From ce16e7bbacebcbbc6465bab5fad834febd2bf587 Mon Sep 17 00:00:00 2001 From: Daniel Date: Wed, 22 May 2024 20:54:22 +0200 Subject: [PATCH 01/24] Rename `at` arguments into `by` https://github.com/easystats/easystats/issues/404 --- R/estimate_contrasts.R | 64 ++++++++++++---------- R/estimate_slopes.R | 64 +++++++++++----------- R/get_emmeans.R | 102 +++++++++++++++++++----------------- man/estimate_contrasts.Rd | 11 ++-- man/estimate_means.Rd | 5 -- man/estimate_slopes.Rd | 10 ++-- man/get_emmeans.Rd | 28 +++++----- man/get_marginaleffects.Rd | 5 -- man/visualisation_matrix.Rd | 50 +----------------- 9 files changed, 149 insertions(+), 190 deletions(-) diff --git a/R/estimate_contrasts.R b/R/estimate_contrasts.R index 2385c54a..8b3beda2 100644 --- a/R/estimate_contrasts.R +++ b/R/estimate_contrasts.R @@ -38,7 +38,7 @@ #' estimate_contrasts(model, fixed = "Petal.Width") #' #' # Or modulate it -#' estimate_contrasts(model, at = "Petal.Width", length = 4) +#' estimate_contrasts(model, by = "Petal.Width", length = 4) #' #' # Standardized differences #' estimated <- estimate_contrasts(lm(Sepal.Width ~ Species, data = iris)) @@ -67,24 +67,30 @@ #' model <- stan_glm(mpg ~ cyl * wt, data = data, refresh = 0) #' estimate_contrasts(model) #' estimate_contrasts(model, fixed = "wt") -#' estimate_contrasts(model, at = "wt", length = 4) +#' estimate_contrasts(model, by = "wt", length = 4) #' #' model <- stan_glm(Sepal.Width ~ Species + Petal.Width + Petal.Length, data = iris, refresh = 0) -#' estimate_contrasts(model, at = "Petal.Length", test = "bf") +#' estimate_contrasts(model, by = "Petal.Length", test = "bf") #' } #' #' @return A data frame of estimated contrasts. #' @export estimate_contrasts <- function(model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, transform = "none", ci = 0.95, p_adjust = "holm", method = "pairwise", adjust = NULL, + at = NULL, ...) { + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } + # Deprecation if (!is.null(adjust)) { insight::format_warning("The `adjust` argument is deprecated. Please write `p_adjust` instead.") @@ -94,7 +100,7 @@ estimate_contrasts <- function(model, # Run emmeans estimated <- get_emcontrasts(model, contrast = contrast, - at = at, + by = by, fixed = fixed, transform = transform, method = method, @@ -106,19 +112,18 @@ estimate_contrasts <- function(model, # Summarize and clean if (insight::model_info(model)$is_bayesian) { - contrasts <- bayestestR::describe_posterior(estimated, ci = ci, ...) - contrasts <- cbind(estimated@grid, contrasts) - contrasts <- .clean_names_bayesian(contrasts, model, transform, type = "contrast") + out <- cbind(estimated@grid, bayestestR::describe_posterior(estimated, ci = ci, ...)) + out <- .clean_names_bayesian(out, model, transform, type = "contrast") } else { - contrasts <- as.data.frame(merge( + out <- as.data.frame(merge( as.data.frame(estimated), stats::confint(estimated, level = ci, adjust = p_adjust) )) - contrasts <- .clean_names_frequentist(contrasts) + out <- .clean_names_frequentist(out) } - contrasts$null <- NULL # introduced in emmeans 1.6.1 (#115) - contrasts <- datawizard::data_relocate( - contrasts, + out$null <- NULL # introduced in emmeans 1.6.1 (#115) + out <- datawizard::data_relocate( + out, c("CI_low", "CI_high"), after = c("Difference", "Odds_ratio", "Ratio") ) @@ -126,38 +131,39 @@ estimate_contrasts <- function(model, # Format contrasts names # Split by either " - " or "/" - level_cols <- strsplit(as.character(contrasts$contrast), " - |\\/") + level_cols <- strsplit(as.character(out$contrast), " - |\\/") level_cols <- data.frame(do.call(rbind, lapply(level_cols, trimws))) names(level_cols) <- c("Level1", "Level2") level_cols$Level1 <- gsub(",", " - ", level_cols$Level1, fixed = TRUE) level_cols$Level2 <- gsub(",", " - ", level_cols$Level2, fixed = TRUE) # Merge levels and