginaleffects`/`predictions` calls. * Fix typo in function names for missing "lnratioavgwts" * Interactions with `fixest::i()` are parsed properly as categorical variables * For `betareg` objects, inference can now be done on all coefficients using `deltamethod()`. previously only the location coefficients were available. * For objects from `crch` package, a number of bugs have been fixed; standard errors should now be correct for `deltamethod()`, `marginaleffects()`, etc. * Fixed a bug in the `tidy()` function for `glmmTMB` models without random effects, which caused all t statistics to be identical. ## 0.7.1 * New supported model class: `gamlss`. Thanks to Marcio Augusto Diniz. * `marginalmeans()` accepts a `wts` argument with values: "equal", "proportional", "cells". * `by` argument - accepts data frames for complex groupings. - in `marginalmeans` only accepts data frames. - accepts "group" to group by response level. - works with bayesian models. * `byfun` argument for the `predictions()` function to aggregate using different functions. * `hypothesis` argument - The matrix column names are used as labels for hypothesis tests. - Better labels with "sequential", "reference", "pairwise". - new shortcuts "revpairwise", "revsequential", "revreference" * `wts` argument is respected in `by` argument and with `*avg` shortcuts in the `transform_pre` argument. * `tidy.predictions()` and `tidy.marginalmeans()` get a new `transform_avg` argument. * New vignettes: - Unit-level contrasts in logistic regressions. Thanks to @arthur-albuquerque. - Python Numpy models in `marginaleffects`. Thanks to timpipeseek. - Bootstrap example in standard errors vignette. ## 0.7.0 Breaking changes: * `by` is deprecated in `summary()` and `tidy()`. Use the same `by` argument in the main functions instead: `comparisons()`, `marginaleffects()`, `predictions()` * Character vectors are no longer supported in the `variables` argument of the `predictions()` function. Use `newdata="fivenum"` or "grid", "mean", or "median" instead. Critical bug fix: * Contrasts with interactions were incorrect in version 0.6.0. The error should have been obvious to most analysts in most cases (weird-looking alignment). Thanks to @vmikk. New supported packages and models: * `survival::clogit` * `biglm`: The main quantities can be computed, but not the delta method standard errors. See https://github.com/vincentarelbundock/marginaleffects/issues/387 New vignette: * Elasticity * Frequently Asked Questions New features: * Elasticity and semi-elasticity using the new `slope` argument in `marginaleffects()`: eyex, dyex, eydx * `datagrid()` accepts functions: `datagrid(newdata = mtcars, hp = range, mpg = fivenum, wt = sd)` * New `datagridcf()` function to create counterfactual datasets. This is a shortcut to the `datagrid()` function with default to `grid_type = "counterfactual"` * New `by` arguments in `predictions()`, `comparisons()`, `marginaleffects()` * New `newdata` shortcuts: "tukey", "grid" * New string shortcuts for `transform_pre` in `comparisons()` * `marginalmeans()` now back transforms confidence intervals when possible. * `vcov` argument string shortcuts are now case-insensitive * The default contrast in `comparisons()` for binary predictors is now a difference between 1 and 0, rather than +1 relative to baseline. * documentation improvements ## 0.6.0 New supported packages and models: * `tidymodels` objects of class `tidy_model` are supported if the fit engine is supported by `marginaleffects`. New function: * `deltamethod()`: Hypothesis tests on functions of parameters * `plot_cco()`: Plot conditional contrasts New arguments: * `hypothesis` for hypothesis tests and custom contrasts * `transform_post` in `predictions()` * `wts` argument in `predictions()` only affects average predictions in `tidy()` or `summary()`. New or improved vignettes: * Hypothesis Tests and Custom Contrasts using the Delta Method: https://marginaleffects.com/vignettes/hypothesis.html * Multiple Imputation: https://marginaleffects.com/vignettes/multiple_imputation.html * Causal Inference with the g-Formula: https://marginaleffects.com/vignettes/gcomputation.html (Thanks to Rohan Kapre for the idea) Deprecated or renamed arguments: * `contrast_factor` and `contrast_numeric` arguments are deprecated in `comparisons()`. Use a named list in the `variables` argument instead. Backward compatibility is maintained. * The `transform_post` argument in `tidy()` and `summary()` is renamed to `transform_avg` to disambiguate against the argument of the same name in `comparisons()`. Backward compatibility is preserved. Misc: * `tidy.predictions()` computes standard errors using the delta method for average predictions * Support `gam` models with matrix columns. * `eps` in `marginaleffects()` is now "adaptive" by default: it equals 0.0001 multiplied the range of the predictor variable * `comparisons()` now supports "log of marginal odds ratio" in the `transform_pre` argument. Thanks to Noah Greifer. * New `transform_pre` shortcuts: dydx, expdydx * `tidy.predictions()` computes standard errors and confidence intervals for linear models or GLM on the link scale. ## 0.5.0 Breaking changes: * `type` no longer accepts a character vector. Must be a single string. * `conf.int` argument deprecated. Use `vcov = FALSE` instead. New supported packages and models: * `mlogit` * `mhurdle` * `tobit1` * `glmmTMB` New features: * `interaction` argument in `comparisons()` to compute interactions between contrasts (cross-contrasts). * `by` argument in `tidy()` and `summary()` computes group-average marginal effects and comparisons. * `transform_pre` argument can define custom contrasts between adjusted predictions (e.g., log adjusted risk ratios). Available in `comparisons()`. * `transform_post` argument allows back transformation before returning the final results. Available in `comparisons()`, `marginalmeans()`, `summary()`, `tidy()`. * The `variables` argument of the `comparisons()` function accepts a named list to specify variable-specific contrast types. * Robust standard errors with the `vcov` argument. This requires version 0.17.1 of the `insight` package. - `sandwich` package shortcuts: `vcov = "HC3"`, `"HC2"`, `"NeweyWest"`, and more. - Mixed effects models: `vcov = "satterthwaite"` or `"kenward-roger"` - One-sided formula to clusters: `vcov = ~cluster_variable` - Variance-covariance matrix - Function which returns a named squared matrix * `marginalmeans()` allows interactions * Bayesian Model Averaging for `brms` models using `type = "average"`. See vignette on the `marginaleffects` website. * `eps` argument for step size of numerical derivative * `marginaleffects` and `comparisons` now report confidence intervals by default. * New dependency on the `data.table` package yields substantial performance improvements. * More informative error messages and warnings * Bug fixes and performance improvements New pages on the `marginaleffects` website: https://marginaleffects.com/ * Alternative software packages * Robust standard errors (and more) * Performance tips * Tables and plots * Multinomial Logit and Discrete Choice Models * Generalized Additive Models * Mixed effects models (Bayesian and Frequentist) * Transformations and Custom Contrasts: Adjusted Risk Ratio Example Argument name changes (backward compatibility is preserved: * Everywhere: - `conf.level` -> `conf_level` * `datagrid()`: - `FUN.factor` -> `FUN_factor` (same for related arguments) - `grid.type` -> `grid_type` ## 0.4.1 New supported packages and models: * `stats::loess` * `sampleSelection::selection` * `sampleSelection::heckit` Misc: * `mgcv::bam` models allow `exclude` argument. * Gam models allow `include_smooth` argument. * New tests * Bug fixes ## 0.4.0 New function: * `comparisons()` computes contrasts Misc: * Speed optimizations * `predictions()` and `plot_cap()` include confidence intervals for linear models * More robust handling of in-formula functions: factor(), strata(), mo() * Do not overwrite user's `ggplot2::theme_set()` call ## 0.3.4 * Bug fixes ## 0.3.3 New supported models: * `mclogit::mclogit` * `robust::lmRob` * `robustlmm::rlmer` * `fixest` confidence intervals in `predictions` Misc: * Support `modelbased::visualisation_matrix` in `newdata` without having to specify `x` explicitly. * `tidy.predictions()` and `summary.predictions()` methods. * Documentation improvements. * CRAN test fixes ## 0.3.2 Support for new models and packages: * `brglm2::bracl` * `mclogit::mblogit` * `scam::scam` * `lmerTest::lmer` Misc: * Drop `numDeriv` dependency, but make it available via a global option: options("marginaleffects_numDeriv" = list(method = "Richardson", method.args = list(eps = 1e-5, d = 0.0001))) * Bugfixes * Documentation improvements * CRAN tests ## 0.3.1 documentation bugfix ## 0.3.0 Breaking changes: * `predictions` returns predictions for every observation in the original dataset instead of `newdata=datagrid()`. * `marginalmeans` objects have new column names, as do the corresponding `tidy` and `summary` outputs. New supported packages and models: * `brms::brm` * `rstanarm::stanglm` * `brglm2::brmultinom` * `MASS::glmmPQL` * `aod::betabin` Misc: * `datagrid` function supersedes `typical` and `counterfactual` with the `grid.type` argument. The `typical` and `counterfactual` functions will remain available and exported, but their use is not encouraged. * `posterior_draws` function can be applied to a `predictions` or a `marginaleffects` object to extract draws from the posterior distribution. * `marginalmeans` standard errors are now computed using the delta method. * `predictions` standard errors are now computed using the delta method when they are not available from `insight::get_predicted`. * New vignette on Bayesian models with `brms` * New vignette on Mixed effects models with `lme4` * If the `data.table` package is installed, `marginaleffects` will automatically use it to speed things up. * Contrast definition reported in a separate column of `marginaleffects` output. * Safer handling of the `type` argument. * Comprehensive list of supported and tests models on the website. * Many bug fixes * Many new tests, including several against `emmeans` ## 0.2.0 Breaking change: * `data` argument becomes `newdata` in all functions. New supported packages and models: * `lme4:glmer.nb` * `mgcv::gam` * `ordinal::clm` * `mgcv` `marginalmeans`: * New `variables_grid` argument `predictions`: * Support `mgcv` `plot_cap` * New `type` argument Misc: * New validity checks and tests ## 0.1.0 First release. Bravo! Thanks to Marco Avina Mendoza, Resul Umit, and all those who offered comments and suggestions. marginaleffects/inst/0000755000176200001440000000000014632712540014365 5ustar liggesusersmarginaleffects/inst/CITATION0000644000176200001440000000116514631452335015527 0ustar liggesusers bibentry(bibtype = "Article", title = "How to Interpret Statistical Models Using {marginaleffects} in {R} and {Python}", author = c( person(given = "Vincent", family = "Arel-Bundock", email = "vincent.arel-bundock@umontreal.ca"), person(given = "Noah", family = "Greifer", email = "ngreifer@iq.harvard.edu"), person(given = "Andrew", family = "Heiss", email = "aheiss@gsu.edu") ), year = "Forthcoming", journal = "Journal of Statistical Software", header = "To cite marginaleffects in publications use:" ) marginaleffects/inst/COPYRIGHTS0000644000176200001440000000354414631452335016013 0ustar liggesusersCopyright (c) 2022, Vincent Arel-Bundock ######################## Functions in `R/methods_gamlss.R` file were adatped from the `gamlss` package for `R`. Copyright (c) 2022, Mikis Stasinopoulos [aut, cre, cph] shared under the GPL3 License. ######################## Functions in the `R/get_hdi.R` file were extracted from the `HDInterval` package for `R`. Copyright (c) 2020, Mike Meredith and John Kruschke, shared under the GPL3 License. ######################## Some of the methods in the `R/mean_or_mode.R` file were adapted from the `prediction` R package, copyright Thomas J. Leeper, shared under the MIT License (copied below). Tests in the `tests/testhat/test-analytical.R` were adapted from the `margins` R package, copyright Thomas J. Leeper (2018), shared under the MIT License (copied below). Copyright (c) 2016-2018, Thomas J. Leeper. MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. marginaleffects/inst/WORDLIST0000644000176200001440000000426414632712433015566 0ustar liggesusersAAP AAPs AFAICT AME APM ARD Aaditya Adelie Adelies Anastasios Angelopoulos Arel Augusto Avina BH Beaumais Boca BorgeJorge Brilleman Bundock CMD CRC Candes Carlisle Codecov Customizable Demetri Diniz Dowd DxP EMMs ETI Ep Epidemiologic Errickson Estimand Estimands Fabbri Foygel GLMMs Gam Gentoos Greifer HC HDI Hedeker Heiss Hernán Heteroskedasticity Hufthammer IPW Isager J'VJ JM JVJ Kapre Kirill Krinsky Kurz LLC Lakens Leeper Lenth Lifecycle Lüdecke MEM MER MFX MRP Marcio MrJerryTAO NHEFS Nafa NumPyro Numpy ORs Ove Pananos PxD RCTs RDatasets Rainey Ramdas Randas Raton Rcpp Rdatasets Rdatatable Resul Rina Rmarkdown Robb Rohan Scheel Solovev Stata Stata's StataCorp Stata’s TLDR TOST Tibshirani Tomz Typst Umit Wittenberg XGBoost Xu aje al arXiv arXivhttp args arxiv backtransform backtransformation backtransforming backtransforms bayesian bc bca bonferroni brms bugfix cdot chisq cme coercible confounders cran cumhaz cumprob customclass customizable cv dX dY datagrid deduplication demeaned dev differenceavg differenceavgwts differencing disp doi dydx dydxavg dydxavgwts dyex dyexavg dyexavglnratioavgwts dyexavgwts elasticities emmeans eps estimand estimands et ev expdydx expdydxavg expdydxavgwts exponentiate exponentiating expvalue eydx eydxavg eydxavgwts eyex eyexavg eyexavgwts fdcenter fdforward fdr fivenum frac fwb geoms get_hdi ggeffects github grayscale grey heteroskedasticity hochberg holm hommel http https incrementing interpretability interpretable inv invlink io iqr issuecomment jacobian jacobians jacobiansmbox kenward knitr kwaa lift liftavg linpred listwise lm lme ln lnor lnoravg lnoravgwts lnratio lnratioavg lnratioavgwts lp marginalmeans mbox meandev meanotherdev methodologists minmax mislabelled modelbased nd newdata numDeriv pairwise param perc plasminogen poisson ppd pre preprint probs programmatically quartile ratioavg ratioavgwts regressand regressors’ reimplement revpairwise revreference revsequential richardson rmst rowid rsample rvar satterthwaite sd semilog sensical sequential softmax streptokinase tPA th threenum tidiers timpipeseek tinytable tukey typst unadvisable underspecified unintuitive untransformed varepsilon vcovHC vincentarelbundock wmean xb zlink zprob ’s marginaleffects/README.md0000644000176200001440000001374314632712433014700 0ustar liggesusers

How to interpret statistical models in R and Python




The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes non-linear components, interactions, or transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to understand, such as predictions, contrasts, risk differences, ratios, odds, lift, slopes, and so on. Unfortunately, computing these quantities—along with associated standard errors—can be a tedious and error-prone task. This problem is compounded by the fact that modeling packages in `R` and `Python` produce objects with varied structures, which hold different information. This means that end-users often have to write customized code to interpret the estimates obtained by fitting Linear, GLM, GAM, Bayesian, Mixed Effects, and other model types. This can lead to wasted effort, confusion, and mistakes, and it can hinder the implementation of best practices. ## Book [This free online book](https://marginaleffects.com/) introduces a conceptual framework to clearly define statistical quantities of interest, and shows how to estimate those quantities using the `marginaleffects` package for `R` and `Python`. The techniques introduced herein can enhance the interpretability of [over 100 classes of statistical and machine learning models](https://marginaleffects.com/vignettes/supported_models.html), including linear, GLM, GAM, mixed-effects, bayesian, categorical outcomes, XGBoost, and more. With a single unified interface, users can compute and plot many estimands, including: - Predictions (aka fitted values or adjusted predictions) - Comparisons such as contrasts, risk differences, risk ratios, odds, etc. - Slopes (aka marginal effects or partial derivatives) - Marginal means - Linear and non-linear hypothesis tests - Equivalence tests - Uncertainty estimates using the delta method, bootstrapping, simulation, or conformal inference. - Much more! [The Marginal Effects Zoo](https://marginaleffects.com/) book includes over 30 chapters of tutorials, case studies, and technical notes. It covers a wide range of topics, including how the `marginaleffects` package can facilitate the analysis of: - Experiments - Observational data - Causal inference with G-Computation - Machine learning models - Bayesian modeling - Multilevel regression with post-stratification (MRP) - Missing data - Matching - Inverse probability weighting - Conformal prediction [Get started by clicking here!](https://marginaleffects.com/vignettes/get_started.html) ## Article Our article on `marginaleffects` is provisionally accepted for publication by the *Journal of Statistical Software*. You can read [the preprint here.](https://marginaleffects.com/files/marginaleffects_arel-bundock_greifer_heiss_jss5115.pdf) To cite `marginaleffects` in publications please use: Arel-Bundock V, Greifer N, Heiss A (Forthcoming). “How to Interpret Statistical Models Using marginaleffects in R and Python.” *Journal of Statistical Software*. A BibTeX entry for LaTeX users is: ``` {latex} @Article{, title = {How to Interpret Statistical Models Using {marginaleffects} in {R} and {Python}}, author = {Vincent Arel-Bundock and Noah Greifer and Andrew Heiss}, year = {Forthcoming}, journal = {Journal of Statistical Software}, } ``` ## Software The `marginaleffects` package for `R` and `Python` offers a single point of entry to easily interpret the results of [over 100 classes of models,](https://marginaleffects.com/vignettes/supported_models.html) using a simple and consistent user interface. Its benefits include: - *Powerful:* It can compute and plot predictions; comparisons (contrasts, risk ratios, etc.); slopes; and conduct hypothesis and equivalence tests for over 100 different classes of models in `R`. - *Simple:* All functions share a simple and unified interface. - *Documented*: Each function is thoroughly documented with abundant examples. The Marginal Effects Zoo website includes 20,000+ words of vignettes and case studies. - *Efficient:* [Some operations](https://marginaleffects.com/vignettes/performance.html) can be up to 1000 times faster and use 30 times less memory than with the `margins` package. - *Valid:* When possible, [numerical results are checked](https://marginaleffects.com/vignettes/supported_models.html) against alternative software like `Stata` or other `R` packages. - *Thin:* The `R` package requires relatively few dependencies. - *Standards-compliant:* `marginaleffects` follows “tidy” principles and returns simple data frames that work with all standard `R` functions. The outputs are easy to program with and feed to other packages like [`ggplot2`](https://marginaleffects.com/vignettes/plot.html) or [`modelsummary`.](https://marginaleffects.com/vignettes/tables.html) - *Extensible:* Adding support for new models is very easy, often requiring less than 10 lines of new code. Please submit [feature requests on Github.](https://github.com/vincentarelbundock/marginaleffects/issues) - *Active development*: Bugs are fixed promptly. marginaleffects/man/0000755000176200001440000000000014631452336014166 5ustar liggesusersmarginaleffects/man/datagrid.Rd0000644000176200001440000001262314631452336016240 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datagrid.R \name{datagrid} \alias{datagrid} \title{Data grids} \usage{ datagrid( ..., model = NULL, newdata = NULL, by = NULL, grid_type = "mean_or_mode", response = FALSE, FUN_character = NULL, FUN_factor = NULL, FUN_logical = NULL, FUN_numeric = NULL, FUN_integer = NULL, FUN_binary = NULL, FUN_other = NULL ) } \arguments{ \item{...