rest - contrasts$contrast <- NULL - contrasts <- cbind(level_cols, contrasts) + out$contrast <- NULL + out <- cbind(level_cols, out) # Table formatting - attr(contrasts, "table_title") <- c("Marginal Contrasts Analysis", "blue") - attr(contrasts, "table_footer") <- .estimate_means_footer( - contrasts, + attr(out, "table_title") <- c("Marginal Contrasts Analysis", "blue") + attr(out, "table_footer") <- .estimate_means_footer( + out, info$contrast, type = "contrasts", p_adjust = p_adjust ) # Add attributes - attr(contrasts, "model") <- model - attr(contrasts, "response") <- insight::find_response(model) - attr(contrasts, "ci") <- ci - attr(contrasts, "transform") <- transform - attr(contrasts, "at") <- info$at - attr(contrasts, "fixed") <- info$fixed - attr(contrasts, "contrast") <- info$contrast - attr(contrasts, "p_adjust") <- p_adjust + attr(out, "model") <- model + attr(out, "response") <- insight::find_response(model) + attr(out, "ci") <- ci + attr(out, "transform") <- transform + attr(out, "at") <- info$by + attr(out, "by") <- info$by + attr(out, "fixed") <- info$fixed + attr(out, "contrast") <- info$contrast + attr(out, "p_adjust") <- p_adjust # Output - class(contrasts) <- c("estimate_contrasts", "see_estimate_contrasts", class(contrasts)) - contrasts + class(out) <- c("estimate_contrasts", "see_estimate_contrasts", class(out)) + out } diff --git a/R/estimate_slopes.R b/R/estimate_slopes.R index 77bbaaad..d913a295 100644 --- a/R/estimate_slopes.R +++ b/R/estimate_slopes.R @@ -81,7 +81,7 @@ #' # Model it #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) #' # Compute the marginal effect of Petal.Length at each level of Species -#' slopes <- estimate_slopes(model, trend = "Petal.Length", at = "Species") +#' slopes <- estimate_slopes(model, trend = "Petal.Length", by = "Species") #' slopes #' #' # Plot it @@ -91,14 +91,14 @@ #' #' @examplesIf require("mgcv") && require("emmeans") && require("see") #' model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris) -#' slopes <- estimate_slopes(model, at = "Petal.Length", length = 50) +#' slopes <- estimate_slopes(model, by = "Petal.Length", length = 50) #' summary(slopes) #' plot(slopes) #' #' model <- mgcv::gam(Sepal.Width ~ s(Petal.Length, by = Species), data = iris) #' slopes <- estimate_slopes(model, #' trend = "Petal.Length", -#' at = c("Petal.Length", "Species"), length = 20 +#' by = c("Petal.Length", "Species"), length = 20 #' ) #' summary(slopes) #' plot(slopes) @@ -106,11 +106,16 @@ #' @export estimate_slopes <- function(model, trend = NULL, - at = NULL, + by = NULL, ci = 0.95, + at = NULL, ...) { + if (!is.null(at)) { + by <- at + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + } # Sanitize arguments - estimated <- get_emtrends(model, trend, at, ...) + estimated <- get_emtrends(model, trend, by, ...) info <- attributes(estimated) # Summarize and clean @@ -118,7 +123,7 @@ estimate_slopes <- function(model, trends <- parameters::parameters(estimated, ci = ci, ...) trends <- .clean_names_bayesian(trends, model, transform = "none", type = "trend") trends <- cbind(estimated@grid, trends) - trends$`.wgt.` <- NULL # Drop the weight column + trends[[".wgt."]] <- NULL # Drop the weight column } else { trends <- parameters::parameters(estimated, ci = ci, ...) } @@ -137,7 +142,8 @@ estimate_slopes <- function(model, attr(trends, "response") <- insight::find_response(model) attr(trends, "ci") <- ci attr(trends, "trend") <- info$trend - attr(trends, "at") <- info$at + attr(trends, "at") <- info$by + attr(trends, "by") <- info$by # Output class(trends) <- c("estimate_slopes_summary", "estimate_slopes", class(trends)) @@ -152,11 +158,11 @@ estimate_slopes <- function(model, #' @export summary.estimate_slopes <- function(object, ...) { - data <- as.data.frame(object) + my_data <- as.data.frame(object) trend <- attributes(object)$trend # Add "Confidence" col based on the sig index present in the data - data$Confidence <- .estimate_slopes_sig(data, ...) + my_data$Confidence <- .estimate_slopes_sig(my_data, ...) # Grouping variables vars <- attributes(object)$at @@ -164,14 +170,14 @@ summary.estimate_slopes <- function(object, ...) { # If no grouping variables, summarize all if (length(vars) == 0) { - out <- .estimate_slopes_summarize(data, trend = trend) + out <- .estimate_slopes_summarize(my_data, trend = trend) } else { out <- data.frame() # Create vizmatrix of grouping variables - groups <- as.data.frame(insight::get_datagrid(data[vars], factors = "all", numerics = "all")) + groups <- as.data.frame(insight::get_datagrid(my_data[vars], factors = "all", numerics = "all")) # Summarize all of the chunks for (i in seq_len(nrow(groups))) { - g <- datawizard::data_match(data, groups[i, , drop = FALSE]) + g <- datawizard::data_match(my_data, groups[i, , drop = FALSE]) out <- rbind(out, .estimate_slopes_summarize(g, trend = trend)) } out <- datawizard::data_relocate(out, vars) @@ -196,15 +202,15 @@ summary.estimate_slopes <- function(object, ...) { c("Coefficient", "Median", "Mean", "MAP_Estimate"), verbose = FALSE ) - signs <- sign(data[[centrality_columns]]) - sign <- signs[1] + centrality_signs <- sign(data[[centrality_columns]]) + centrality_sign <- centrality_signs[1] sig <- data$Confidence[1] starts <- 1 ends <- nrow(data) # Iterate through all rows to find blocks for (i in 2:nrow(data)) { - if ((data$Confidence[i] != sig) || ((signs[i] != sign) && data$Confidence[i] == "Uncertain")) { - sign <- signs[i] + if ((data$Confidence[i] != sig) || ((centrality_signs[i] != centrality_sign) && data$Confidence[i] == "Uncertain")) { + centrality_sign <- centrality_signs[i] sig <- data$Confidence[i] starts <- c(starts, i) ends <- c(ends, i - 1) @@ -216,8 +222,8 @@ summary.estimate_slopes <- function(object, ...) { out <- data.frame() for (g in seq_len(length(starts))) { dat <- data[starts[g]:ends[g], ] - dat <- as.data.frame(insight::get_datagrid(dat, at = NULL, factors = "mode")) - dat <- cbind(data.frame("Start" = data[starts[g], trend], "End" = data[ends[g], trend]), dat) + dat <- as.data.frame(insight::get_datagrid(dat, by = NULL, factors = "mode")) + dat <- cbind(data.frame(Start = data[starts[g], trend], End = data[ends[g], trend]), dat) out <- rbind(out, dat) } out @@ -233,16 +239,14 @@ summary.estimate_slopes <- function(object, ...) { if ("pd" %in% names(x)) confidence <- "pd" } - if (confidence == "p") { - sig <- tools::toTitleCase(effectsize::interpret_p(x$p, ...)) - } else if (confidence == "BF") { - sig <- tools::toTitleCase(effectsize::interpret_bf(x$BF, ...)) - } else if (confidence == "pd") { - sig <- tools::toTitleCase(effectsize::interpret_pd(x$pd, ...)) - } else { - # Based on CI - sig <- ifelse((x$CI_high < 0 & x$CI_low < 0) | (x$CI_high > 0 & x$CI_low > 0), "Significant", "Uncertain") - sig <- factor(sig, levels = c("Uncertain", "Significant")) - } - sig + switch(confidence, + p = tools::toTitleCase(effectsize::interpret_p(x$p, ...)), + BF = tools::toTitleCase(effectsize::interpret_bf(x$BF, ...)), + pd = tools::toTitleCase(effectsize::interpret_pd(x$pd, ...)), + { + # Based on CI + out <- ifelse((x$CI_high < 0 & x$CI_low < 0) | (x$CI_high > 0 & x$CI_low > 0), "Significant", "Uncertain") + factor(out, levels = c("Uncertain", "Significant")) + } + ) } diff --git a/R/get_emmeans.R b/R/get_emmeans.R index 9f3782af..988eb3da 100644 --- a/R/get_emmeans.R +++ b/R/get_emmeans.R @@ -20,8 +20,8 @@ #' Thus for a logistic model, `"none"` will give estimations expressed in #' log-odds (probabilities on logit scale) and `"response"` in terms of #' probabilities. -#' @param levels,modulate Deprecated, use `at` instead. -#' @param at The predictor variable(s) *at* which to evaluate the desired effect +#' @param levels,modulate,at Deprecated, use `by` instead. +#' @param by The predictor variable(s) at which to evaluate the desired effect #' / mean / contrasts. Other predictors of the model that are not included #' here will be collapsed and "averaged" over (the effect will be estimated #' across them). @@ -31,62 +31,68 @@ #' model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris) #' #' if (require("emmeans", quietly = TRUE)) { -#' # By default, 'at' is set to "Species" +#' # By default, 'by' is set to "Species" #' get_emmeans(model) #' #' # Overall mean (close to 'mean(iris$Sepal.Length)') -#' get_emmeans(model, at = NULL) +#' get_emmeans(model, by = NULL) #' #' # One can estimate marginal means at several values of a 'modulate' variable -#' get_emmeans(model, at = "Petal.Width", length = 3) +#' get_emmeans(model, by = "Petal.Width", length = 3) #' #' # Interactions #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) #' #' get_emmeans(model) -#' get_emmeans(model, at = c("Species", "Petal.Length"), length = 2) -#' get_emmeans(model, at = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2) +#' get_emmeans(model, by = c("Species", "Petal.Length"), length = 2) +#' get_emmeans(model, by = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2) #' } #' @export get_emmeans <- function(model, - at = "auto", + by = "auto", fixed = NULL, transform = "response", levels = NULL, modulate = NULL, + at = NULL, ...) { # Deprecation + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } if (!is.null(levels) || !is.null(modulate)) { - insight::format_warning("The `levels` and `modulate` arguments are deprecated. Please use `at` instead.") - at <- c(levels, modulate) + insight::format_warning("The `levels` and `modulate` arguments are deprecated. Please use `by` instead.") + by <- c(levels, modulate) } # check if available insight::check_if_installed("emmeans") # Guess arguments - args <- .guess_emmeans_arguments(model, at, fixed, ...) + my_args <- .guess_emmeans_arguments(model, by, fixed, ...) # Run emmeans estimated <- emmeans::emmeans( model, - specs = args$emmeans_specs, - at = args$emmeans_at, + specs = my_args$emmeans_specs, + at = my_args$emmeans_at, type = transform, ... ) # Special behaviour for transformations #138 (see below) - if ("retransform" %in% names(args) && length(args$retransform) > 0) { - for (var in names(args$retransform)) { - estimated@levels[[var]] <- levels(args$retransform[[var]]) - estimated@grid[[var]] <- args$retransform[[var]] + if ("retransform" %in% names(my_args) && length(my_args$retransform) > 0) { + for (var in names(my_args$retransform)) { + estimated@levels[[var]] <- levels(my_args$retransform[[var]]) + estimated@grid[[var]] <- my_args$retransform[[var]] } } - attr(estimated, "at") <- args$at - attr(estimated, "fixed") <- args$fixed + attr(estimated, "at") <- my_args$by + attr(estimated, "by") <- my_args$by + attr(estimated, "fixed") <- my_args$fixed estimated } @@ -137,24 +143,24 @@ model_emmeans <- get_emmeans #' @keywords internal .guess_emmeans_arguments <- function(model, - at = NULL, + by = NULL, fixed = NULL, ...) { # Gather info predictors <- insight::find_predictors(model, effects = "fixed", flatten = TRUE, ...) - data <- insight::get_data(model) + my_data <- insight::get_data(model) # Guess arguments - if (!is.null(at) && length(at) == 1 && at == "auto") { - at <- predictors[!sapply(data[predictors], is.numeric)] - if (!length(at) || all(is.na(at))) { - stop("Model contains no categorical factor. Please specify 'at'.", call. = FALSE) + if (!is.null(by) && length(by) == 1 && by == "auto") { + by <- predictors[!sapply(my_data[predictors], is.numeric)] + if (!length(by) || all(is.na(by))) { + stop("Model contains no categorical factor. Please specify 'by'.", call. = FALSE) } - message("We selected `at = c(", toString(paste0('"', at, '"')), ")`.") + message("We