}{named arguments with vectors of values or functions for user-specified variables. \itemize{ \item Functions are applied to the variable in the \code{model} dataset or \code{newdata}, and must return a vector of the appropriate type. \item Character vectors are automatically transformed to factors if necessary. +The output will include all combinations of these variables (see Examples below.) }} \item{model}{Model object} \item{newdata}{data.frame (one and only one of the \code{model} and \code{newdata} arguments can be used.)} \item{by}{character vector with grouping variables within which \verb{FUN_*} functions are applied to create "sub-grids" with unspecified variables.} \item{grid_type}{character. Determines the functions to apply to each variable. The defaults can be overridden by defining individual variables explicitly in \code{...}, or by supplying a function to one of the \verb{FUN_*} arguments. \itemize{ \item "mean_or_mode": Character, factor, logical, and binary variables are set to their modes. Numeric, integer, and other variables are set to their means. \item "balanced": Each unique level of character, factor, logical, and binary variables are preserved. Numeric, integer, and other variables are set to their means. Warning: When there are many variables and many levels per variable, a balanced grid can be very large. In those cases, it is better to use \code{grid_type="mean_or_mode"} and to specify the unique levels of a subset of named variables explicitly. \item "counterfactual": the entire dataset is duplicated for each combination of the variable values specified in \code{...}. Variables not explicitly supplied to \code{datagrid()} are set to their observed values in the original dataset. }} \item{response}{Logical should the response variable be included in the grid, even if it is not specified explicitly.} \item{FUN_character}{the function to be applied to character variables.} \item{FUN_factor}{the function to be applied to factor variables. This only applies if the variable in the original data is a factor. For variables converted to factor in a model-fitting formula, for example, \code{FUN_character} is used.} \item{FUN_logical}{the function to be applied to logical variables.} \item{FUN_numeric}{the function to be applied to numeric variables.} \item{FUN_integer}{the function to be applied to integer variables.} \item{FUN_binary}{the function to be applied to binary variables.} \item{FUN_other}{the function to be applied to other variable types.} } \value{ A \code{data.frame} in which each row corresponds to one combination of the named predictors supplied by the user via the \code{...} dots. Variables which are not explicitly defined are held at their mean or mode. } \description{ Generate a data grid of user-specified values for use in the \code{newdata} argument of the \code{predictions()}, \code{comparisons()}, and \code{slopes()} functions. This is useful to define where in the predictor space we want to evaluate the quantities of interest. Ex: the predicted outcome or slope for a 37 year old college graduate. } \details{ If \code{datagrid} is used in a \code{predictions()}, \code{comparisons()}, or \code{slopes()} call as the \code{newdata} argument, the model is automatically inserted in the \code{model} argument of \code{datagrid()} call, and users do not need to specify either the \code{model} or \code{newdata} arguments. The same behavior will occur when the value supplied to \verb{newdata=} is a function call which starts with "datagrid". This is intended to allow users to create convenience shortcuts like: \preformatted{ library(marginaleffects) mod <- lm(mpg ~ am + vs + factor(cyl) + hp, mtcars) datagrid_bal <- function(...) datagrid(..., grid_type = "balanced") predictions(model, newdata = datagrid_bal(cyl = 4)) } If users supply a model, the data used to fit that model is retrieved using the \code{insight::get_data} function. } \examples{ # The output only has 2 rows, and all the variables except `hp` are at their # mean or mode. datagrid(newdata = mtcars, hp = c(100, 110)) # We get the same result by feeding a model instead of a data.frame mod <- lm(mpg ~ hp, mtcars) datagrid(model = mod, hp = c(100, 110)) # Use in `marginaleffects` to compute "Typical Marginal Effects". When used # in `slopes()` or `predictions()` we do not need to specify the #`model` or `newdata` arguments. slopes(mod, newdata = datagrid(hp = c(100, 110))) # datagrid accepts functions datagrid(hp = range, cyl = unique, newdata = mtcars) comparisons(mod, newdata = datagrid(hp = fivenum)) # The full dataset is duplicated with each observation given counterfactual # values of 100 and 110 for the `hp` variable. The original `mtcars` includes # 32 rows, so the resulting dataset includes 64 rows. dg <- datagrid(newdata = mtcars, hp = c(100, 110), grid_type = "counterfactual") nrow(dg) # We get the same result by feeding a model instead of a data.frame mod <- lm(mpg ~ hp, mtcars) dg <- datagrid(model = mod, hp = c(100, 110), grid_type = "counterfactual") nrow(dg) } marginaleffects/man/get_varcov_args.Rd0000644000176200001440000000063614631452336017635 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_vcov.R \name{get_varcov_args} \alias{get_varcov_args} \title{Take a \code{summary()} style \code{vcov} argument and convert it to \code{insight::get_varcov()}} \usage{ get_varcov_args(model, vcov) } \description{ Take a \code{summary()} style \code{vcov} argument and convert it to \code{insight::get_varcov()} } \keyword{internal} marginaleffects/man/plot_predictions.Rd0000644000176200001440000002351114631452336020040 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_predictions.R \name{plot_predictions} \alias{plot_predictions} \title{Plot Conditional or Marginal Predictions} \usage{ plot_predictions( model, condition = NULL, by = NULL, newdata = NULL, type = NULL, vcov = NULL, conf_level = 0.95, wts = FALSE, transform = NULL, points = 0, rug = FALSE, gray = FALSE, draw = TRUE, ... ) } \arguments{ \item{model}{Model object} \item{condition}{Conditional predictions \itemize{ \item Character vector (max length 4): Names of the predictors to display. \item Named list (max length 4): List names correspond to predictors. List elements can be: \itemize{ \item Numeric vector \item Function which returns a numeric vector or a set of unique categorical values \item Shortcut strings for common reference values: "minmax", "quartile", "threenum" } \item 1: x-axis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid). \item Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers \code{?stats::fivenum} }} \item{by}{Marginal predictions \itemize{ \item Character vector (max length 3): Names of the categorical predictors to marginalize across. \item 1: x-axis. 2: color. 3: facets. }} \item{newdata}{When \code{newdata} is \code{NULL}, the grid is determined by the \code{condition} argument. When \code{newdata} is not \code{NULL}, the argument behaves in the same way as in the \code{predictions()} function.} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{transform}{A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries.} \item{points}{Number between 0 and 1 which controls the transparency of raw data points. 0 (default) does not display any points.} \item{rug}{TRUE displays tick marks on the axes to mark the distribution of raw data.} \item{gray}{FALSE grayscale or color plot} \item{draw}{\code{TRUE} returns a \code{ggplot2} plot. \code{FALSE} returns a \code{data.frame} of the underlying data.} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{ggplot2} object or data frame (if \code{draw=FALSE}) } \description{ Plot predictions on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets). The \code{by} argument is used to plot marginal predictions, that is, predictions made on the original data, but averaged by subgroups. This is analogous to using the \code{by} argument in the \code{predictions()} function. The \code{condition} argument is used to plot conditional predictions, that is, predictions made on a user-specified grid. This is analogous to using the \code{newdata} argument and \code{datagrid()} function in a \code{predictions()} call. All variables whose values are not specified explicitly are treated as usual by \code{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 \code{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: \itemize{ \item https://marginaleffects.com/vignettes/plot.html \item https://marginaleffects.com } } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \section{Prediction types}{ The \code{type} argument determines the scale of the predictions used to compute quantities of interest with functions from the \code{marginaleffects} package. Admissible values for \code{type} depend on the model object. When users specify an incorrect value for \code{type}, \code{marginaleffects} will raise an informative error with a list of valid \code{type} values for the specific model object. The first entry in the list in that error message is the default type. The \code{invlink(link)} is a special type defined by \code{marginaleffects}. It is available for some (but not all) models and functions. With this link type, we first compute predictions on the link scale, then we use the inverse link function to backtransform the predictions to the response scale. This is useful for models with non-linear link functions as it can ensure that confidence intervals stay within desirable bounds, ex: 0 to 1 for a logit model. Note that an average of estimates with \code{type="invlink(link)"} will not always be equivalent to the average of estimates with \code{type="response"}. Some of the most common \code{type} values are: response, link, E, Ep, average, class, conditional, count, cum.prob, cumhaz, cumprob, density, detection, disp, ev, expected, expvalue, fitted, hazard, invlink(link), latent, latent_N, linear, linear.predictor, linpred, location, lp, mean, numeric, p, ppd, pr, precision, prediction, prob, probability, probs, quantile, risk, rmst, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob } \examples{ mod <- lm(mpg ~ hp + wt, data = mtcars) plot_predictions(mod, condition = "wt") mod <- lm(mpg ~ hp * wt * am, data = mtcars) plot_predictions(mod, condition = c("hp", "wt")) plot_predictions(mod, condition = list("hp", wt = "threenum")) plot_predictions(mod, condition = list("hp", wt = range)) } marginaleffects/man/get_predict.Rd0000644000176200001440000002111014632674174016750 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_predict.R, R/methods_MASS.R, % R/methods_MCMCglmm.R, R/methods_afex.R, R/methods_aod.R, % R/methods_betareg.R, R/methods_bife.R, R/methods_biglm.R, R/methods_nnet.R, % R/methods_brglm2.R, R/methods_brms.R, R/methods_crch.R, R/methods_dbarts.R, % R/methods_fixest.R, R/methods_flexsurv.R, R/methods_gamlss.R, % R/methods_glmmTMB.R, R/methods_lme4.R, R/methods_mclogit.R, % R/methods_mhurdle.R, R/methods_mlogit.R, R/methods_mlr3.R, % R/methods_ordinal.R, R/methods_quantreg.R, R/methods_rms.R, % R/methods_robustlmm.R, R/methods_rstanarm.R, R/methods_stats.R, % R/methods_survey.R, R/methods_survival.R, R/methods_tidymodels.R, % R/methods_tobit1.R \name{get_predict} \alias{get_predict} \alias{get_predict.default} \alias{get_predict.polr} \alias{get_predict.glmmPQL} \alias{get_predict.MCMCglmm} \alias{get_predict.afex_aov} \alias{get_predict.glimML} \alias{get_predict.betareg} \alias{get_predict.bife} \alias{get_predict.biglm} \alias{get_predict.multinom} \alias{get_predict.brmultinom} \alias{get_predict.brmsfit} \alias{get_predict.crch} \alias{get_predict.bart} \alias{get_predict.fixest} \alias{get_predict.flexsurvreg} \alias{get_predict.gamlss} \alias{get_predict.glmmTMB} \alias{get_predict.merMod} \alias{get_predict.lmerModLmerTest} \alias{get_predict.lmerMod} \alias{get_predict.mblogit} \alias{get_predict.mhurdle} \alias{get_predict.mlogit} \alias{get_predict.Learner} \alias{get_predict.clm} \alias{get_predict.rq} \alias{get_predict.rms} \alias{get_predict.orm} \alias{get_predict.lrm} \alias{get_predict.ols} \alias{get_predict.rlmerMod} \alias{get_predict.stanreg} \alias{get_predict.lm} \alias{get_predict.glm} \alias{get_predict.svyolr} \alias{get_predict.svyglm} \alias{get_predict.coxph} \alias{get_predict.model_fit} \alias{get_predict.workflow} \alias{get_predict.tobit1} \title{Get predicted values from a model object (internal function)} \usage{ get_predict(model, newdata, type, ...) \method{get_predict}{default}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{polr}(model, newdata = insight::get_data(model), type = "probs", ...) \method{get_predict}{glmmPQL}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{MCMCglmm}(model, newdata, type = "response", ndraws = 1000, ...) \method{get_predict}{afex_aov}(model, newdata = NULL, ...) \method{get_predict}{glimML}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{betareg}(model, newdata, ...) \method{get_predict}{bife}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{biglm}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{multinom}(model, newdata = insight::get_data(model), type = "probs", ...) \method{get_predict}{brmultinom}(model, newdata = insight::get_data(model), type = "probs", ...) \method{get_predict}{brmsfit}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{crch}(model, newdata = NULL, type = "location", ...) \method{get_predict}{bart}(model, newdata = NULL, ...) \method{get_predict}{fixest}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{flexsurvreg}(model, newdata, type, ...) \method{get_predict}{gamlss}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{glmmTMB}( model, newdata = insight::get_data(model), type = "response", newparams = NULL, ... ) \method{get_predict}{merMod}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{lmerModLmerTest}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{lmerMod}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{mblogit}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{mhurdle}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{mlogit}(model, newdata, ...) \method{get_predict}{Learner}(model, newdata, type = NULL, ...) \method{get_predict}{clm}(model, newdata = insight::get_data(model), type = "prob", ...) \method{get_predict}{rq}(model, newdata = insight::get_data(model), type = NULL, ...) \method{get_predict}{rms}(model, newdata = insight::get_data(model), type = NULL, ...) \method{get_predict}{orm}(model, newdata = insight::get_data(model), type = NULL, ...) \method{get_predict}{lrm}(model, newdata = insight::get_data(model), type = NULL, ...) \method{get_predict}{ols}(model, newdata = insight::get_data(model), type = NULL, ...) \method{get_predict}{rlmerMod}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{stanreg}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{lm}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{glm}(model, newdata = insight::get_data(model), type = "response", ...) \method{get_predict}{svyolr}(model, newdata = insight::get_data(model), type = "probs", ...) \method{get_predict}{svyglm}( model, newdata = insight::get_data(model), type = "response", se.fit = FALSE, ... ) \method{get_predict}{coxph}(model, newdata = insight::get_data(model), type = "lp", ...) \method{get_predict}{model_fit}(model, newdata, type = NULL, ...) \method{get_predict}{workflow}(model, newdata, type = NULL, ...) \method{get_predict}{tobit1}(model, newdata = insight::get_data(model), type = "response", ...) } \arguments{ \item{model}{Model object} \item{newdata}{Grid of predictor values at which we evaluate the slopes. \itemize{ \item Warning: Please avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[=datagrid]{datagrid()}} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Slopes evaluated when each predictor is held at its mean or mode. \item "median": Slopes evaluated when each predictor is held at its median or mode. \item "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Slopes evaluated at Tukey's 5 numbers. \item "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } }} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A data.frame of predicted values with a number of rows equal to the number of rows in \code{newdata} and columns "rowid" and "estimate". A "group" column is added for multivariate models or models with categorical outcomes. } \description{ Get predicted values from a model object (internal function) } \keyword{internal} marginaleffects/man/plot_slopes.Rd0000644000176200001440000002163314631452336017025 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_slopes.R \name{plot_slopes} \alias{plot_slopes} \title{Plot Conditional or Marginal Slopes} \usage{ plot_slopes( model, variables = NULL, condition = NULL, by = NULL, newdata = NULL, type = "response", vcov = NULL, conf_level = 0.95, wts = FALSE, slope = "dydx", rug = FALSE, gray = FALSE, draw = TRUE, ... ) } \arguments{ \item{model}{Model object} \item{variables}{Name of the variable whose marginal effect (slope) we want to plot on the y-axis.} \item{condition}{Conditional slopes \itemize{ \item Character vector (max length 4): Names of the predictors to display. \item Named list (max length 4): List names correspond to predictors. List elements can be: \itemize{ \item Numeric vector \item Function which returns a numeric vector or a set of unique categorical values \item Shortcut strings for common reference values: "minmax", "quartile", "threenum" } \item 1: x-axis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid). \item Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers \code{?stats::fivenum}. }} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} \item{newdata}{When \code{newdata} is \code{NULL}, the grid is determined by the \code{condition} argument. When \code{newdata} is not \code{NULL}, the argument behaves in the same way as in the \code{slopes()} function.} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{slope}{string indicates the type of slope or (semi-)elasticity to compute: \itemize{ \item "dydx": dY/dX \item "eyex": dY/dX * Y / X \item "eydx": dY/dX * Y \item "dyex": dY/dX / X \item Y is the predicted value of the outcome; X is the observed value of the predictor. }} \item{rug}{TRUE displays tick marks on the axes to mark the distribution of raw data.} \item{gray}{FALSE grayscale or color plot} \item{draw}{\code{TRUE} returns a \code{ggplot2} plot. \code{FALSE} returns a \code{data.frame} of the underlying data.} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{ggplot2} object } \description{ Plot slopes on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets). The \code{by} argument is used to plot marginal slopes, that is, slopes made on the original data, but averaged by subgroups. This is analogous to using the \code{by} argument in the \code{slopes()} function. The \code{condition} argument is used to plot conditional slopes, that is, slopes computed on a user-specified grid. This is analogous to using the \code{newdata} argument and \code{datagrid()} function in a \code{slopes()} call. All variables whose values are not specified explicitly are treated as usual by \code{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 \code{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: \itemize{ \item https://marginaleffects.com/vignettes/plot.html \item https://marginaleffects.com } } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \examples{ library(marginaleffects) mod <- lm(mpg ~ hp * drat * factor(am), data = mtcars) plot_slopes(mod, variables = "hp", condition = "drat") plot_slopes(mod, variables = "hp", condition = c("drat", "am")) plot_slopes(mod, variables = "hp", condition = list("am", "drat" = 3:5)) plot_slopes(mod, variables = "am", condition = list("hp", "drat" = range)) plot_slopes(mod, variables = "am", condition = list("hp", "drat" = "threenum")) } marginaleffects/man/specify_hypothesis.Rd0000644000176200001440000000322114631452336020374 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/hypothesis_helper.R \name{specify_hypothesis} \alias{specify_hypothesis} \title{(EXPERIMENTAL) This experimental function will soon be deprecated. Please supply a formula or function to the \code{hypothesis} argument to conduct (group-wise) hypothesis tests.} \usage{ specify_hypothesis( hypothesis = "reference", comparison = "difference", label = NULL, label_columns = NULL, by = c("term", "group", "contrast"), internal = FALSE ) } \arguments{ \item{hypothesis}{String or Function. Compute a test statistic. \itemize{ \item String: "reference" or "sequential" \item Function: Accepts a single argument named \code{estimate} and returns a numeric vector. }} \item{comparison}{String. "ratio" or "difference"} \item{label}{Function. Accepts a vector of row labels and combines them to create hypothesis labels.} \item{label_columns}{Character vector. Column names to use for hypothesis labels. Default is \code{c("group", "term", "rowid", attr(x, "variables_datagrid"), attr(x, "by"))}.} \item{by}{Character vector. Variable names which indicate subgroups in which the \code{hypothesis} function should be applied.} \item{internal}{Logical. Raises a deprecation warning when FALSE.} } \value{ \code{specify_hypothesis()} is a "function factory", which means that executing it will return a function suitable for use in the \code{hypothesis} argument of a \code{marginaleffects} function. } \description{ (EXPERIMENTAL) This experimental function will soon be deprecated. Please supply a formula or function to the \code{hypothesis} argument to conduct (group-wise) hypothesis tests. } marginaleffects/man/complete_levels.Rd0000644000176200001440000000104414631452336017636 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/complete_levels.R \name{complete_levels} \alias{complete_levels} \title{Create a data.frame with all factor or character levels} \usage{ complete_levels(x, character_levels = NULL) } \description{ \code{model.matrix} breaks when \code{newdata} includes a factor variable, but not all levels are present in the data. This is bad for us because we often want to get predictions with one (or few) rows, where some factor levels are inevitably missing. } \keyword{internal} marginaleffects/man/posterior_draws.Rd0000644000176200001440000000165414631452336017711 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/posterior_draws.R \name{posterior_draws} \alias{posterior_draws} \title{Extract Posterior Draws or Bootstrap Resamples from \code{marginaleffects} Objects} \usage{ posterior_draws(x, shape = "long") } \arguments{ \item{x}{An object produced by a \code{marginaleffects} package function, such as \code{predictions()}, \code{avg_slopes()}, \code{hypotheses()}, etc.} \item{shape}{string indicating the shape of the output format: \itemize{ \item "long": long format data frame \item "DxP": Matrix with draws as rows and parameters as columns \item "PxD": Matrix with draws as rows and parameters as columns \item "rvar": Random variable datatype (see \code{posterior} package documentation). }} } \value{ A data.frame with \code{drawid} and \code{draw} columns. } \description{ Extract Posterior Draws or Bootstrap Resamples from \code{marginaleffects} Objects } marginaleffects/man/get_model_matrix.Rd0000644000176200001440000000424414632674174020013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_model_matrix.R \name{get_model_matrix} \alias{get_model_matrix} \alias{get_model_matrix.default} \title{Get a named model matrix} \usage{ get_model_matrix(model, newdata) \method{get_model_matrix}{default}(model, newdata) } \arguments{ \item{model}{Model object} \item{newdata}{Grid of predictor values at which we evaluate the slopes. \itemize{ \item Warning: Please avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[=datagrid]{datagrid()}} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Slopes evaluated when each predictor is held at its mean or mode. \item "median": Slopes evaluated when each predictor is held at its median or mode. \item "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Slopes evaluated at Tukey's 5 numbers. \item "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } }} } \description{ Get a named model matrix } \keyword{internal} marginaleffects/man/get_coef.Rd0000644000176200001440000000534214631452336016234 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_coef.R, R/methods_MASS.R, R/methods_afex.R, % R/methods_betareg.R, R/methods_nnet.R, R/methods_brglm2.R, R/methods_brms.R, % R/methods_dataframe.R, R/methods_gamlss.R, R/methods_glmmTMB.R, % R/methods_lme4.R, R/methods_mclogit.R, R/methods_mgcv.R, R/methods_mlm.R, % R/methods_sampleSelection.R, R/methods_scam.R, R/methods_stats.R, % R/methods_survey.R, R/methods_tidymodels.R \name{get_coef} \alias{get_coef} \alias{get_coef.default} \alias{get_coef.polr} \alias{get_coef.afex_aov} \alias{get_coef.betareg} \alias{get_coef.multinom} \alias{get_coef.brmultinom} \alias{get_coef.bracl} \alias{get_coef.brmsfit} \alias{get_coef.data.frame} \alias{get_coef.gamlss} \alias{get_coef.glmmTMB} \alias{get_coef.merMod} \alias{get_coef.lmerModLmerTest} \alias{get_coef.lmerMod} \alias{get_coef.mblogit} \alias{get_coef.gam} \alias{get_coef.mlm} \alias{get_coef.selection} \alias{get_coef.scam} \alias{get_coef.nls} \alias{get_coef.svyolr} \alias{get_coef.workflow} \title{Get a named vector of coefficients from a model object (internal function)} \usage{ get_coef(model, ...) \method{get_coef}{default}(model, ...) \method{get_coef}{polr}(model, ...) \method{get_coef}{afex_aov}(model, ...) \method{get_coef}{betareg}(model, ...) \method{get_coef}{multinom}(model, ...) \method{get_coef}{brmultinom}(model, ...) \method{get_coef}{bracl}(model, ...) \method{get_coef}{brmsfit}(model, ...) \method{get_coef}{data.frame}(model, ...) \method{get_coef}{gamlss}(model, ...) \method{get_coef}{glmmTMB}(model, ...) \method{get_coef}{merMod}(model, ...) \method{get_coef}{lmerModLmerTest}(model, ...) \method{get_coef}{lmerMod}(model, ...) \method{get_coef}{mblogit}(model, ...) \method{get_coef}{gam}(model, ...) \method{get_coef}{mlm}(model, ...) \method{get_coef}{selection}(model, ...) \method{get_coef}{scam}(model, ...) \method{get_coef}{nls}(model, ...) \method{get_coef}{svyolr}(model, ...) \method{get_coef}{workflow}(model, ...) } \arguments{ \item{model}{Model object} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A named vector of coefficients. The names must match those of the variance matrix. } \description{ Get a named vector of coefficients from a model object (internal function) } \keyword{internal} marginaleffects/man/expect_slopes.Rd0000644000176200001440000000045214631452336017333 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tinytest.R \name{expect_slopes} \alias{expect_slopes} \title{\code{tinytest} helper} \usage{ expect_slopes(object, n_unique = NULL, pct_na = 5, se = TRUE, ...) } \description{ \code{tinytest} helper } \keyword{internal} marginaleffects/man/inferences.Rd0000644000176200001440000001243114631452336016577 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/inferences.R \name{inferences} \alias{inferences} \title{(EXPERIMENTAL) Bootstrap, Conformal, and Simulation-Based Inference} \usage{ inferences( x, method, R = 1000, conf_type = "perc", conformal_test = NULL, conformal_calibration = NULL, conformal_score = "residual_abs", ... ) } \arguments{ \item{x}{Object produced by one of the core \code{marginaleffects} functions.} \item{method}{String \itemize{ \item "delta": delta method standard errors \item "boot" package \item "fwb": fractional weighted bootstrap \item "rsample" package \item "simulation" from a multivariate normal distribution (Krinsky & Robb, 1986) \item "mi" multiple imputation for missing data \item "conformal_split": prediction intervals using split conformal prediction (see Angelopoulos & Bates, 2022) \item "conformal_cv+": prediction intervals using cross-validation+ conformal prediction (see Barber et al., 2020) }} \item{R}{Number of resamples, simulations, or cross-validation folds.} \item{conf_type}{String: type of bootstrap interval to construct. \itemize{ \item \code{boot}: "perc", "norm", "basic", or "bca" \item \code{fwb}: "perc", "norm", "basic", "bc", or "bca" \item \code{rsample}: "perc" or "bca" \item \code{simulation}: argument ignored. }} \item{conformal_test}{Data frame of test data for conformal prediction.} \item{conformal_calibration}{Data frame of calibration data for split conformal prediction (\verb{method="conformal_split}).} \item{conformal_score}{String. Warning: The \code{type} argument in \code{predictions()} must generate predictions which are on the same scale as the outcome variable. Typically, this means that \code{type} must be "response" or "probs". \itemize{ \item "residual_abs" or "residual_sq" for regression tasks (numeric outcome) \item "softmax" for classification tasks (when \code{predictions()} returns a \code{group} columns, such as multinomial or ordinal logit models. }} \item{...}{\itemize{ \item If \code{method="boot"}, additional arguments are passed to \code{boot::boot()}. \item If \code{method="fwb"}, additional arguments are passed to \code{fwb::fwb()}. \item If \code{method="rsample"}, additional arguments are passed to \code{rsample::bootstraps()}. \item Additional arguments are ignored for all other methods. }} } \value{ A \code{marginaleffects} object with simulation or bootstrap resamples and objects attached. } \description{ Warning: This function is experimental. It may be renamed, the user interface may change, or the functionality may migrate to arguments in other \code{marginaleffects} functions. Apply this function to a \code{marginaleffects} object to change the inferential method used to compute uncertainty estimates. } \details{ When \code{method="simulation"}, we conduct simulation-based inference following the method discussed in Krinsky & Robb (1986): \enumerate{ \item Draw \code{R} sets of simulated coefficients from a multivariate normal distribution with mean equal to the original model's estimated coefficients and variance equal to the model's variance-covariance matrix (classical, "HC3", or other). \item Use the \code{R} sets of coefficients to compute \code{R} sets of estimands: predictions, comparisons, slopes, or hypotheses. \item Take quantiles of the resulting distribution of estimands to obtain a confidence interval and the standard deviation of simulated estimates to estimate the standard error. } When \code{method="fwb"}, drawn weights are supplied to the model fitting function's \code{weights} argument; if the model doesn't accept non-integer weights, this method should not be used. If weights were included in the original model fit, they are extracted by \code{\link[=weights]{weights()}} and multiplied by the drawn weights. These weights are supplied to the \code{wts} argument of the estimation function (e.g., \code{comparisons()}). } \section{References}{ Krinsky, I., and A. L. Robb. 1986. “On Approximating the Statistical Properties of Elasticities.” Review of Economics and Statistics 68 (4): 715–9. King, Gary, Michael Tomz, and Jason Wittenberg. "Making the most of statistical analyses: Improving interpretation and presentation." American journal of political science (2000): 347-361 Dowd, Bryan E., William H. Greene, and Edward C. Norton. "Computation of standard errors." Health services research 49.2 (2014): 731-750. Angelopoulos, Anastasios N., and Stephen Bates. 2022. "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification." arXiv. https://doi.org/10.48550/arXiv.2107.07511. Barber, Rina Foygel, Emmanuel J. Candes, Aaditya Ramdas, and Ryan J. Tibshirani. 2020. “Predictive Inference with the Jackknife+.” arXiv. http://arxiv.org/abs/1905.02928. } \examples{ \dontrun{ library(marginaleffects) library(magrittr) set.seed(1024) mod <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris) # bootstrap avg_predictions(mod, by = "Species") \%>\% inferences(method = "boot") avg_predictions(mod, by = "Species") \%>\% inferences(method = "rsample") # Fractional (bayesian) bootstrap avg_slopes(mod, by = "Species") \%>\% inferences(method = "fwb") \%>\% posterior_draws("rvar") \%>\% data.frame() # Simulation-based inference slopes(mod) \%>\% inferences(method = "simulation") \%>\% head() } } marginaleffects/man/marginalmeans.Rd0000644000176200001440000000100314631452336017265 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{marginalmeans} \alias{marginalmeans} \title{Deprecated function} \usage{ marginalmeans( model, variables = NULL, newdata = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, transform = NULL, cross = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, wts = "equal", by = NULL, numderiv = "fdforward", ... ) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/get_group_names.Rd0000644000176200001440000000443214631452336017636 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_group_names.R, R/methods_MASS.R, % R/methods_nnet.R, R/methods_brglm2.R, R/methods_brms.R, R/methods_mclogit.R, % R/methods_mlm.R, R/methods_ordinal.R, R/methods_pscl.R, R/methods_survey.R \name{get_group_names} \alias{get_group_names} \alias{get_group_names.default} \alias{get_group_names.polr} \alias{get_group_names.multinom} \alias{get_group_names.bracl} \alias{get_group_names.brmsfit} \alias{get_group_names.mblogit} \alias{get_group_names.mlm} \alias{get_group_names.clm} \alias{get_group_names.hurdle} \alias{get_group_names.svyolr} \title{Get levels of the outcome variable in grouped or multivariate models} \usage{ get_group_names(model, ...) \method{get_group_names}{default}(model, ...) \method{get_group_names}{polr}(model, ...) \method{get_group_names}{multinom}(model, ...) \method{get_group_names}{bracl}(model, ...) \method{get_group_names}{brmsfit}(model, ...) \method{get_group_names}{mblogit}(model, type, ...) \method{get_group_names}{mlm}(model, ...) \method{get_group_names}{clm}(model, ...) \method{get_group_names}{hurdle}(model, type = "count", ...) \method{get_group_names}{svyolr}(model, ...) } \arguments{ \item{model}{Model object} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} } \value{ A character vector } \description{ Get levels of the outcome variable in grouped or multivariate models } \keyword{internal} marginaleffects/man/meffects.Rd0000644000176200001440000000034714631452336016255 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{meffects} \alias{meffects} \title{Deprecated function} \usage{ meffects(...) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/expect_predictions.Rd0000644000176200001440000000046314631452336020353 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tinytest.R \name{expect_predictions} \alias{expect_predictions} \title{\code{tinytest} helper} \usage{ expect_predictions(object, se = TRUE, n_row = NULL, n_col = NULL) } \description{ \code{tinytest} helper } \keyword{internal} marginaleffects/man/expect_margins.Rd0000644000176200001440000000051414631452336017465 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tinytest.R \name{expect_margins} \alias{expect_margins} \title{\code{tinytest} helper} \usage{ expect_margins( results, margins_object, se = TRUE, tolerance = 1e-05, verbose = FALSE ) } \description{ \code{tinytest} helper } \keyword{internal} marginaleffects/man/predictions.Rd0000644000176200001440000006512714632674174017022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predictions.R \name{predictions} \alias{predictions} \alias{avg_predictions} \title{Predictions} \usage{ predictions( model, newdata = NULL, variables = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, by = FALSE, byfun = NULL, wts = FALSE, transform = NULL, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, numderiv = "fdforward", ... ) avg_predictions( model, newdata = NULL, variables = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, by = TRUE, byfun = NULL, wts = FALSE, transform = NULL, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, numderiv = "fdforward", ... ) } \arguments{ \item{model}{Model object} \item{newdata}{Grid of predictor values at which we evaluate predictions. \itemize{ \item Warning: Please avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level predictions for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item string: \itemize{ \item "mean": Predictions evaluated when each predictor is held at its mean or mode. \item "median": Predictions evaluated when each predictor is held at its median or mode. \item "balanced": Predictions evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Predictions evaluated at Tukey's 5 numbers. \item "grid": Predictions evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[=datagrid]{datagrid()}} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} }} \item{variables}{Counterfactual variables. \itemize{ \item Output: \itemize{ \item \code{predictions()}: The entire dataset is replicated once for each unique combination of \code{variables}, and predictions are made. \item \code{avg_predictions()}: The entire dataset is replicated, predictions are made, and they are marginalized by \code{variables} categories. \item Warning: This can be expensive in large datasets. \item Warning: Users who need "conditional" predictions should use the \code{newdata} argument instead of \code{variables}. } \item Input: \itemize{ \item \code{NULL}: computes one prediction per row of \code{newdata} \item Character vector: the dataset is replicated once of every combination of unique values of the variables identified in \code{variables}. \item Named list: names identify the subset of variables of interest and their values. For numeric variables, the \code{variables} argument supports functions and string shortcuts: \itemize{ \item A function which returns a numeric value \item Numeric vector: Contrast between the 2nd element and the 1st element of the \code{x} vector. \item "iqr": Contrast across the interquartile range of the regressor. \item "sd": Contrast across one standard deviation around the regressor mean. \item "2sd": Contrast across two standard deviations around the regressor mean. \item "minmax": Contrast between the maximum and the minimum values of the regressor. \item "threenum": mean and 1 standard deviation on both sides \item "fivenum": Tukey's five numbers } } }} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} \item{byfun}{A function such as \code{mean()} or \code{sum()} used to aggregate estimates within the subgroups defined by the \code{by} argument. \code{NULL} uses the \code{mean()} function. Must accept a numeric vector and return a single numeric value. This is sometimes used to take the sum or mean of predicted probabilities across outcome or predictor levels. See examples section.} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{transform}{A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries.} \item{hypothesis}{specify a hypothesis test or custom contrast using a numeric value, vector, or matrix; a string equation; string; a formula, or a function. \itemize{ \item Numeric: \itemize{ \item Single value: the null hypothesis used in the computation of Z and p (before applying \code{transform}). \item Vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the \code{hypothesis} argument. \item Matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. } \item String equation to specify linear or non-linear hypothesis tests. If the \code{term} column uniquely identifies rows, terms can be used in the formula. Otherwise, use \code{b1}, \code{b2}, etc. to identify the position of each parameter. The \verb{b*} wildcard can be used to test hypotheses on all estimates. If a named vector is used, the names are used as labels in the output. Examples: \itemize{ \item \code{hp = drat} \item \code{hp + drat = 12} \item \code{b1 + b2 + b3 = 0} \item \verb{b* / b1 = 1} } \item String: \itemize{ \item "pairwise": pairwise differences between estimates in each row. \item "reference": differences between the estimates in each row and the estimate in the first row. \item "sequential": difference between an estimate and the estimate in the next row. \item "meandev": difference between an estimate and the mean of all estimates. \item "meanotherdev": difference between an estimate and the mean of all other estimates, excluding the current one. \item "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. } \item Formula: \itemize{ \item \code{comparison ~ pairs | group} \item Left-hand side determines the type of comparison to conduct: \code{difference} or \code{ratio}. If the left-hand side is empty, \code{difference} is chosen. \item Right-hand side determines the pairs of estimates to compare: \code{reference}, \code{sequential}, or \code{meandev} \item Optional: Users can supply grouping variables after a vertical bar to conduct comparisons withing subsets. \item Examples: \itemize{ \item \code{~ reference} \item \code{ratio ~ pairwise} \item \code{difference ~ pairwise | groupid} } } \item Function: \itemize{ \item Accepts an argument \code{x}: object produced by a \code{marginaleffects} function or a data frame with column \code{rowid} and \code{estimate} \item Returns a data frame with columns \code{term} and \code{estimate} (mandatory) and \code{rowid} (optional). \item The function can also accept optional input arguments: \code{newdata}, \code{by}, \code{draws}. \item This function approach will not work for Bayesian models or with bootstrapping. In those cases, it is easy to use \code{posterior_draws()} to extract and manipulate the draws directly. } \item See the Examples section below and the vignette: https://marginaleffects.com/vignettes/hypothesis.html }} \item{equivalence}{Numeric vector of length 2: bounds used for the two-one-sided test (TOST) of equivalence, and for the non-inferiority and non-superiority tests. See Details section below.} \item{p_adjust}{Adjust p-values for multiple comparisons: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", or "fdr". See \link[stats:p.adjust]{stats::p.adjust}} \item{df}{Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and \code{Inf}. When \code{df} is \code{Inf}, the normal distribution is used. When \code{df} is finite, the \code{t} distribution is used. See \link[insight:get_df]{insight::get_df} for a convenient function to extract degrees of freedom. Ex: \code{slopes(model, df = insight::get_df(model))}} \item{numderiv}{string or list of strings indicating the method to use to for the numeric differentiation used in to compute delta method standard errors. \itemize{ \item "fdforward": finite difference method with forward differences \item "fdcenter": finite difference method with central differences (default) \item "richardson": Richardson extrapolation method \item Extra arguments can be specified by passing a list to the \code{numDeriv} argument, with the name of the method first and named arguments following, ex: \code{numderiv=list("fdcenter", eps = 1e-5)}. When an unknown argument is used, \code{marginaleffects} prints the list of valid arguments for each method. }} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{data.frame} with one row per observation and several columns: \itemize{ \item \code{rowid}: row number of the \code{newdata} data frame \item \code{type}: prediction type, as defined by the \code{type} argument \item \code{group}: (optional) value of the grouped outcome (e.g., categorical outcome models) \item \code{estimate}: predicted outcome \item \code{std.error}: standard errors computed using the delta method. \item \code{p.value}: p value associated to the \code{estimate} column. The null is determined by the \code{hypothesis} argument (0 by default), and p values are computed before applying the \code{transform} argument. For models of class \code{feglm}, \code{Gam}, \code{glm} and \code{negbin}, p values are computed on the link scale by default unless the \code{type} argument is specified explicitly. \item \code{s.value}: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). \item \code{conf.low}: lower bound of the confidence interval (or equal-tailed interval for bayesian models) \item \code{conf.high}: upper bound of the confidence interval (or equal-tailed interval for bayesian models) } See \code{?print.marginaleffects} for printing options. } \description{ Outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels (a.k.a. "reference grid"). \itemize{ \item \code{predictions()}: unit-level (conditional) estimates. \item \code{avg_predictions()}: average (marginal) estimates. } The \code{newdata} argument and the \code{datagrid()} function can be used to control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. See the predictions vignette and package website for worked examples and case studies: \itemize{ \item \url{https://marginaleffects.com/vignettes/predictions.html} \item \url{https://marginaleffects.com/} } } \section{Functions}{ \itemize{ \item \code{avg_predictions()}: Average predictions }} \section{Standard errors using the delta method}{ Standard errors for all quantities estimated by \code{marginaleffects} can be obtained via the delta method. This requires differentiating a function with respect to the coefficients in the model using a finite difference approach. In some models, the delta method standard errors can be sensitive to various aspects of the numeric differentiation strategy, including the step size. By default, the step size is set to \code{1e-8}, or to \code{1e-4} times the smallest absolute model coefficient, whichever is largest. \code{marginaleffects} can delegate numeric differentiation to the \code{numDeriv} package, which allows more flexibility. To do this, users can pass arguments to the \code{numDeriv::jacobian} function through a global option. For example: \itemize{ \item \code{options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))} \item \code{options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-5)))} \item \code{options(marginaleffects_numDeriv = NULL)} } See the "Standard Errors and Confidence Intervals" vignette on the \code{marginaleffects} website for more details on the computation of standard errors: https://marginaleffects.com/vignettes/uncertainty.html Note that the \code{inferences()} function can be used to compute uncertainty estimates using a bootstrap or simulation-based inference. See the vignette: https://marginaleffects.com/vignettes/bootstrap.html } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \section{Bayesian posterior summaries}{ By default, credible intervals in bayesian models are built as equal-tailed intervals. This can be changed to a highest density interval by setting a global option: \code{options("marginaleffects_posterior_interval" = "eti")} \code{options("marginaleffects_posterior_interval" = "hdi")} By default, the center of the posterior distribution in bayesian models is identified by the median. Users can use a different summary function by setting a global option: \code{options("marginaleffects_posterior_center" = "mean")} \code{options("marginaleffects_posterior_center" = "median")} When estimates are averaged using the \code{by} argument, the \code{tidy()} function, or the \code{summary()} function, the posterior distribution is marginalized twice over. First, we take the average \emph{across} units but \emph{within} each iteration of the MCMC chain, according to what the user requested in \code{by} argument or \code{tidy()/summary()} functions. Then, we identify the center of the resulting posterior using the function supplied to the \code{"marginaleffects_posterior_center"} option (the median by default). } \section{Equivalence, Inferiority, Superiority}{ \eqn{\theta} is an estimate, \eqn{\sigma_\theta} its estimated standard error, and \eqn{[a, b]} are the bounds of the interval supplied to the \code{equivalence} argument. Non-inferiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \leq a}{\theta <= a} \item \eqn{H_1}{H1}: \eqn{\theta > a} \item \eqn{t=(\theta - a)/\sigma_\theta}{t=(\theta - a)/\sigma_\theta} \item p: Upper-tail probability } Non-superiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \geq b}{\theta >= b} \item \eqn{H_1}{H1}: \eqn{\theta < b} \item \eqn{t=(\theta - b)/\sigma_\theta}{t=(\theta - b)/\sigma_\theta} \item p: Lower-tail probability } Equivalence: Two One-Sided Tests (TOST) \itemize{ \item p: Maximum of the non-inferiority and non-superiority p values. } Thanks to Russell V. Lenth for the excellent \code{emmeans} package and documentation which inspired this feature. } \section{Prediction types}{ The \code{type} argument determines the scale of the predictions used to compute quantities of interest with functions from the \code{marginaleffects} package. Admissible values for \code{type} depend on the model object. When users specify an incorrect value for \code{type}, \code{marginaleffects} will raise an informative error with a list of valid \code{type} values for the specific model object. The first entry in the list in that error message is the default type. The \code{invlink(link)} is a special type defined by \code{marginaleffects}. It is available for some (but not all) models and functions. With this link type, we first compute predictions on the link scale, then we use the inverse link function to backtransform the predictions to the response scale. This is useful for models with non-linear link functions as it can ensure that confidence intervals stay within desirable bounds, ex: 0 to 1 for a logit model. Note that an average of estimates with \code{type="invlink(link)"} will not always be equivalent to the average of estimates with \code{type="response"}. Some of the most common \code{type} values are: response, link, E, Ep, average, class, conditional, count, cum.prob, cumhaz, cumprob, density, detection, disp, ev, expected, expvalue, fitted, hazard, invlink(link), latent, latent_N, linear, linear.predictor, linpred, location, lp, mean, numeric, p, ppd, pr, precision, prediction, prob, probability, probs, quantile, risk, rmst, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob } \section{Order of operations}{ Behind the scenes, the arguments of \code{marginaleffects} functions are evaluated in this order: \enumerate{ \item \code{newdata} \item \code{variables} \item \code{comparison} and \code{slopes} \item \code{by} \item \code{vcov} \item \code{hypothesis} \item \code{transform} } } \section{Parallel computation}{ The \code{slopes()} and \code{comparisons()} functions can use parallelism to speed up computation. Operations are parallelized for the computation of standard errors, at the model coefficient level. There is always considerable overhead when using parallel computation, mainly involved in passing the whole dataset to the different processes. Thus, parallel computation is most likely to be useful when the model includes many parameters and the dataset is relatively small. Warning: In many cases, parallel processing will not be useful at all. To activate parallel computation, users must load the \code{future.apply} package, call \code{plan()} function, and set a global option. For example: \if{html}{\out{
}}\preformatted{library(future.apply) plan("multicore", workers = 4) options(marginaleffects_parallel = TRUE) slopes(model) }\if{html}{\out{
}} To disable parallelism in \code{marginaleffects} altogether, you can set a global option: \if{html}{\out{
}}\preformatted{options(marginaleffects_parallel = FALSE) }\if{html}{\out{
}} } \section{Global options}{ The behavior of \code{marginaleffects} functions can be modified by setting global options. Disable some safety checks: \if{html}{\out{
}}\preformatted{options(marginaleffects_safe = FALSE)` }\if{html}{\out{
}} } \examples{ \dontshow{if (interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} \dontshow{\}) # examplesIf} # Adjusted Prediction for every row of the original dataset mod <- lm(mpg ~ hp + factor(cyl), data = mtcars) pred <- predictions(mod) head(pred) # Adjusted Predictions at User-Specified Values of the Regressors predictions(mod, newdata = datagrid(hp = c(100, 120), cyl = 4)) m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars) predictions(m, newdata = datagrid(FUN_factor = unique, FUN_numeric = median)) # Average Adjusted Predictions (AAP) library(dplyr) mod <- lm(mpg ~ hp * am * vs, mtcars) avg_predictions(mod) predictions(mod, by = "am") # Conditional Adjusted Predictions plot_predictions(mod, condition = "hp") # Counterfactual predictions with the `variables` argument # the `mtcars` dataset has 32 rows mod <- lm(mpg ~ hp + am, data = mtcars) p <- predictions(mod) head(p) nrow(p) # average counterfactual predictions avg_predictions(mod, variables = "am") # counterfactual predictions obtained by replicating the entire for different # values of the predictors p <- predictions(mod, variables = list(hp = c(90, 110))) nrow(p) # hypothesis test: is the prediction in the 1st row equal to the prediction in the 2nd row mod <- lm(mpg ~ wt + drat, data = mtcars) predictions( mod, newdata = datagrid(wt = 2:3), hypothesis = "b1 = b2") # same hypothesis test using row indices predictions( mod, newdata = datagrid(wt = 2:3), hypothesis = "b1 - b2 = 0") # same hypothesis test using numeric vector of weights predictions( mod, newdata = datagrid(wt = 2:3), hypothesis = c(1, -1)) # two custom contrasts using a matrix of weights lc <- matrix(c( 1, -1, 2, 3), ncol = 2) predictions( mod, newdata = datagrid(wt = 2:3), hypothesis = lc) # `by` argument mod <- lm(mpg ~ hp * am * vs, data = mtcars) predictions(mod, by = c("am", "vs")) library(nnet) nom <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE) # first 5 raw predictions predictions(nom, type = "probs") |> head() # average predictions avg_predictions(nom, type = "probs", by = "group") by <- data.frame( group = c("3", "4", "5"), by = c("3,4", "3,4", "5")) predictions(nom, type = "probs", by = by) # sum of predicted probabilities for combined response levels mod <- multinom(factor(cyl) ~ mpg + am, data = mtcars, trace = FALSE) by <- data.frame( by = c("4,6", "4,6", "8"), group = as.character(c(4, 6, 8))) predictions(mod, newdata = "mean", byfun = sum, by = by) } \references{ \itemize{ \item Greenland S. 2019. "Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values." The American Statistician. 73(S1): 106–114. \item Cole, Stephen R, Jessie K Edwards, and Sander Greenland. 2020. "Surprise!" American Journal of Epidemiology 190 (2): 191–93. https://doi.org/10.1093/aje/kwaa136 } } marginaleffects/man/reexports.Rd0000644000176200001440000000066714631452336016521 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/broom.R \docType{import} \name{reexports} \alias{reexports} \alias{tidy} \alias{glance} \title{Objects exported from other packages} \keyword{internal} \description{ These objects are imported from other packages. Follow the links below to see their documentation. \describe{ \item{generics}{\code{\link[generics]{glance}}, \code{\link[generics]{tidy}}} }} marginaleffects/man/get_vcov.Rd0000644000176200001440000000756114631452336016302 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_vcov.R, R/methods_MCMCglmm.R, % R/methods_afex.R, R/methods_aod.R, R/methods_biglm.R, R/methods_brms.R, % R/methods_dbarts.R, R/methods_gamlss.R, R/methods_glmmTMB.R, % R/methods_mhurdle.R, R/methods_mlr3.R, R/methods_rms.R, R/methods_scam.R, % R/methods_tidymodels.R \name{get_vcov} \alias{get_vcov} \alias{get_vcov.default} \alias{get_vcov.MCMCglmm} \alias{get_vcov.afex_aov} \alias{get_vcov.glimML} \alias{get_vcov.biglm} \alias{get_vcov.brmsfit} \alias{get_vcov.bart} \alias{get_vcov.gamlss} \alias{get_vcov.glmmTMB} \alias{get_vcov.mhurdle} \alias{get_vcov.Learner} \alias{get_vcov.orm} \alias{get_vcov.scam} \alias{get_vcov.model_fit} \alias{get_vcov.workflow} \title{Get a named variance-covariance matrix from a model object (internal function)} \usage{ get_vcov(model, ...) \method{get_vcov}{default}(model, vcov = NULL, ...) \method{get_vcov}{MCMCglmm}(model, vcov = NULL, ...) \method{get_vcov}{afex_aov}(model, vcov = NULL, ...) \method{get_vcov}{glimML}(model, vcov = NULL, ...) \method{get_vcov}{biglm}(model, vcov = NULL, ...) \method{get_vcov}{brmsfit}(model, vcov = NULL, ...) \method{get_vcov}{bart}(model, vcov = NULL, ...) \method{get_vcov}{gamlss}(model, ...) \method{get_vcov}{glmmTMB}(model, ...) \method{get_vcov}{mhurdle}(model, vcov = NULL, ...) \method{get_vcov}{Learner}(model, ...) \method{get_vcov}{orm}(model, vcov = NULL, ...) \method{get_vcov}{scam}(model, vcov = NULL, ...) \method{get_vcov}{model_fit}(model, type = NULL, ...) \method{get_vcov}{workflow}(model, type = NULL, ...) } \arguments{ \item{model}{Model object} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} } \value{ A named square matrix of variance and covariances. The names must match the coefficient names. } \description{ Get a named variance-covariance matrix from a model object (internal function) } \keyword{internal} marginaleffects/man/slopes.Rd0000644000176200001440000006272714632674174016007 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/slopes.R \name{slopes} \alias{slopes} \alias{avg_slopes} \title{Slopes (aka Partial derivatives, Marginal Effects, or Trends)} \usage{ slopes( model, newdata = NULL, variables = NULL, type = NULL, by = FALSE, vcov = TRUE, conf_level = 0.95, slope = "dydx", wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ... ) avg_slopes( model, newdata = NULL, variables = NULL, type = NULL, by = TRUE, vcov = TRUE, conf_level = 0.95, slope = "dydx", wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ... ) } \arguments{ \item{model}{Model object} \item{newdata}{Grid of predictor values at which we evaluate the slopes. \itemize{ \item Warning: Please avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[=datagrid]{datagrid()}} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Slopes evaluated when each predictor is held at its mean or mode. \item "median": Slopes evaluated when each predictor is held at its median or mode. \item "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Slopes evaluated at Tukey's 5 numbers. \item "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } }} \item{variables}{Focal variables \itemize{ \item \code{NULL}: compute slopes or comparisons for all the variables in the model object (can be slow). \item Character vector: subset of variables (usually faster). }} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{slope}{string indicates the type of slope or (semi-)elasticity to compute: \itemize{ \item "dydx": dY/dX \item "eyex": dY/dX * Y / X \item "eydx": dY/dX * Y \item "dyex": dY/dX / X \item Y is the predicted value of the outcome; X is the observed value of the predictor. }} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{hypothesis}{specify a hypothesis test or custom contrast using a numeric value, vector, or matrix; a string equation; string; a formula, or a function. \itemize{ \item Numeric: \itemize{ \item Single value: the null hypothesis used in the computation of Z and p (before applying \code{transform}). \item Vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the \code{hypothesis} argument. \item Matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. } \item String equation to specify linear or non-linear hypothesis tests. If the \code{term} column uniquely identifies rows, terms can be used in the formula. Otherwise, use \code{b1}, \code{b2}, etc. to identify the position of each parameter. The \verb{b*} wildcard can be used to test hypotheses on all estimates. If a named vector is used, the names are used as labels in the output. Examples: \itemize{ \item \code{hp = drat} \item \code{hp + drat = 12} \item \code{b1 + b2 + b3 = 0} \item \verb{b* / b1 = 1} } \item String: \itemize{ \item "pairwise": pairwise differences between estimates in each row. \item "reference": differences between the estimates in each row and the estimate in the first row. \item "sequential": difference between an estimate and the estimate in the next row. \item "meandev": difference between an estimate and the mean of all estimates. \item "meanotherdev": difference between an estimate and the mean of all other estimates, excluding the current one. \item "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. } \item Formula: \itemize{ \item \code{comparison ~ pairs | group} \item Left-hand side determines the type of comparison to conduct: \code{difference} or \code{ratio}. If the left-hand side is empty, \code{difference} is chosen. \item Right-hand side determines the pairs of estimates to compare: \code{reference}, \code{sequential}, or \code{meandev} \item Optional: Users can supply grouping variables after a vertical bar to conduct comparisons withing subsets. \item Examples: \itemize{ \item \code{~ reference} \item \code{ratio ~ pairwise} \item \code{difference ~ pairwise | groupid} } } \item Function: \itemize{ \item Accepts an argument \code{x}: object produced by a \code{marginaleffects} function or a data frame with column \code{rowid} and \code{estimate} \item Returns a data frame with columns \code{term} and \code{estimate} (mandatory) and \code{rowid} (optional). \item The function can also accept optional input arguments: \code{newdata}, \code{by}, \code{draws}. \item This function approach will not work for Bayesian models or with bootstrapping. In those cases, it is easy to use \code{posterior_draws()} to extract and manipulate the draws directly. } \item See the Examples section below and the vignette: https://marginaleffects.com/vignettes/hypothesis.html }} \item{equivalence}{Numeric vector of length 2: bounds used for the two-one-sided test (TOST) of equivalence, and for the non-inferiority and non-superiority tests. See Details section below.