selected `by = c(", toString(paste0('"', by, '"')), ")`.") } - args <- list(at = at, fixed = fixed) - .format_emmeans_arguments(model, args, data, ...) + my_args <- list(by = by, fixed = fixed) + .format_emmeans_arguments(model, args = my_args, data = my_data, ...) } @@ -168,28 +174,26 @@ model_emmeans <- get_emmeans # Deal with 'at' if (is.null(args$at)) { args$data_matrix <- NULL + } else if (is.data.frame(args$at)) { + args$data_matrix <- args$at + args$at <- names(args$at) + } else if (is.list(args$at)) { + args$data_matrix <- expand.grid(args$at) + args$at <- names(args$data_matrix) + } else if (inherits(args$at, "formula")) { + args$data_matrix <- stats::model.frame(args$at, data = data) + args$at <- names(args$data_matrix) } else { - if (is.data.frame(args$at)) { - args$data_matrix <- args$at - args$at <- names(args$at) - } else if (is.list(args$at)) { - args$data_matrix <- expand.grid(args$at) - args$at <- names(args$data_matrix) - } else if (inherits(args$at, "formula")) { - args$data_matrix <- stats::model.frame(args$at, data = data) - args$at <- names(args$data_matrix) + if (!is.null(args$at) && all(args$at == "all")) { + target <- insight::find_predictors(model, effects = "fixed", flatten = TRUE) + target <- target[!target %in% args$fixed] } else { - if (!is.null(args$at) && all(args$at == "all")) { - target <- insight::find_predictors(model, effects = "fixed", flatten = TRUE) - target <- target[!target %in% args$fixed] - } else { - target <- args$at - } - grid <- insight::get_datagrid(data, at = target, ...) - args$at <- attributes(grid)$at_specs$varname - args$data_matrix <- as.data.frame(grid[args$at]) - if (length(args$at) == 0) args$at <- NULL # Post-clean + target <- args$at } + datagrid <- insight::get_datagrid(data, at = target, ...) + args$at <- attributes(datagrid)$at_specs$varname + args$data_matrix <- as.data.frame(datagrid[args$at]) + if (length(args$at) == 0) args$at <- NULL # Post-clean } # Deal with 'contrast' @@ -229,9 +233,9 @@ model_emmeans <- get_emmeans # It's annoying and an ugly fix, not sure how to address if (!is.null(args$emmeans_at)) { args$retransform <- list() - terms <- insight::find_terms(model)$conditional + model_terms <- insight::find_terms(model)$conditional for (var_at in names(args$emmeans_at)) { - term <- terms[grepl(var_at, terms, fixed = TRUE)] + term <- model_terms[grepl(var_at, model_terms, fixed = TRUE)] if (any(grepl(paste0("as.factor(", var_at, ")"), term, fixed = TRUE)) || any(grepl(paste0("as.character(", var_at, ")"), term, fixed = TRUE))) { args$retransform[[var_at]] <- args$emmeans_at[[var_at]] diff --git a/man/estimate_contrasts.Rd b/man/estimate_contrasts.Rd index a0df02e4..f2b64f47 100644 --- a/man/estimate_contrasts.Rd +++ b/man/estimate_contrasts.Rd @@ -7,13 +7,14 @@ estimate_contrasts( model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, transform = "none", ci = 0.95, p_adjust = "holm", method = "pairwise", adjust = NULL, + at = NULL, ... ) } @@ -23,7 +24,7 @@ estimate_contrasts( \item{contrast}{A character vector indicating the name of the variable(s) for which to compute the contrasts.} -\item{at}{The predictor variable(s) \emph{at} which to evaluate the desired effect +\item{by}{The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).