} \item{p_adjust}{Adjust p-values for multiple comparisons: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", or "fdr". See \link[stats:p.adjust]{stats::p.adjust}} \item{df}{Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and \code{Inf}. When \code{df} is \code{Inf}, the normal distribution is used. When \code{df} is finite, the \code{t} distribution is used. See \link[insight:get_df]{insight::get_df} for a convenient function to extract degrees of freedom. Ex: \code{slopes(model, df = insight::get_df(model))}} \item{eps}{NULL or numeric value which determines the step size to use when calculating numerical derivatives: (f(x+eps)-f(x))/eps. When \code{eps} is \code{NULL}, the step size is 0.0001 multiplied by the difference between the maximum and minimum values of the variable with respect to which we are taking the derivative. Changing \code{eps} may be necessary to avoid numerical problems in certain models.} \item{numderiv}{string or list of strings indicating the method to use to for the numeric differentiation used in to compute delta method standard errors. \itemize{ \item "fdforward": finite difference method with forward differences \item "fdcenter": finite difference method with central differences (default) \item "richardson": Richardson extrapolation method \item Extra arguments can be specified by passing a list to the \code{numDeriv} argument, with the name of the method first and named arguments following, ex: \code{numderiv=list("fdcenter", eps = 1e-5)}. When an unknown argument is used, \code{marginaleffects} prints the list of valid arguments for each method. }} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{data.frame} with one row per observation (per term/group) and several columns: \itemize{ \item \code{rowid}: row number of the \code{newdata} data frame \item \code{type}: prediction type, as defined by the \code{type} argument \item \code{group}: (optional) value of the grouped outcome (e.g., categorical outcome models) \item \code{term}: the variable whose marginal effect is computed \item \code{dydx}: slope of the outcome with respect to the term, for a given combination of predictor values \item \code{std.error}: standard errors computed by via the delta method. \item \code{p.value}: p value associated to the \code{estimate} column. The null is determined by the \code{hypothesis} argument (0 by default), and p values are computed before applying the \code{transform} argument. For models of class \code{feglm}, \code{Gam}, \code{glm} and \code{negbin}, p values are computed on the link scale by default unless the \code{type} argument is specified explicitly. \item \code{s.value}: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). \item \code{conf.low}: lower bound of the confidence interval (or equal-tailed interval for bayesian models) \item \code{conf.high}: upper bound of the confidence interval (or equal-tailed interval for bayesian models) } See \code{?print.marginaleffects} for printing options. } \description{ Partial derivative of the regression equation with respect to a regressor of interest. \itemize{ \item \code{slopes()}: unit-level (conditional) estimates. \item \code{avg_slopes()}: average (marginal) estimates. } The \code{newdata} argument and the \code{datagrid()} function can be used to control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. See the slopes vignette and package website for worked examples and case studies: \itemize{ \item \url{https://marginaleffects.com/vignettes/slopes.html} \item \url{https://marginaleffects.com/} } } \details{ A "slope" or "marginal effect" is the partial derivative of the regression equation with respect to a variable in the model. This function uses automatic differentiation to compute slopes for a vast array of models, including non-linear models with transformations (e.g., polynomials). Uncertainty estimates are computed using the delta method. Numerical derivatives for the \code{slopes} function are calculated using a simple epsilon difference approach: \eqn{\partial Y / \partial X = (f(X + \varepsilon/2) - f(X-\varepsilon/2)) / \varepsilon}{dY/dX = (f(X + e/2) - f(X-e/2)) / e}, where f is the \code{predict()} method associated with the model class, and \eqn{\varepsilon}{e} is determined by the \code{eps} argument. } \section{Functions}{ \itemize{ \item \code{avg_slopes()}: Average slopes }} \section{Standard errors using the delta method}{ Standard errors for all quantities estimated by \code{marginaleffects} can be obtained via the delta method. This requires differentiating a function with respect to the coefficients in the model using a finite difference approach. In some models, the delta method standard errors can be sensitive to various aspects of the numeric differentiation strategy, including the step size. By default, the step size is set to \code{1e-8}, or to \code{1e-4} times the smallest absolute model coefficient, whichever is largest. \code{marginaleffects} can delegate numeric differentiation to the \code{numDeriv} package, which allows more flexibility. To do this, users can pass arguments to the \code{numDeriv::jacobian} function through a global option. For example: \itemize{ \item \code{options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))} \item \code{options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-5)))} \item \code{options(marginaleffects_numDeriv = NULL)} } See the "Standard Errors and Confidence Intervals" vignette on the \code{marginaleffects} website for more details on the computation of standard errors: https://marginaleffects.com/vignettes/uncertainty.html Note that the \code{inferences()} function can be used to compute uncertainty estimates using a bootstrap or simulation-based inference. See the vignette: https://marginaleffects.com/vignettes/bootstrap.html } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \section{Bayesian posterior summaries}{ By default, credible intervals in bayesian models are built as equal-tailed intervals. This can be changed to a highest density interval by setting a global option: \code{options("marginaleffects_posterior_interval" = "eti")} \code{options("marginaleffects_posterior_interval" = "hdi")} By default, the center of the posterior distribution in bayesian models is identified by the median. Users can use a different summary function by setting a global option: \code{options("marginaleffects_posterior_center" = "mean")} \code{options("marginaleffects_posterior_center" = "median")} When estimates are averaged using the \code{by} argument, the \code{tidy()} function, or the \code{summary()} function, the posterior distribution is marginalized twice over. First, we take the average \emph{across} units but \emph{within} each iteration of the MCMC chain, according to what the user requested in \code{by} argument or \code{tidy()/summary()} functions. Then, we identify the center of the resulting posterior using the function supplied to the \code{"marginaleffects_posterior_center"} option (the median by default). } \section{Equivalence, Inferiority, Superiority}{ \eqn{\theta} is an estimate, \eqn{\sigma_\theta} its estimated standard error, and \eqn{[a, b]} are the bounds of the interval supplied to the \code{equivalence} argument. Non-inferiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \leq a}{\theta <= a} \item \eqn{H_1}{H1}: \eqn{\theta > a} \item \eqn{t=(\theta - a)/\sigma_\theta}{t=(\theta - a)/\sigma_\theta} \item p: Upper-tail probability } Non-superiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \geq b}{\theta >= b} \item \eqn{H_1}{H1}: \eqn{\theta < b} \item \eqn{t=(\theta - b)/\sigma_\theta}{t=(\theta - b)/\sigma_\theta} \item p: Lower-tail probability } Equivalence: Two One-Sided Tests (TOST) \itemize{ \item p: Maximum of the non-inferiority and non-superiority p values. } Thanks to Russell V. Lenth for the excellent \code{emmeans} package and documentation which inspired this feature. } \section{Prediction types}{ The \code{type} argument determines the scale of the predictions used to compute quantities of interest with functions from the \code{marginaleffects} package. Admissible values for \code{type} depend on the model object. When users specify an incorrect value for \code{type}, \code{marginaleffects} will raise an informative error with a list of valid \code{type} values for the specific model object. The first entry in the list in that error message is the default type. The \code{invlink(link)} is a special type defined by \code{marginaleffects}. It is available for some (but not all) models and functions. With this link type, we first compute predictions on the link scale, then we use the inverse link function to backtransform the predictions to the response scale. This is useful for models with non-linear link functions as it can ensure that confidence intervals stay within desirable bounds, ex: 0 to 1 for a logit model. Note that an average of estimates with \code{type="invlink(link)"} will not always be equivalent to the average of estimates with \code{type="response"}. Some of the most common \code{type} values are: response, link, E, Ep, average, class, conditional, count, cum.prob, cumhaz, cumprob, density, detection, disp, ev, expected, expvalue, fitted, hazard, invlink(link), latent, latent_N, linear, linear.predictor, linpred, location, lp, mean, numeric, p, ppd, pr, precision, prediction, prob, probability, probs, quantile, risk, rmst, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob } \section{Parallel computation}{ The \code{slopes()} and \code{comparisons()} functions can use parallelism to speed up computation. Operations are parallelized for the computation of standard errors, at the model coefficient level. There is always considerable overhead when using parallel computation, mainly involved in passing the whole dataset to the different processes. Thus, parallel computation is most likely to be useful when the model includes many parameters and the dataset is relatively small. Warning: In many cases, parallel processing will not be useful at all. To activate parallel computation, users must load the \code{future.apply} package, call \code{plan()} function, and set a global option. For example: \if{html}{\out{
}}\preformatted{library(future.apply) plan("multicore", workers = 4) options(marginaleffects_parallel = TRUE) slopes(model) }\if{html}{\out{
}} To disable parallelism in \code{marginaleffects} altogether, you can set a global option: \if{html}{\out{
}}\preformatted{options(marginaleffects_parallel = FALSE) }\if{html}{\out{
}} } \section{Order of operations}{ Behind the scenes, the arguments of \code{marginaleffects} functions are evaluated in this order: \enumerate{ \item \code{newdata} \item \code{variables} \item \code{comparison} and \code{slopes} \item \code{by} \item \code{vcov} \item \code{hypothesis} \item \code{transform} } } \section{Global options}{ The behavior of \code{marginaleffects} functions can be modified by setting global options. Disable some safety checks: \if{html}{\out{
}}\preformatted{options(marginaleffects_safe = FALSE)` }\if{html}{\out{
}} } \examples{ \dontshow{if (interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} \dontshow{\}) # examplesIf} # Unit-level (conditional) Marginal Effects mod <- glm(am ~ hp * wt, data = mtcars, family = binomial) mfx <- slopes(mod) head(mfx) # Average Marginal Effect (AME) avg_slopes(mod, by = TRUE) # Marginal Effect at the Mean (MEM) slopes(mod, newdata = datagrid()) # Marginal Effect at User-Specified Values # Variables not explicitly included in `datagrid()` are held at their means slopes(mod, newdata = datagrid(hp = c(100, 110))) # Group-Average Marginal Effects (G-AME) # Calculate marginal effects for each observation, and then take the average # marginal effect within each subset of observations with different observed # values for the `cyl` variable: mod2 <- lm(mpg ~ hp * cyl, data = mtcars) avg_slopes(mod2, variables = "hp", by = "cyl") # Marginal Effects at User-Specified Values (counterfactual) # Variables not explicitly included in `datagrid()` are held at their # original values, and the whole dataset is duplicated once for each # combination of the values in `datagrid()` mfx <- slopes(mod, newdata = datagrid( hp = c(100, 110), grid_type = "counterfactual")) head(mfx) # Heteroskedasticity robust standard errors mfx <- slopes(mod, vcov = sandwich::vcovHC(mod)) head(mfx) # hypothesis test: is the `hp` marginal effect at the mean equal to the `drat` marginal effect mod <- lm(mpg ~ wt + drat, data = mtcars) slopes( mod, newdata = "mean", hypothesis = "wt = drat") # same hypothesis test using row indices slopes( mod, newdata = "mean", hypothesis = "b1 - b2 = 0") # same hypothesis test using numeric vector of weights slopes( mod, newdata = "mean", hypothesis = c(1, -1)) # two custom contrasts using a matrix of weights lc <- matrix( c( 1, -1, 2, 3), ncol = 2) colnames(lc) <- c("Contrast A", "Contrast B") slopes( mod, newdata = "mean", hypothesis = lc) } \references{ \itemize{ \item Greenland S. 2019. "Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values." The American Statistician. 73(S1): 106–114. \item Cole, Stephen R, Jessie K Edwards, and Sander Greenland. 2020. "Surprise!" American Journal of Epidemiology 190 (2): 191–93. https://doi.org/10.1093/aje/kwaa136 } } marginaleffects/man/set_coef.Rd0000644000176200001440000000676214631452336016257 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/set_coef.R, R/methods_AER.R, R/methods_MASS.R, % R/methods_Rchoice.R, R/methods_afex.R, R/methods_aod.R, R/methods_betareg.R, % R/methods_nnet.R, R/methods_crch.R, R/methods_dataframe.R, % R/methods_flexsurv.R, R/methods_gamlss.R, R/methods_glmmTMB.R, % R/methods_glmx.R, R/methods_lme4.R, R/methods_mlm.R, R/methods_nlme.R, % R/methods_pscl.R, R/methods_robustlmm.R, R/methods_sampleSelection.R, % R/methods_scam.R, R/methods_stats.R, R/methods_survey.R, % R/methods_survival.R, R/methods_tidymodels.R \name{set_coef} \alias{set_coef} \alias{set_coef.default} \alias{set_coef.tobit} \alias{set_coef.polr} \alias{set_coef.glmmPQL} \alias{set_coef.hetprob} \alias{set_coef.ivpml} \alias{set_coef.afex_aov} \alias{set_coef.glimML} \alias{set_coef.betareg} \alias{set_coef.multinom} \alias{set_coef.crch} \alias{set_coef.hxlr} \alias{set_coef.data.frame} \alias{set_coef.flexsurvreg} \alias{set_coef.gamlss} \alias{set_coef.glmmTMB} \alias{set_coef.glmx} \alias{set_coef.merMod} \alias{set_coef.lmerModLmerTest} \alias{set_coef.lmerMod} \alias{set_coef.mlm} \alias{set_coef.lme} \alias{set_coef.hurdle} \alias{set_coef.zeroinfl} \alias{set_coef.rlmerMod} \alias{set_coef.selection} \alias{set_coef.scam} \alias{set_coef.glm} \alias{set_coef.lm} \alias{set_coef.nls} \alias{set_coef.svyolr} \alias{set_coef.survreg} \alias{set_coef.model_fit} \alias{set_coef.workflow} \title{Internal function to set coefficients} \usage{ set_coef(model, coefs, ...) \method{set_coef}{default}(model, coefs, ...) \method{set_coef}{tobit}(model, coefs, ...) \method{set_coef}{polr}(model, coefs, ...) \method{set_coef}{glmmPQL}(model, coefs, ...) \method{set_coef}{hetprob}(model, coefs, ...) \method{set_coef}{ivpml}(model, coefs, ...) \method{set_coef}{afex_aov}(model, coefs, ...) \method{set_coef}{glimML}(model, coefs, ...) \method{set_coef}{betareg}(model, coefs, ...) \method{set_coef}{multinom}(model, coefs, ...) \method{set_coef}{crch}(model, coefs, ...) \method{set_coef}{hxlr}(model, coefs, ...) \method{set_coef}{data.frame}(model, coefs, ...) \method{set_coef}{flexsurvreg}(model, coefs, ...) \method{set_coef}{gamlss}(model, coefs, ...) \method{set_coef}{glmmTMB}(model, coefs, ...) \method{set_coef}{glmx}(model, coefs, ...) \method{set_coef}{merMod}(model, coefs, ...) \method{set_coef}{lmerModLmerTest}(model, coefs, ...) \method{set_coef}{lmerMod}(model, coefs, ...) \method{set_coef}{mlm}(model, coefs, ...) \method{set_coef}{lme}(model, coefs, ...) \method{set_coef}{hurdle}(model, coefs, ...) \method{set_coef}{zeroinfl}(model, coefs, ...) \method{set_coef}{rlmerMod}(model, coefs, ...) \method{set_coef}{selection}(model, coefs, ...) \method{set_coef}{scam}(model, coefs, ...) \method{set_coef}{glm}(model, coefs, ...) \method{set_coef}{lm}(model, coefs, ...) \method{set_coef}{nls}(model, coefs, ...) \method{set_coef}{svyolr}(model, coefs, ...) \method{set_coef}{survreg}(model, coefs, ...) \method{set_coef}{model_fit}(model, coefs, ...) \method{set_coef}{workflow}(model, coefs, ...) } \arguments{ \item{model}{object to modify} \item{coefs}{vector of coefficients to insert in the model object} } \value{ Model object of the same class as the \code{model} argument, but with different stored coefficients. } \description{ Set the coefficients in a model to different values and return the modified object (internal function) } \details{ To compute the variance of marginal effects we need to take the Jacobian with } \keyword{internal} marginaleffects/man/posteriordraws.Rd0000644000176200001440000000166114631452336017550 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/posterior_draws.R \name{posteriordraws} \alias{posteriordraws} \title{\code{posteriordraws()} is an alias to \code{posterior_draws()}} \usage{ posteriordraws(x, shape = "long") } \arguments{ \item{x}{An object produced by a \code{marginaleffects} package function, such as \code{predictions()}, \code{avg_slopes()}, \code{hypotheses()}, etc.} \item{shape}{string indicating the shape of the output format: \itemize{ \item "long": long format data frame \item "DxP": Matrix with draws as rows and parameters as columns \item "PxD": Matrix with draws as rows and parameters as columns \item "rvar": Random variable datatype (see \code{posterior} package documentation). }} } \value{ A data.frame with \code{drawid} and \code{draw} columns. } \description{ This alias is kept for backward compatibility and because some users may prefer that name. } \keyword{internal} marginaleffects/man/marginaleffects.Rd0000644000176200001440000000037414631452336017613 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{marginaleffects} \alias{marginaleffects} \title{Deprecated function} \usage{ marginaleffects(...) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/deltamethod.Rd0000644000176200001440000000036014631452336016746 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{deltamethod} \alias{deltamethod} \title{Deprecated function} \usage{ deltamethod(...) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/comparisons.Rd0000644000176200001440000010103214632674174017016 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/comparisons.R \name{comparisons} \alias{comparisons} \alias{avg_comparisons} \title{Comparisons Between Predictions Made With Different Regressor Values} \usage{ comparisons( model, newdata = NULL, variables = NULL, comparison = "difference", type = NULL, vcov = TRUE, by = FALSE, conf_level = 0.95, transform = NULL, cross = FALSE, wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ... ) avg_comparisons( model, newdata = NULL, variables = NULL, type = NULL, vcov = TRUE, by = TRUE, conf_level = 0.95, comparison = "difference", transform = NULL, cross = FALSE, wts = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, eps = NULL, numderiv = "fdforward", ... ) } \arguments{ \item{model}{Model object} \item{newdata}{Grid of predictor values at which we evaluate the comparisons. \itemize{ \item Warning: Avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level contrasts for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item data frame: Unit-level contrasts for each row of the \code{newdata} data frame. \item string: \itemize{ \item "mean": Contrasts at the Mean. Contrasts when each predictor is held at its mean or mode. \item "median": Contrasts at the Median. Contrasts when each predictor is held at its median or mode. \item "balanced": Contrasts evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Contrasts at Tukey's 5 numbers. \item "grid": Contrasts on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item \code{newdata = datagrid(mpg = fivenum)}: \code{mpg} variable held at Tukey's five numbers (using the \code{fivenum} function), and other regressors fixed at their means or modes. \item See the Examples section and the \link{datagrid} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} }} \item{variables}{Focal variables \itemize{ \item \code{NULL}: compute comparisons for all the variables in the model object (can be slow). \item Character vector: subset of variables (usually faster). \item Named list: names identify the subset of variables of interest, and values define the type of contrast to compute. Acceptable values depend on the variable type: \itemize{ \item Factor or character variables: \itemize{ \item "reference": Each factor level is compared to the factor reference (base) level \item "all": All combinations of observed levels \item "sequential": Each factor level is compared to the previous factor level \item "pairwise": Each factor level is compared to all other levels \item "minmax": The highest and lowest levels of a factor. \item "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses. \item Vector of length 2 with the two values to compare. \item Data frame with the same number of rows as \code{newdata}, with two columns of "lo" and "hi" values to compare. \item Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. } \item Logical variables: \itemize{ \item NULL: contrast between TRUE and FALSE \item Data frame with the same number of rows as \code{newdata}, with two columns of "lo" and "hi" values to compare. \item Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. } \item Numeric variables: \itemize{ \item Numeric of length 1: Forward contrast for a gap of \code{x}, computed between the observed value and the observed value plus \code{x}. Users can set a global option to get a "center" or "backward" contrast instead: \code{options(marginaleffects_contrast_direction="center")} \item Numeric vector of length 2: Contrast between the largest and the smallest elements of the \code{x} vector. \item Data frame with the same number of rows as \code{newdata}, with two columns of "lo" and "hi" values to compare. \item Function that accepts a vector and returns a data frame with two columns of "lo" and "hi" values to compare. See examples below. \item "iqr": Contrast across the interquartile range of the regressor. \item "sd": Contrast across one standard deviation around the regressor mean. \item "2sd": Contrast across two standard deviations around the regressor mean. \item "minmax": Contrast between the maximum and the minimum values of the regressor. } \item Examples: \itemize{ \item \code{variables = list(gear = "pairwise", hp = 10)} \item \code{variables = list(gear = "sequential", hp = c(100, 120))} \item \verb{variables = list(hp = \\(x) data.frame(low = x - 5, high = x + 10))} \item See the Examples section below for more. } } }} \item{comparison}{How should pairs of predictions be compared? Difference, ratio, odds ratio, or user-defined functions. \itemize{ \item string: shortcuts to common contrast functions. \itemize{ \item Supported shortcuts strings: difference, differenceavg, differenceavgwts, dydx, eyex, eydx, dyex, dydxavg, eyexavg, eydxavg, dyexavg, dydxavgwts, eyexavgwts, eydxavgwts, dyexavgwts, ratio, ratioavg, ratioavgwts, lnratio, lnratioavg, lnratioavgwts, lnor, lnoravg, lnoravgwts, lift, liftavg, expdydx, expdydxavg, expdydxavgwts \item See the Comparisons section below for definitions of each transformation. } \item function: accept two equal-length numeric vectors of adjusted predictions (\code{hi} and \code{lo}) and returns a vector of contrasts of the same length, or a unique numeric value. \itemize{ \item See the Transformations section below for examples of valid functions. } }} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{transform}{string or function. Transformation applied to unit-level estimates and confidence intervals just before the function returns results. Functions must accept a vector and return a vector of the same length. Support string shortcuts: "exp", "ln"} \item{cross}{\itemize{ \item \code{FALSE}: Contrasts represent the change in adjusted predictions when one predictor changes and all other variables are held constant. \item \code{TRUE}: Contrasts represent the changes in adjusted predictions when all the predictors specified in the \code{variables} argument are manipulated simultaneously (a "cross-contrast"). }} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{hypothesis}{specify a hypothesis test or custom contrast using a numeric value, vector, or matrix; a string equation; string; a formula, or a function. \itemize{ \item Numeric: \itemize{ \item Single value: the null hypothesis used in the computation of Z and p (before applying \code{transform}). \item Vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the \code{hypothesis} argument. \item Matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. } \item String equation to specify linear or non-linear hypothesis tests. If the \code{term} column uniquely identifies rows, terms can be used in the formula. Otherwise, use \code{b1}, \code{b2}, etc. to identify the position of each parameter. The \verb{b*} wildcard can be used to test hypotheses on all estimates. If a named vector is used, the names are used as labels in the output. Examples: \itemize{ \item \code{hp = drat} \item \code{hp + drat = 12} \item \code{b1 + b2 + b3 = 0} \item \verb{b* / b1 = 1} } \item String: \itemize{ \item "pairwise": pairwise differences between estimates in each row. \item "reference": differences between the estimates in each row and the estimate in the first row. \item "sequential": difference between an estimate and the estimate in the next row. \item "meandev": difference between an estimate and the mean of all estimates. \item "meanotherdev": difference between an estimate and the mean of all other estimates, excluding the current one. \item "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. } \item Formula: \itemize{ \item \code{comparison ~ pairs | group} \item Left-hand side determines the type of comparison to conduct: \code{difference} or \code{ratio}. If the left-hand side is empty, \code{difference} is chosen. \item Right-hand side determines the pairs of estimates to compare: \code{reference}, \code{sequential}, or \code{meandev} \item Optional: Users can supply grouping variables after a vertical bar to conduct comparisons withing subsets. \item Examples: \itemize{ \item \code{~ reference} \item \code{ratio ~ pairwise} \item \code{difference ~ pairwise | groupid} } } \item Function: \itemize{ \item Accepts an argument \code{x}: object produced by a \code{marginaleffects} function or a data frame with column \code{rowid} and \code{estimate} \item Returns a data frame with columns \code{term} and \code{estimate} (mandatory) and \code{rowid} (optional). \item The function can also accept optional input arguments: \code{newdata}, \code{by}, \code{draws}. \item This function approach will not work for Bayesian models or with bootstrapping. In those cases, it is easy to use \code{posterior_draws()} to extract and manipulate the draws directly. } \item See the Examples section below and the vignette: https://marginaleffects.com/vignettes/hypothesis.html }} \item{equivalence}{Numeric vector of length 2: bounds used for the two-one-sided test (TOST) of equivalence, and for the non-inferiority and non-superiority tests. See Details section below.} \item{p_adjust}{Adjust p-values for multiple comparisons: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", or "fdr". See \link[stats:p.adjust]{stats::p.adjust}} \item{df}{Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and \code{Inf}. When \code{df} is \code{Inf}, the normal distribution is used. When \code{df} is finite, the \code{t} distribution is used. See \link[insight:get_df]{insight::get_df} for a convenient function to extract degrees of freedom. Ex: \code{slopes(model, df = insight::get_df(model))}} \item{eps}{NULL or numeric value which determines the step size to use when calculating numerical derivatives: (f(x+eps)-f(x))/eps. When \code{eps} is \code{NULL}, the step size is 0.0001 multiplied by the difference between the maximum and minimum values of the variable with respect to which we are taking the derivative. Changing \code{eps} may be necessary to avoid numerical problems in certain models.} \item{numderiv}{string or list of strings indicating the method to use to for the numeric differentiation used in to compute delta method standard errors. \itemize{ \item "fdforward": finite difference method with forward differences \item "fdcenter": finite difference method with central differences (default) \item "richardson": Richardson extrapolation method \item Extra arguments can be specified by passing a list to the \code{numDeriv} argument, with the name of the method first and named arguments following, ex: \code{numderiv=list("fdcenter", eps = 1e-5)}. When an unknown argument is used, \code{marginaleffects} prints the list of valid arguments for each method. }} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{data.frame} with one row per observation (per term/group) and several columns: \itemize{ \item \code{rowid}: row number of the \code{newdata} data frame \item \code{type}: prediction type, as defined by the \code{type} argument \item \code{group}: (optional) value of the grouped outcome (e.g., categorical outcome models) \item \code{term}: the variable whose marginal effect is computed \item \code{dydx}: slope of the outcome with respect to the term, for a given combination of predictor values \item \code{std.error}: standard errors computed by via the delta method. \item \code{p.value}: p value associated to the \code{estimate} column. The null is determined by the \code{hypothesis} argument (0 by default), and p values are computed before applying the \code{transform} argument. \item \code{s.value}: Shannon information transforms of p values. How many consecutive "heads" tosses would provide the same amount of evidence (or "surprise") against the null hypothesis that the coin is fair? The purpose of S is to calibrate the analyst's intuition about the strength of evidence encoded in p against a well-known physical phenomenon. See Greenland (2019) and Cole et al. (2020). \item \code{conf.low}: lower bound of the confidence interval (or equal-tailed interval for bayesian models) \item \code{conf.high}: upper bound of the confidence interval (or equal-tailed interval for bayesian models) } See \code{?print.marginaleffects} for printing options. } \description{ Predict the outcome variable at different regressor values (e.g., college graduates vs. others), and compare those predictions by computing a difference, ratio, or some other function. \code{comparisons()} can return many quantities of interest, such as contrasts, differences, risk ratios, changes in log odds, lift, slopes, elasticities, etc. \itemize{ \item \code{comparisons()}: unit-level (conditional) estimates. \item \code{avg_comparisons()}: average (marginal) estimates. } \code{variables} identifies the focal regressors whose "effect" we are interested in. \code{comparison} determines how predictions with different regressor values are compared (difference, ratio, odds, etc.). The \code{newdata} argument and the \code{datagrid()} function control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc. See the comparisons vignette and package website for worked examples and case studies: \itemize{ \item \url{https://marginaleffects.com/vignettes/comparisons.html} \item \url{https://marginaleffects.com/} } } \section{Functions}{ \itemize{ \item \code{avg_comparisons()}: Average comparisons }} \section{Standard errors using the delta method}{ Standard errors for all quantities estimated by \code{marginaleffects} can be obtained via the delta method. This requires differentiating a function with respect to the coefficients in the model using a finite difference approach. In some models, the delta method standard errors can be sensitive to various aspects of the numeric differentiation strategy, including the step size. By default, the step size is set to \code{1e-8}, or to \code{1e-4} times the smallest absolute model coefficient, whichever is largest. \code{marginaleffects} can delegate numeric differentiation to the \code{numDeriv} package, which allows more flexibility. To do this, users can pass arguments to the \code{numDeriv::jacobian} function through a global option. For example: \itemize{ \item \code{options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))} \item \code{options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-5)))} \item \code{options(marginaleffects_numDeriv = NULL)} } See the "Standard Errors and Confidence Intervals" vignette on the \code{marginaleffects} website for more details on the computation of standard errors: https://marginaleffects.com/vignettes/uncertainty.html Note that the \code{inferences()} function can be used to compute uncertainty estimates using a bootstrap or simulation-based inference. See the vignette: https://marginaleffects.com/vignettes/bootstrap.html } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \section{comparison argument functions}{ The following transformations can be applied by supplying one of the shortcut strings to the \code{comparison} argument. \code{hi} is a vector of adjusted predictions for the "high" side of the contrast. \code{lo} is a vector of adjusted predictions for the "low" side of the contrast. \code{y} is a vector of adjusted predictions for the original data. \code{x} is the predictor in the original data. \code{eps} is the step size to use to compute derivatives and elasticities.\tabular{ll}{ Shortcut \tab Function \cr difference \tab \(hi, lo) hi - lo \cr differenceavg \tab \(hi, lo) mean(hi - lo) \cr dydx \tab \(hi, lo, eps) (hi - lo)/eps \cr eyex \tab \(hi, lo, eps, y, x) (hi - lo)/eps * (x/y) \cr eydx \tab \(hi, lo, eps, y, x) ((hi - lo)/eps)/y \cr dyex \tab \(hi, lo, eps, x) ((hi - lo)/eps) * x \cr dydxavg \tab \(hi, lo, eps) mean((hi - lo)/eps) \cr eyexavg \tab \(hi, lo, eps, y, x) mean((hi - lo)/eps * (x/y)) \cr eydxavg \tab \(hi, lo, eps, y, x) mean(((hi - lo)/eps)/y) \cr dyexavg \tab \(hi, lo, eps, x) mean(((hi - lo)/eps) * x) \cr ratio \tab \(hi, lo) hi/lo \cr ratioavg \tab \(hi, lo) mean(hi)/mean(lo) \cr lnratio \tab \(hi, lo) log(hi/lo) \cr lnratioavg \tab \(hi, lo) log(mean(hi)/mean(lo)) \cr lnor \tab \(hi, lo) log((hi/(1 - hi))/(lo/(1 - lo))) \cr lnoravg \tab \(hi, lo) log((mean(hi)/(1 - mean(hi)))/(mean(lo)/(1 - mean(lo)))) \cr lift \tab \(hi, lo) (hi - lo)/lo \cr liftavg \tab \(hi, lo) (mean(hi - lo))/mean(lo) \cr expdydx \tab \(hi, lo, eps) ((exp(hi) - exp(lo))/exp(eps))/eps \cr expdydxavg \tab \(hi, lo, eps) mean(((exp(hi) - exp(lo))/exp(eps))/eps) \cr } } \section{Bayesian posterior summaries}{ By default, credible intervals in bayesian models are built as equal-tailed intervals. This can be changed to a highest density interval by setting a global option: \code{options("marginaleffects_posterior_interval" = "eti")} \code{options("marginaleffects_posterior_interval" = "hdi")} By default, the center of the posterior distribution in bayesian models is identified by the median. Users can use a different summary function by setting a global option: \code{options("marginaleffects_posterior_center" = "mean")} \code{options("marginaleffects_posterior_center" = "median")} When estimates are averaged using the \code{by} argument, the \code{tidy()} function, or the \code{summary()} function, the posterior distribution is marginalized twice over. First, we take the average \emph{across} units but \emph{within} each iteration of the MCMC chain, according to what the user requested in \code{by} argument or \code{tidy()/summary()} functions. Then, we identify the center of the resulting posterior using the function supplied to the \code{"marginaleffects_posterior_center"} option (the median by default). } \section{Equivalence, Inferiority, Superiority}{ \eqn{\theta} is an estimate, \eqn{\sigma_\theta} its estimated standard error, and \eqn{[a, b]} are the bounds of the interval supplied to the \code{equivalence} argument. Non-inferiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \leq a}{\theta <= a} \item \eqn{H_1}{H1}: \eqn{\theta > a} \item \eqn{t=(\theta - a)/\sigma_\theta}{t=(\theta - a)/\sigma_\theta} \item p: Upper-tail probability } Non-superiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \geq b}{\theta >= b} \item \eqn{H_1}{H1}: \eqn{\theta < b} \item \eqn{t=(\theta - b)/\sigma_\theta}{t=(\theta - b)/\sigma_\theta} \item p: Lower-tail probability } Equivalence: Two One-Sided Tests (TOST) \itemize{ \item p: Maximum of the non-inferiority and non-superiority p values. } Thanks to Russell V. Lenth for the excellent \code{emmeans} package and documentation which inspired this feature. } \section{Prediction types}{ The \code{type} argument determines the scale of the predictions used to compute quantities of interest with functions from the \code{marginaleffects} package. Admissible values for \code{type} depend on the model object. When users specify an incorrect value for \code{type}, \code{marginaleffects} will raise an informative error with a list of valid \code{type} values for the specific model object. The first entry in the list in that error message is the default type. The \code{invlink(link)} is a special type defined by \code{marginaleffects}. It is available for some (but not all) models and functions. With this link type, we first compute predictions on the link scale, then we use the inverse link function to backtransform the predictions to the response scale. This is useful for models with non-linear link functions as it can ensure that confidence intervals stay within desirable bounds, ex: 0 to 1 for a logit model. Note that an average of estimates with \code{type="invlink(link)"} will not always be equivalent to the average of estimates with \code{type="response"}. Some of the most common \code{type} values are: response, link, E, Ep, average, class, conditional, count, cum.prob, cumhaz, cumprob, density, detection, disp, ev, expected, expvalue, fitted, hazard, invlink(link), latent, latent_N, linear, linear.predictor, linpred, location, lp, mean, numeric, p, ppd, pr, precision, prediction, prob, probability, probs, quantile, risk, rmst, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob } \section{Order of operations}{ Behind the scenes, the arguments of \code{marginaleffects} functions are evaluated in this order: \enumerate{ \item \code{newdata} \item \code{variables} \item \code{comparison} and \code{slopes} \item \code{by} \item \code{vcov} \item \code{hypothesis} \item \code{transform} } } \section{Parallel computation}{ The \code{slopes()} and \code{comparisons()} functions can use parallelism to speed up computation. Operations are parallelized for the computation of standard errors, at the model coefficient level. There is always considerable overhead when using parallel computation, mainly involved in passing the whole dataset to the different processes. Thus, parallel computation is most likely to be useful when the model includes many parameters and the dataset is relatively small. Warning: In many cases, parallel processing will not be useful at all. To activate parallel computation, users must load the \code{future.apply} package, call \code{plan()} function, and set a global option. For example: \if{html}{\out{
}}\preformatted{library(future.apply) plan("multicore", workers = 4) options(marginaleffects_parallel = TRUE) slopes(model) }\if{html}{\out{
}} To disable parallelism in \code{marginaleffects} altogether, you can set a global option: \if{html}{\out{
}}\preformatted{options(marginaleffects_parallel = FALSE) }\if{html}{\out{
}} } \section{Global options}{ The behavior of \code{marginaleffects} functions can be modified by setting global options. Disable some safety checks: \if{html}{\out{
}}\preformatted{options(marginaleffects_safe = FALSE)` }\if{html}{\out{
}} } \examples{ \dontshow{if (interactive() || isTRUE(Sys.getenv("R_DOC_BUILD") == "true")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} \dontshow{\}) # examplesIf} library(marginaleffects) # Linear model tmp <- mtcars tmp$am <- as.logical(tmp$am) mod <- lm(mpg ~ am + factor(cyl), tmp) avg_comparisons(mod, variables = list(cyl = "reference")) avg_comparisons(mod, variables = list(cyl = "sequential")) avg_comparisons(mod, variables = list(cyl = "pairwise")) # GLM with different scale types mod <- glm(am ~ factor(gear), data = mtcars) avg_comparisons(mod, type = "response") avg_comparisons(mod, type = "link") # Contrasts at the mean comparisons(mod, newdata = "mean") # Contrasts between marginal means comparisons(mod, newdata = "marginalmeans") # Contrasts at user-specified values comparisons(mod, newdata = datagrid(am = 0, gear = tmp$gear)) comparisons(mod, newdata = datagrid(am = unique, gear = max)) m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars) comparisons(m, variables = "hp", newdata = datagrid(FUN_factor = unique, FUN_numeric = median)) # Numeric contrasts mod <- lm(mpg ~ hp, data = mtcars) avg_comparisons(mod, variables = list(hp = 1)) avg_comparisons(mod, variables = list(hp = 5)) avg_comparisons(mod, variables = list(hp = c(90, 100))) avg_comparisons(mod, variables = list(hp = "iqr")) avg_comparisons(mod, variables = list(hp = "sd")) avg_comparisons(mod, variables = list(hp = "minmax")) # using a function to specify a custom difference in one regressor dat <- mtcars dat$new_hp <- 49 * (dat$hp - min(dat$hp)) / (max(dat$hp) - min(dat$hp)) + 1 modlog <- lm(mpg ~ log(new_hp) + factor(cyl), data = dat) fdiff <- \(x) data.frame(x, x + 10) avg_comparisons(modlog, variables = list(new_hp = fdiff)) # Adjusted Risk Ratio: see the contrasts vignette mod <- glm(vs ~ mpg, data = mtcars, family = binomial) avg_comparisons(mod, comparison = "lnratioavg", transform = exp) # Adjusted Risk Ratio: Manual specification of the `comparison` avg_comparisons( mod, comparison = function(hi, lo) log(mean(hi) / mean(lo)), transform = exp) # cross contrasts mod <- lm(mpg ~ factor(cyl) * factor(gear) + hp, data = mtcars) avg_comparisons(mod, variables = c("cyl", "gear"), cross = TRUE) # variable-specific contrasts avg_comparisons(mod, variables = list(gear = "sequential", hp = 10)) # hypothesis test: is the `hp` marginal effect at the mean equal to the `drat` marginal effect mod <- lm(mpg ~ wt + drat, data = mtcars) comparisons( mod, newdata = "mean", hypothesis = "wt = drat") # same hypothesis test using row indices comparisons( mod, newdata = "mean", hypothesis = "b1 - b2 = 0") # same hypothesis test using numeric vector of weights comparisons( mod, newdata = "mean", hypothesis = c(1, -1)) # two custom contrasts using a matrix of weights lc <- matrix(c( 1, -1, 2, 3), ncol = 2) comparisons( mod, newdata = "mean", hypothesis = lc) # Effect of a 1 group-wise standard deviation change # First we calculate the SD in each group of `cyl` # Second, we use that SD as the treatment size in the `variables` argument library(dplyr) mod <- lm(mpg ~ hp + factor(cyl), mtcars) tmp <- mtcars \%>\% group_by(cyl) \%>\% mutate(hp_sd = sd(hp)) avg_comparisons(mod, variables = list(hp = function(x) data.frame(x, x + tmp$hp_sd)), by = "cyl") # `by` argument mod <- lm(mpg ~ hp * am * vs, data = mtcars) comparisons(mod, by = TRUE) mod <- lm(mpg ~ hp * am * vs, data = mtcars) avg_comparisons(mod, variables = "hp", by = c("vs", "am")) library(nnet) mod <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE) by <- data.frame( group = c("3", "4", "5"), by = c("3,4", "3,4", "5")) comparisons(mod, type = "probs", by = by) } \references{ \itemize{ \item Greenland S. 2019. "Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values." The American Statistician. 73(S1): 106–114. \item Cole, Stephen R, Jessie K Edwards, and Sander Greenland. 2020. "Surprise!" American Journal of Epidemiology 190 (2): 191–93. https://doi.org/10.1093/aje/kwaa136 } } marginaleffects/man/plot_comparisons.Rd0000644000176200001440000002351614631452336020057 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_comparisons.R \name{plot_comparisons} \alias{plot_comparisons} \title{Plot Conditional or Marginal Comparisons} \usage{ plot_comparisons( 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, ... ) } \arguments{ \item{model}{Model object} \item{variables}{Name of the variable whose contrast we want to plot on the y-axis.} \item{condition}{Conditional slopes \itemize{ \item Character vector (max length 4): Names of the predictors to display. \item Named list (max length 4): List names correspond to predictors. List elements can be: \itemize{ \item Numeric vector \item Function which returns a numeric vector or a set of unique categorical values \item Shortcut strings for common reference values: "minmax", "quartile", "threenum" } \item 1: x-axis. 2: color/shape. 3: facet (wrap if no fourth variable, otherwise cols of grid). 4: facet (rows of grid). \item Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers \code{?stats::fivenum}. }} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} \item{newdata}{When \code{newdata} is \code{NULL}, the grid is determined by the \code{condition} argument. When \code{newdata} is not \code{NULL}, the argument behaves in the same way as in the \code{comparisons()} function.} \item{type}{string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When \code{type} is \code{NULL}, the first entry in the error message is used by default.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{comparison}{How should pairs of predictions be compared? Difference, ratio, odds ratio, or user-defined functions. \itemize{ \item string: shortcuts to common contrast functions. \itemize{ \item Supported shortcuts strings: difference, differenceavg, differenceavgwts, dydx, eyex, eydx, dyex, dydxavg, eyexavg, eydxavg, dyexavg, dydxavgwts, eyexavgwts, eydxavgwts, dyexavgwts, ratio, ratioavg, ratioavgwts, lnratio, lnratioavg, lnratioavgwts, lnor, lnoravg, lnoravgwts, lift, liftavg, expdydx, expdydxavg, expdydxavgwts \item See the Comparisons section below for definitions of each transformation. } \item function: accept two equal-length numeric vectors of adjusted predictions (\code{hi} and \code{lo}) and returns a vector of contrasts of the same length, or a unique numeric value. \itemize{ \item See the Transformations section below for examples of valid functions. } }} \item{transform}{string or function. Transformation applied to unit-level estimates and confidence intervals just before the function returns results. Functions must accept a vector and return a vector of the same length. Support string shortcuts: "exp", "ln"} \item{rug}{TRUE displays tick marks on the axes to mark the distribution of raw data.} \item{gray}{FALSE grayscale or color plot} \item{draw}{\code{TRUE} returns a \code{ggplot2} plot. \code{FALSE} returns a \code{data.frame} of the underlying data.} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \value{ A \code{ggplot2} object } \description{ Plot comparisons on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets). The \code{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 \code{by} argument in the \code{comparisons()} function. The \code{condition} argument is used to plot conditional comparisons, that is, comparisons made on a user-specified grid. This is analogous to using the \code{newdata} argument and \code{datagrid()} function in a \code{comparisons()} call. All variables whose values are not specified explicitly are treated as usual by \code{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 \code{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: \itemize{ \item https://marginaleffects.com/vignettes/plot.html \item https://marginaleffects.com } } \section{Model-Specific Arguments}{ Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts. Please report other package-specific \code{predict()} arguments on Github so we can add them to the table below. https://github.com/vincentarelbundock/marginaleffects/issues\tabular{llll}{ Package \tab Class \tab Argument \tab Documentation \cr \code{brms} \tab \code{brmsfit} \tab \code{ndraws} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \tab \tab \code{re_formula} \tab \link[brms:posterior_predict.brmsfit]{brms::posterior_predict} \cr \code{lme4} \tab \code{merMod} \tab \code{re.form} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \tab \tab \code{allow.new.levels} \tab \link[lme4:predict.merMod]{lme4::predict.merMod} \cr \code{glmmTMB} \tab \code{glmmTMB} \tab \code{re.form} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{allow.new.levels} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \tab \tab \code{zitype} \tab \link[glmmTMB:predict.glmmTMB]{glmmTMB::predict.glmmTMB} \cr \code{mgcv} \tab \code{bam} \tab \code{exclude} \tab \link[mgcv:predict.bam]{mgcv::predict.bam} \cr \code{robustlmm} \tab \code{rlmerMod} \tab \code{re.form} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \tab \tab \code{allow.new.levels} \tab \link[robustlmm:rlmerMod-class]{robustlmm::predict.rlmerMod} \cr \code{MCMCglmm} \tab \code{MCMCglmm} \tab \code{ndraws} \tab \cr } } \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")) } marginaleffects/man/figures/0000755000176200001440000000000014631452336015632 5ustar liggesusersmarginaleffects/man/figures/zoo_header.png0000644000176200001440000157144314631452336020476 0ustar liggesusersPNG  IHDRo%zTXtRaw profile type exifxmQ[ =aqLbfz($M2eF@+.}{xuaHi\r&Xb᪎Iz8_s{kja xĽ XͲ< D8T" K^{-;`n@lPIH3%O7ӆ&Ko/cOzTXtRaw profile type iptcx,(IR# .c #K D4dCD 07@H.+`Iy:iCCPICC profilex}=H@_SKEvP" EjVL.& IZpc⬫ ~8;8)HK -b=8ǻ{wP/3nD\dW+EeIR=||jbO$1ô7g6m>qex¤ ?r]segtj8L,XicV45i∪/d >-8bKGD pHYs.#.#x?vtIME #b IDATxy-]of@ DrhCH%$ F$&H*6')\v\H`A@(SN*qbUXa+S1 E2bhf}~>UާO?{%,JRթ:uռڽNUNXU%uJ:Iݼ[ApnmfvBDp\;SN`UEA8nUkgU !y3a]_c}`ssV׹j1 j]ݑqO;_=O?Ktpu\yg?{{>`>ue1QYy!:uU; ή9J˹j1Z@XwfVAp{:G \joK_m7#;F@.UI}Ym,1+;g>=ɻ[3ɪy;lَlk_g:.K`egK?0`2CQ_2Ypi/gݹmcѭv'7;;Ekl*v4\vｫp;-ߴ6;p\Xr qlw_|ד<7ɯe^ rAphKcr;Yo,@S[عz^nus}Vm}d9؎ۛ` * y 44Osϭ֝Ͻ!7gG~Jדz:C.Ywe3%N{w&M;1Qr,p¦8LպYn 6aU|N6Ǧ6|XN%]ځroy{+Wxpt/[JwJSyb1h o˳Ƕ`gz[w}ecM-\wPO$vUdol=~y񏶖C۾T{//'o\7+8gGX)˕)(ԭ6 $PY?oofܲss>#>EjNϻ|Jk{w-CN_?}1mɌyCZeZur׫S՜YTzd=\g~IT8< IfW`wtu.(PhhW|Zy>mFܞ wv׭wO*?;76}v}D4cP9j${Zsw?cL;WO7SmR<5& f5vٻZϙN\Dp19`߲I;}wwo_SϾ%iae}y2vKl2;к,]΃9+[|yʾNZr6Xrn%]rI~ʔwp螛FMXI~cka4١gp*~ueќ`~cʭ AW9;F{VLDs!wgsmf]'48N ͗΃ji{򡩆s;g-54caG[ۖV>wλ`aib9I=w6f-J.X_+In4sMjg34wݝ3gyv77ٿ/in,Is׫}˹`;$Yֳ&>_e٬Nҗn4hN b&yXa3?zE3.X!ˀ-gnJc%S}Gp97hs?twL|gR}3[[%fu7'Z2.A l_ɨ ΃0yM);d? {>(Si$zZ٧ohRrbGyq=*1v0ίOד:׾c7 H$YGi=~~w4! 啔גVCQN&e#Q*ad@A>Pr"{n#,˵bwKpzp:uM6v7ϕ{mgoKYrמpߵl0m$}<q<E ii*-2i/ngpaY'flz,X?(, 89QUO}^MuN2=6 x}Ay~OC9 ~[h] wd$W'==k^I9}%MsxdnyM:E -$٦u`Wi;ж,L;sӒ.?+9S X%yUuI^Yc$gKcy>ćCu<|I=8ŒXVi?;Pp}\T,r(sTz*-9[7ciT.S=nN=hzw 3[uh_;{UPY?fm{srbqY6ɱc@t;C~0mRO=|{<;tQQs:Ny+.9==k:s|imEvaz3Kf:|NS=P'!@>:j N^ [cD!>~nkI|o(\oqh ,$eӦw FM MJ:{gMɂ%~~Ʃ*emxeo}\LuMk{SnvZ|Hڛf˶:$ 7sl6e2f*p~v߼8U=f |/η78J3թzO:Z}>^s쾩󛔿uO~)k:qBil!syWg&y7-{r+3]&q#1NceIzîM5@3a`< W1yP.M 2uQy$m- tݮ.T](u쾎s~{ w6Y~o塍7`fc`=dc)9mud3gӒk}YusݡwJ:'uIՌ4-ٖAsw+;^Kp|{ 4 ̉wޘ[g/oܾyeNֶΉK[\|N23U) qܝȔ6Hk$:zvGB*eN̼ajƉS}g-%ympuoϜ׭ {f3ֳkt~Jւ֒UR+<º/l3CQ>V=|y;}|8stWK߶.ixiwlQ}}H27)[S~)gM%:p:Lms_$? ۙ 8( K* C'W-4{O %6ಥ%ikJq9pu}JV9J\isIޑIXPW(V$͎cg{|w3c͡9䝭uzas;D|hEɐ;k}oܛ/tꄫ$S56:~YKGΣkIGlomucg|m|d}t[U'Oss:q;7tsgցmzd]֤ի9º3 b9yJ_N>n/.'Տߩ' 3<(HIGMⰡs9d4[8JۜϘV.w~Q=r&>Ұm\Ґb )U|SRSSs+ߡ-`xunȾE0Ռ,;n^ze?i?o\X{9o0O$ߜM)ݥh'ͱdFܓO9F4\I0ܝO9q6͔;Y\򸳔aC䞉ܜuf1_h'^AS8DgNםXUFwyy"$ĜsGb+Ios_ۆ Op\1%'3*ޝseN7 fdhf췜{M_U[^ټwv&k[zN]u5D]:GI:Xz5>O;ǯN},w,*{˕c'$@A^g<2?b>ouY] ?C.AuUO B2ζ7zÙA[z*@?-+ի`ju⍡V'bHˣ%ʴj`YQso^Xl,$o-_2Z͋1:WEEI^\Չ7]}sQK?ۓICɗ p׋fA[nKpOIG;/ͩH ש n;ժ-;-i/À;'d3xOFbMzcyܟI򡙿P-E2.D(?#Ϝ>QiCN,='3y۝Dlc۪ Ҏݴc}jSzfKwV#{οq]/ޜxxg$N7@ycg*]+?ڞ)k=>^`k×||w[ڼ$Wgޓ[;=N | IDAT@})PߕNc]#0V M/uzvsc:QF_)n kvYJz"d;3߿/}D~@7>^ɲN|}߻YtbmhGnv\ tcSM<51pG2PzmML߫c~_y=u9q~8o(ĽyՌs}:y9vc_NE.K*e׉2?,/c<Ϊ?NWwWSm.+͎şץקyܘ8*81ه˗U|)9gۗ_ug*ؤGTuǡuP/Y׫ ~},oo(v ( f d5pP>hl|a*v. 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a string equation; string; a formula, or a function. \itemize{ \item Numeric: \itemize{ \item Single value: the null hypothesis used in the computation of Z and p (before applying \code{transform}). \item Vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the \code{hypothesis} argument. \item Matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. } \item String equation to specify linear or non-linear hypothesis tests. If the \code{term} column uniquely identifies rows, terms can be used in the formula. Otherwise, use \code{b1}, \code{b2}, etc. to identify the position of each parameter. The \verb{b*} wildcard can be used to test hypotheses on all estimates. If a named vector is used, the names are used as labels in the output. Examples: \itemize{ \item \code{hp = drat} \item \code{hp + drat = 12} \item \code{b1 + b2 + b3 = 0} \item \verb{b* / b1 = 1} } \item String: \itemize{ \item "pairwise": pairwise differences between estimates in each row. \item "reference": differences between the estimates in each row and the estimate in the first row. \item "sequential": difference between an estimate and the estimate in the next row. \item "meandev": difference between an estimate and the mean of all estimates. \item "meanotherdev": difference between an estimate and the mean of all other estimates, excluding the current one. \item "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. } \item Formula: \itemize{ \item \code{comparison ~ pairs | group} \item Left-hand side determines the type of comparison to conduct: \code{difference} or \code{ratio}. If the left-hand side is empty, \code{difference} is chosen. \item Right-hand side determines the pairs of estimates to compare: \code{reference}, \code{sequential}, or \code{meandev} \item Optional: Users can supply grouping variables after a vertical bar to conduct comparisons withing subsets. \item Examples: \itemize{ \item \code{~ reference} \item \code{ratio ~ pairwise} \item \code{difference ~ pairwise | groupid} } } \item Function: \itemize{ \item Accepts an argument \code{x}: object produced by a \code{marginaleffects} function or a data frame with column \code{rowid} and \code{estimate} \item Returns a data frame with columns \code{term} and \code{estimate} (mandatory) and \code{rowid} (optional). \item The function can also accept optional input arguments: \code{newdata}, \code{by}, \code{draws}. \item This function approach will not work for Bayesian models or with bootstrapping. In those cases, it is easy to use \code{posterior_draws()} to extract and manipulate the draws directly. } \item See the Examples section below and the vignette: https://marginaleffects.com/vignettes/hypothesis.html }} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{conf_level}{numeric value between 0 and 1. Confidence level to use to build a confidence interval.} \item{df}{Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and \code{Inf}. When using \code{joint_test="f"}, the \code{df} argument should be a numeric vector of length 2.} \item{equivalence}{Numeric vector of length 2: bounds used for the two-one-sided test (TOST) of equivalence, and for the non-inferiority and non-superiority tests. See Details section below.} \item{joint}{Joint test of statistical significance. The null hypothesis value can be set using the \code{hypothesis} argument. \itemize{ \item FALSE: Hypotheses are not tested jointly. \item TRUE: All parameters are tested jointly. \item String: A regular expression to match parameters to be tested jointly. \code{grep(joint, perl = TRUE)} \item Character vector of parameter names to be tested. Characters refer to the names of the vector returned by \code{coef(object)}. \item Integer vector of indices. Which parameters positions to test jointly. }} \item{joint_test}{A character string specifying the type of test, either "f" or "chisq". The null hypothesis is set by the \code{hypothesis} argument, with default null equal to 0 for all parameters.