} @@ -144,7 +145,7 @@ estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)")) estimate_contrasts(model, fixed = "Petal.Width") # Or modulate it -estimate_contrasts(model, at = "Petal.Width", length = 4) +estimate_contrasts(model, by = "Petal.Width", length = 4) # Standardized differences estimated <- estimate_contrasts(lm(Sepal.Width ~ Species, data = iris)) @@ -173,10 +174,10 @@ estimate_contrasts(model, fixed = "am") model <- stan_glm(mpg ~ cyl * wt, data = data, refresh = 0) estimate_contrasts(model) estimate_contrasts(model, fixed = "wt") -estimate_contrasts(model, at = "wt", length = 4) +estimate_contrasts(model, by = "wt", length = 4) model <- stan_glm(Sepal.Width ~ Species + Petal.Width + Petal.Length, data = iris, refresh = 0) -estimate_contrasts(model, at = "Petal.Length", test = "bf") +estimate_contrasts(model, by = "Petal.Length", test = "bf") } \dontshow{\}) # examplesIf} } diff --git a/man/estimate_means.Rd b/man/estimate_means.Rd index 840663cb..660b8f24 100644 --- a/man/estimate_means.Rd +++ b/man/estimate_means.Rd @@ -17,11 +17,6 @@ estimate_means( \arguments{ \item{model}{A statistical model.} -\item{at}{The predictor variable(s) \emph{at} which to evaluate the desired effect -/ mean / contrasts. Other predictors of the model that are not included -here will be collapsed and "averaged" over (the effect will be estimated -across them).} - \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} diff --git a/man/estimate_slopes.Rd b/man/estimate_slopes.Rd index 528bba76..0cff75f8 100644 --- a/man/estimate_slopes.Rd +++ b/man/estimate_slopes.Rd @@ -4,7 +4,7 @@ \alias{estimate_slopes} \title{Estimate Marginal Effects} \usage{ -estimate_slopes(model, trend = NULL, at = NULL, ci = 0.95, ...) +estimate_slopes(model, trend = NULL, by = NULL, ci = 0.95, at = NULL, ...) } \arguments{ \item{model}{A statistical model.} @@ -12,7 +12,7 @@ estimate_slopes(model, trend = NULL, at = NULL, ci = 0.95, ...) \item{trend}{A character indicating the name of the variable for which to compute the slopes.} -\item{at}{The predictor variable(s) \emph{at} which to evaluate the desired effect +\item{by}{The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).} @@ -100,7 +100,7 @@ ggplot(iris, aes(x = Petal.Length, y = Sepal.Width)) + # Model it model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) # Compute the marginal effect of Petal.Length at each level of Species -slopes <- estimate_slopes(model, trend = "Petal.Length", at = "Species") +slopes <- estimate_slopes(model, trend = "Petal.Length", by = "Species") slopes # Plot it @@ -110,14 +110,14 @@ standardize(slopes) \dontshow{\}) # examplesIf} \dontshow{if (require("mgcv") && require("emmeans") && require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris) -slopes <- estimate_slopes(model, at = "Petal.Length", length = 50) +slopes <- estimate_slopes(model, by = "Petal.Length", length = 50) summary(slopes) plot(slopes) model <- mgcv::gam(Sepal.Width ~ s(Petal.Length, by = Species), data = iris) slopes <- estimate_slopes(model, trend = "Petal.Length", - at = c("Petal.Length", "Species"), length = 20 + by = c("Petal.Length", "Species"), length = 20 ) summary(slopes) plot(slopes) diff --git a/man/get_emmeans.Rd b/man/get_emmeans.Rd index 0890f146..d0662b52 100644 --- a/man/get_emmeans.Rd +++ b/man/get_emmeans.Rd @@ -32,21 +32,23 @@ model_emcontrasts( get_emmeans( model, - at = "auto", + by = "auto", fixed = NULL, transform = "response", levels = NULL, modulate = NULL, + at = NULL, ... ) model_emmeans( model, - at = "auto", + by = "auto", fixed = NULL, transform = "response", levels = NULL, modulate = NULL, + at = NULL, ... ) @@ -76,11 +78,6 @@ model_emtrends( \item{contrast}{A character vector indicating the name of the variable(s) for which to compute the contrasts.} -\item{at}{The predictor variable(s) \emph{at} which to evaluate the desired effect -/ mean / contrasts. Other predictors of the model that are not included -here will be collapsed and "averaged" over (the effect will be estimated -across them).} - \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} @@ -99,7 +96,12 @@ probabilities.} \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} -\item{levels, modulate}{Deprecated, use \code{at} instead.} +\item{by}{The predictor variable(s) at which to evaluate the desired effect +/ mean / contrasts. Other predictors of the model that are not included +here will be collapsed and "averaged" over (the effect will be estimated +across them).