} \item{numderiv}{string or list of strings indicating the method to use to for the numeric differentiation used in to compute delta method standard errors. \itemize{ \item "fdforward": finite difference method with forward differences \item "fdcenter": finite difference method with central differences (default) \item "richardson": Richardson extrapolation method \item Extra arguments can be specified by passing a list to the \code{numDeriv} argument, with the name of the method first and named arguments following, ex: \code{numderiv=list("fdcenter", eps = 1e-5)}. When an unknown argument is used, \code{marginaleffects} prints the list of valid arguments for each method. }} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} } \description{ Uncertainty estimates are calculated as first-order approximate standard errors for linear or non-linear functions of a vector of random variables with known or estimated covariance matrix. In that sense, \code{\link{hypotheses}} emulates the behavior of the excellent and well-established \link[car:deltaMethod]{car::deltaMethod} and \link[car:linearHypothesis]{car::linearHypothesis} functions, but it supports more models; requires fewer dependencies; expands the range of tests to equivalence and superiority/inferiority; and offers convenience features like robust standard errors. To learn more, read the hypothesis tests vignette, visit the package website, or scroll down this page for a full list of vignettes: \itemize{ \item \url{https://marginaleffects.com/vignettes/hypothesis.html} \item \url{https://marginaleffects.com/} } Warning #1: Tests are conducted directly on the scale defined by the \code{type} argument. For some models, it can make sense to conduct hypothesis or equivalence tests on the \code{"link"} scale instead of the \code{"response"} scale which is often the default. Warning #2: For hypothesis tests on objects produced by the \code{marginaleffects} package, it is safer to use the \code{hypothesis} argument of the original function. Using \code{hypotheses()} may not work in certain environments, in lists, or when working programmatically with *apply style functions. Warning #3: The tests assume that the \code{hypothesis} expression is (approximately) normally distributed, which for non-linear functions of the parameters may not be realistic. More reliable confidence intervals can be obtained using the \code{inferences()} function with \code{method = "boot"}. } \section{Joint hypothesis tests}{ The test statistic for the joint Wald test is calculated as (R * theta_hat - r)' * inv(R * V_hat * R') * (R * theta_hat - r) / Q, where theta_hat is the vector of estimated parameters, V_hat is the estimated covariance matrix, R is a Q x P matrix for testing Q hypotheses on P parameters, r is a Q x 1 vector for the null hypothesis, and Q is the number of rows in R. If the test is a Chi-squared test, the test statistic is not normalized. The p-value is then calculated based on either the F-distribution (for F-test) or the Chi-squared distribution (for Chi-squared test). For the F-test, the degrees of freedom are Q and (n - P), where n is the sample size and P is the number of parameters. For the Chi-squared test, the degrees of freedom are Q. } \section{Equivalence, Inferiority, Superiority}{ \eqn{\theta} is an estimate, \eqn{\sigma_\theta} its estimated standard error, and \eqn{[a, b]} are the bounds of the interval supplied to the \code{equivalence} argument. Non-inferiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \leq a}{\theta <= a} \item \eqn{H_1}{H1}: \eqn{\theta > a} \item \eqn{t=(\theta - a)/\sigma_\theta}{t=(\theta - a)/\sigma_\theta} \item p: Upper-tail probability } Non-superiority: \itemize{ \item \eqn{H_0}{H0}: \eqn{\theta \geq b}{\theta >= b} \item \eqn{H_1}{H1}: \eqn{\theta < b} \item \eqn{t=(\theta - b)/\sigma_\theta}{t=(\theta - b)/\sigma_\theta} \item p: Lower-tail probability } Equivalence: Two One-Sided Tests (TOST) \itemize{ \item p: Maximum of the non-inferiority and non-superiority p values. } Thanks to Russell V. Lenth for the excellent \code{emmeans} package and documentation which inspired this feature. } \examples{ library(marginaleffects) mod <- lm(mpg ~ hp + wt + factor(cyl), data = mtcars) hypotheses(mod) # Test of equality between coefficients hypotheses(mod, hypothesis = "hp = wt") # Non-linear function hypotheses(mod, hypothesis = "exp(hp + wt) = 0.1") # Robust standard errors hypotheses(mod, hypothesis = "hp = wt", vcov = "HC3") # b1, b2, ... shortcuts can be used to identify the position of the # parameters of interest in the output of hypotheses(mod, hypothesis = "b2 = b3") # wildcard hypotheses(mod, hypothesis = "b* / b2 = 1") # term names with special characters have to be enclosed in backticks hypotheses(mod, hypothesis = "`factor(cyl)6` = `factor(cyl)8`") mod2 <- lm(mpg ~ hp * drat, data = mtcars) hypotheses(mod2, hypothesis = "`hp:drat` = drat") # predictions(), comparisons(), and slopes() mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial) cmp <- comparisons(mod, newdata = "mean") hypotheses(cmp, hypothesis = "b1 = b2") mfx <- slopes(mod, newdata = "mean") hypotheses(cmp, hypothesis = "b2 = 0.2") pre <- predictions(mod, newdata = datagrid(hp = 110, mpg = c(30, 35))) hypotheses(pre, hypothesis = "b1 = b2") # The `hypothesis` argument can be used to compute standard errors for fitted values mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial) f <- function(x) predict(x, type = "link", newdata = mtcars) p <- hypotheses(mod, hypothesis = f) head(p) f <- function(x) predict(x, type = "response", newdata = mtcars) p <- hypotheses(mod, hypothesis = f) head(p) # Complex aggregation # Step 1: Collapse predicted probabilities by outcome level, for each individual # Step 2: Take the mean of the collapsed probabilities by group and `cyl` library(dplyr) library(MASS) library(dplyr) dat <- transform(mtcars, gear = factor(gear)) mod <- polr(gear ~ factor(cyl) + hp, dat) aggregation_fun <- function(x) { predictions(x, vcov = FALSE) |> mutate(group = ifelse(group \%in\% c("3", "4"), "3 & 4", "5")) |> summarize(estimate = sum(estimate), .by = c("rowid", "cyl", "group")) |> summarize(estimate = mean(estimate), .by = c("cyl", "group")) |> rename(term = cyl) } hypotheses(mod, hypothesis = aggregation_fun) # Equivalence, non-inferiority, and non-superiority tests mod <- lm(mpg ~ hp + factor(gear), data = mtcars) p <- predictions(mod, newdata = "median") hypotheses(p, equivalence = c(17, 18)) mfx <- avg_slopes(mod, variables = "hp") hypotheses(mfx, equivalence = c(-.1, .1)) cmp <- avg_comparisons(mod, variables = "gear", hypothesis = "pairwise") hypotheses(cmp, equivalence = c(0, 10)) # joint hypotheses: character vector model <- lm(mpg ~ as.factor(cyl) * hp, data = mtcars) hypotheses(model, joint = c("as.factor(cyl)6:hp", "as.factor(cyl)8:hp")) # joint hypotheses: regular expression hypotheses(model, joint = "cyl") # joint hypotheses: integer indices hypotheses(model, joint = 2:3) # joint hypotheses: different null hypotheses hypotheses(model, joint = 2:3, hypothesis = 1) hypotheses(model, joint = 2:3, hypothesis = 1:2) # joint hypotheses: marginaleffects object cmp <- avg_comparisons(model) hypotheses(cmp, joint = "cyl") } marginaleffects/man/marginal_means.Rd0000644000176200001440000000100614631452336017427 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{marginal_means} \alias{marginal_means} \title{Deprecated function} \usage{ marginal_means( model, variables = NULL, newdata = NULL, vcov = TRUE, conf_level = 0.95, type = NULL, transform = NULL, cross = FALSE, hypothesis = NULL, equivalence = NULL, p_adjust = NULL, df = Inf, wts = "equal", by = NULL, numderiv = "fdforward", ... ) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/datagridcf.Rd0000644000176200001440000000035514631452336016550 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{datagridcf} \alias{datagridcf} \title{Deprecated function} \usage{ datagridcf(...) } \description{ Deprecated function } \keyword{internal} marginaleffects/man/sanitize_model_specific.Rd0000644000176200001440000001631014632674174021340 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods_aod.R, R/methods_betareg.R, % R/sanity_model.R, R/methods_brms.R, R/methods_dbarts.R, R/methods_glmmTMB.R, % R/methods_lme4.R, R/methods_mclogit.R, R/methods_mlogit.R, % R/methods_ordinal.R, R/methods_plm.R, R/methods_quantreg.R, % R/methods_survey.R \name{sanitize_model_specific.glimML} \alias{sanitize_model_specific.glimML} \alias{sanitize_model_specific.betareg} \alias{sanitize_model_specific} \alias{sanitize_model_specific.default} \alias{sanitize_model_specific.brmsfit} \alias{sanitize_model_specific.bart} \alias{sanitize_model_specific.glmmTMB} \alias{sanitize_model_specific.merMod} \alias{sanitize_model_specific.mblogit} \alias{sanitize_model_specific.mlogit} \alias{sanitize_model_specific.clm} \alias{sanitize_model_specific.plm} \alias{sanitize_model_specific.rqs} \alias{sanitize_model_specific.svyolr} \alias{sanitize_model_specific.svyglm} \title{Method to raise model-specific warnings and errors} \usage{ \method{sanitize_model_specific}{glimML}(model, ...) \method{sanitize_model_specific}{betareg}(model, ...) sanitize_model_specific(model, ...) \method{sanitize_model_specific}{default}( model, vcov = NULL, calling_function = "marginaleffects", ... ) \method{sanitize_model_specific}{brmsfit}(model, ...) \method{sanitize_model_specific}{bart}(model, ...) \method{sanitize_model_specific}{glmmTMB}(model, vcov = TRUE, re.form = NULL, ...) \method{sanitize_model_specific}{merMod}(model, re.form = NULL, ...) \method{sanitize_model_specific}{mblogit}(model, calling_function = "marginaleffects", ...) \method{sanitize_model_specific}{mlogit}(model, newdata, ...) \method{sanitize_model_specific}{clm}(model, ...) \method{sanitize_model_specific}{plm}(model, ...) \method{sanitize_model_specific}{plm}(model, ...) \method{sanitize_model_specific}{rqs}(model, ...) \method{sanitize_model_specific}{svyolr}(model, wts = FALSE, by = FALSE, ...) \method{sanitize_model_specific}{svyglm}(model, wts = FALSE, by = FALSE, ...) } \arguments{ \item{model}{Model object} \item{...}{Additional arguments are passed to the \code{predict()} method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the \code{marginaleffects} website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the \code{?slopes} documentation for a non-exhaustive list of available arguments.} \item{vcov}{Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: \itemize{ \item FALSE: Do not compute standard errors. This can speed up computation considerably. \item TRUE: Unit-level standard errors using the default \code{vcov(model)} variance-covariance matrix. \item String which indicates the kind of uncertainty estimates to return. \itemize{ \item Heteroskedasticity-consistent: \code{"HC"}, \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, \code{"HC4m"}, \code{"HC5"}. See \code{?sandwich::vcovHC} \item Heteroskedasticity and autocorrelation consistent: \code{"HAC"} \item Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" \item Other: \code{"NeweyWest"}, \code{"KernHAC"}, \code{"OPG"}. See the \code{sandwich} package documentation. } \item One-sided formula which indicates the name of cluster variables (e.g., \code{~unit_id}). This formula is passed to the \code{cluster} argument of the \code{sandwich::vcovCL} function. \item Square covariance matrix \item Function which returns a covariance matrix (e.g., \code{stats::vcov(model)}) }} \item{newdata}{Grid of predictor values at which we evaluate the slopes. \itemize{ \item Warning: Please avoid modifying your dataset between fitting the model and calling a \code{marginaleffects} function. This can sometimes lead to unexpected results. \item \code{NULL} (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using \code{\link[insight:get_data]{insight::get_data()}}, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. \item \code{\link[=datagrid]{datagrid()}} call to specify a custom grid of regressors. For example: \itemize{ \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[=datagrid]{datagrid()}} documentation. } \item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} \item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Slopes evaluated when each predictor is held at its mean or mode. \item "median": Slopes evaluated when each predictor is held at its median or mode. \item "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. \item "tukey": Slopes evaluated at Tukey's 5 numbers. \item "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). } }} \item{wts}{logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in \verb{avg_*()} or with the \code{by} argument, and not unit-level estimates. See \code{?weighted.mean} \itemize{ \item string: column name of the weights variable in \code{newdata}. When supplying a column name to \code{wts}, it is recommended to supply the original data (including the weights variable) explicitly to \code{newdata}. \item numeric: vector of length equal to the number of rows in the original data or in \code{newdata} (if supplied). \item FALSE: Equal weights. \item TRUE: Extract weights from the fitted object with \code{insight::find_weights()} and use them when taking weighted averages of estimates. Warning: \code{newdata=datagrid()} returns a single average weight, which is equivalent to using \code{wts=FALSE} }} \item{by}{Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs: \itemize{ \item \code{FALSE}: return the original unit-level estimates. \item \code{TRUE}: aggregate estimates for each term. \item Character vector of column names in \code{newdata} or in the data frame produced by calling the function without the \code{by} argument. \item Data frame with a \code{by} column of group labels, and merging columns shared by \code{newdata} or the data frame produced by calling the same function without the \code{by} argument. \item See examples below. \item For more complex aggregations, you can use the \code{FUN} argument of the \code{hypotheses()} function. See that function's documentation and the Hypothesis Test vignettes on the \code{marginaleffects} website. }} } \value{ A warning, an error, or nothing } \description{ Method to raise model-specific warnings and errors } \keyword{internal} marginaleffects/man/print.marginaleffects.Rd0000644000176200001440000000507014631452336020744 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.R \name{print.marginaleffects} \alias{print.marginaleffects} \title{Print \code{marginaleffects} objects} \usage{ \method{print}{marginaleffects}( x, style = getOption("marginaleffects_print_style", default = "summary"), digits = getOption("marginaleffects_print_digits", default = 3), p_eps = getOption("marginaleffects_print_p_eps", default = 0.001), topn = getOption("marginaleffects_print_topn", default = 5), nrows = getOption("marginaleffects_print_nrows", default = 30), ncols = getOption("marginaleffects_print_ncols", default = 30), type = getOption("marginaleffects_print_type", default = TRUE), column_names = getOption("marginaleffects_print_column_names", default = TRUE), ... ) } \arguments{ \item{x}{An object produced by one of the \code{marginaleffects} package functions.} \item{style}{"summary", "data.frame", or "tinytable"} \item{digits}{The number of digits to display.} \item{p_eps}{p values smaller than this number are printed in "<0.001" style.} \item{topn}{The number of rows to be printed from the beginning and end of tables with more than \code{nrows} rows.} \item{nrows}{The number of rows which will be printed before truncation.} \item{ncols}{The maximum number of column names to display at the bottom of the printed output.} \item{type}{boolean: should the type be printed?} \item{column_names}{boolean: should the column names be printed?} \item{...}{Other arguments are currently ignored.} } \description{ This function controls the text which is printed to the console when one of the core \code{marginalefffects} functions is called and the object is returned: \code{predictions()}, \code{comparisons()}, \code{slopes()}, \code{hypotheses()}, \code{avg_predictions()}, \code{avg_comparisons()}, \code{avg_slopes()}. All of those functions return standard data frames. Columns can be extracted by name, \code{predictions(model)$estimate}, and all the usual data manipulation functions work out-of-the-box: \code{colnames()}, \code{head()}, \code{subset()}, \code{dplyr::filter()}, \code{dplyr::arrange()}, etc. Some of the data columns are not printed by default. You can disable pretty printing and print the full results as a standard data frame using the \code{style} argument or by applying \code{as.data.frame()} on the object. See examples below. } \examples{ library(marginaleffects) mod <- lm(mpg ~ hp + am + factor(gear), data = mtcars) p <- predictions(mod, by = c("am", "gear")) p subset(p, am == 1) print(p, style = "data.frame") data.frame(p) } marginaleffects/man/knit_print.marginaleffects.Rd0000644000176200001440000000065414631452336021774 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.R \name{knit_print.marginaleffects} \alias{knit_print.marginaleffects} \title{Print a marginaleffects object in knitr} \usage{ \method{knit_print}{marginaleffects}(x, ...) } \value{ A string with class \code{knit_asis} to be printed in Rmarkdown or Quarto documents. } \description{ Print a marginaleffects object in knitr } \keyword{internal} marginaleffects/DESCRIPTION0000644000176200001440000001322114632777612015130 0ustar liggesusersPackage: marginaleffects Title: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests Version: 0.21.0 Authors@R: c(person(given = "Vincent", family = "Arel-Bundock", role = c("aut", "cre", "cph"), email = "vincent.arel-bundock@umontreal.ca", comment = c(ORCID = "0000-0003-2042-7063")), person(given = "Noah", family = "Greifer", role = "ctb", email = "noah.greifer@gmail.com", comment = c(ORCID = "0000-0003-3067-7154")), person("Etienne", "Bacher", email = "etienne.bacher@protonmail.com", role = "ctb", comment = c(ORCID = "0000-0002-9271-5075"))) Description: Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. License: GPL (>= 3) Copyright: inst/COPYRIGHTS Encoding: UTF-8 URL: https://marginaleffects.com/ BugReports: https://github.com/vincentarelbundock/marginaleffects/issues RoxygenNote: 7.3.1 Depends: R (>= 3.6.0) Imports: checkmate, data.table, generics, insight (>= 0.19.7), methods, rlang, utils, Rcpp (>= 1.0.0) Enhances: knitr LinkingTo: Rcpp, RcppEigen Suggests: AER, Amelia, afex, aod, bench, betareg, BH, bife, biglm, blme, boot, brglm2, brms, brmsmargins, broom, car, carData, causaldata, clarify, cjoint, cobalt, collapse, conflicted, countrycode, covr, crch, DALEXtra, DCchoice, dbarts, distributional, dfidx, dplyr, emmeans, equivalence, estimatr, fixest, flexsurv, fmeffects, fontquiver, Formula, future, future.apply, fwb, gam, gamlss, gamlss.dist, geepack, ggdist, ggokabeito, ggplot2, ggrepel, glmmTMB, glmx, haven, here, itsadug, ivreg, kableExtra, lme4, lmerTest, logistf, magrittr, MatchIt, MASS, mclogit, MCMCglmm, missRanger, mgcv, mice, miceadds, mlogit, mlr3verse, modelbased, modelsummary, nlme, nnet, numDeriv, nycflights13, optmatch, ordinal, parameters, parsnip, partykit, patchwork, pkgdown, phylolm, plm, polspline, poorman, posterior, pscl, purrr, quantreg, Rchoice, REndo, rcmdcheck, remotes, reticulate, rmarkdown, rms, robust, robustbase, robustlmm, rsample, rstanarm, rstantools, rsvg, sampleSelection, sandwich, scam, spelling, speedglm, survey, survival, svglite, systemfonts, tibble, tictoc, tidymodels, tidyr, tidyverse, tinysnapshot, tinytable (>= 0.2.0), tinytest, titanic, truncreg, tsModel, withr, workflows, yaml, xgboost, testthat (>= 3.0.0), altdoc Collate: 'RcppExports.R' 'backtransform.R' 'bootstrap_boot.R' 'bootstrap_fwb.R' 'bootstrap_rsample.R' 'bootstrap_simulation.R' 'broom.R' 'by.R' 'ci.R' 'comparisons.R' 'complete_levels.R' 'conformal.R' 'datagrid.R' 'deprecated.R' 'equivalence.R' 'get_coef.R' 'get_contrast_data.R' 'get_contrast_data_character.R' 'get_contrast_data_factor.R' 'get_contrast_data_logical.R' 'get_contrast_data_numeric.R' 'get_contrasts.R' 'get_group_names.R' 'get_hypothesis.R' 'get_jacobian.R' 'get_model_matrix.R' 'get_model_matrix_attribute.R' 'get_modeldata.R' 'get_predict.R' 'get_se_delta.R' 'get_term_labels.R' 'get_vcov.R' 'github_issue.R' 'hush.R' 'hypotheses.R' 'hypotheses_joint.R' 'hypothesis_helper.R' 'imputation.R' 'inferences.R' 'mean_or_mode.R' 'methods.R' 'set_coef.R' 'methods_AER.R' 'methods_MASS.R' 'methods_MCMCglmm.R' 'methods_Rchoice.R' 'methods_afex.R' 'methods_aod.R' 'methods_betareg.R' 'methods_bife.R' 'methods_biglm.R' 'methods_nnet.R' 'methods_brglm2.R' 'sanity_model.R' 'methods_brms.R' 'methods_crch.R' 'methods_dataframe.R' 'methods_dbarts.R' 'methods_fixest.R' 'methods_flexsurv.R' 'methods_gamlss.R' 'methods_glmmTMB.R' 'methods_glmx.R' 'methods_lme4.R' 'methods_mclogit.R' 'methods_mgcv.R' 'methods_mhurdle.R' 'methods_mlm.R' 'methods_mlogit.R' 'methods_mlr3.R' 'methods_nlme.R' 'methods_ordinal.R' 'methods_plm.R' 'methods_pscl.R' 'methods_quantreg.R' 'methods_rms.R' 'methods_robustlmm.R' 'methods_rstanarm.R' 'methods_sampleSelection.R' 'methods_scam.R' 'methods_stats.R' 'methods_survey.R' 'methods_survival.R' 'methods_tidymodels.R' 'methods_tobit1.R' 'modelarchive.R' 'myTryCatch.R' 'package.R' 'plot.R' 'plot_build.R' 'plot_comparisons.R' 'plot_predictions.R' 'plot_slopes.R' 'posterior_draws.R' 'predictions.R' 'print.R' 'recall.R' 'sanitize_comparison.R' 'sanitize_condition.R' 'sanitize_conf_level.R' 'sanitize_hypothesis.R' 'sanitize_hypothesis_formula.R' 'sanitize_interaction.R' 'sanitize_newdata.R' 'sanitize_numderiv.R' 'sanitize_type.R' 'sanitize_variables.R' 'sanitize_vcov.R' 'sanity.R' 'sanity_by.R' 'sanity_dots.R' 'settings.R' 'slopes.R' 'sort.R' 'tinytest.R' 'type_dictionary.R' 'unpack_matrix_cols.R' 'utils.R' Language: en-US Config/testthat/edition: 3 NeedsCompilation: yes Packaged: 2024-06-14 00:57:04 UTC; vincent Author: Vincent Arel-Bundock [aut, cre, cph] (), Noah Greifer [ctb] (), Etienne Bacher [ctb] () Maintainer: Vincent Arel-Bundock Repository: CRAN Date/Publication: 2024-06-14 08:30:02 UTC