} + +\item{levels, modulate, at}{Deprecated, use \code{by} instead.} \item{trend}{A character indicating the name of the variable for which to compute the slopes.} @@ -136,21 +138,21 @@ if (require("emmeans", quietly = TRUE)) { model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris) if (require("emmeans", quietly = TRUE)) { - # By default, 'at' is set to "Species" + # By default, 'by' is set to "Species" get_emmeans(model) # Overall mean (close to 'mean(iris$Sepal.Length)') - get_emmeans(model, at = NULL) + get_emmeans(model, by = NULL) # One can estimate marginal means at several values of a 'modulate' variable - get_emmeans(model, at = "Petal.Width", length = 3) + get_emmeans(model, by = "Petal.Width", length = 3) # Interactions model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) get_emmeans(model) - get_emmeans(model, at = c("Species", "Petal.Length"), length = 2) - get_emmeans(model, at = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2) + get_emmeans(model, by = c("Species", "Petal.Length"), length = 2) + get_emmeans(model, by = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2) } if (require("emmeans")) { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) diff --git a/man/get_marginaleffects.Rd b/man/get_marginaleffects.Rd index b17f0a39..5b8a47a3 100644 --- a/man/get_marginaleffects.Rd +++ b/man/get_marginaleffects.Rd @@ -12,11 +12,6 @@ get_marginaleffects(model, trend = NULL, at = NULL, fixed = NULL, ...) \item{trend}{A character indicating the name of the variable for which to compute the slopes.} -\item{at}{The predictor variable(s) \emph{at} which to evaluate the desired effect -/ mean / contrasts. Other predictors of the model that are not included -here will be collapsed and "averaged" over (the effect will be estimated -across them).} - \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} diff --git a/man/visualisation_matrix.Rd b/man/visualisation_matrix.Rd index 827b0133..dec6f37d 100644 --- a/man/visualisation_matrix.Rd +++ b/man/visualisation_matrix.Rd @@ -30,55 +30,7 @@ visualisation_matrix(x, ...) \item{...}{Arguments passed to or from other methods (for instance, \code{length} or \code{range} to control the spread of numeric variables.).} -\item{at}{Indicates the \emph{focal predictors} (variables) for the reference grid -and at which values focal predictors should be represented. If not specified -otherwise, representative values for numeric variables or predictors are -evenly distributed from the minimum to the maximum, with a total number of -\code{length} values covering that range (see 'Examples'). Possible options for -\code{at} are: -\itemize{ -\item \code{"all"}, which will include all variables or predictors. -\item a character vector of one or more variable or predictor names, like -\code{c("Species", "Sepal.Width")}, which will create a grid of all combinations -of unique values. For factors, will use all levels, for numeric variables, -will use a range of length \code{length} (evenly spread from minimum to maximum) -and for character vectors, will use all unique values. -\item a list of named elements, indicating focal predictors and their representative -values, e.g. \code{at = list(Sepal.Length = c(2, 4), Species = "setosa")}. -\item a string with assignments, e.g. \code{at = "Sepal.Length = 2"} or -\code{at = c("Sepal.Length = 2", "Species = 'setosa'")} - note the usage of single -and double quotes to assign strings within strings. -} - -There is a special handling of assignments with \emph{brackets}, i.e. values -defined inside \code{[} and \verb{]}.For \strong{numeric} variables, the value(s) inside -the brackets should either be -\itemize{ -\item two values, indicating minimum and maximum (e.g. \code{at = "Sepal.Length = [0, 5]"}), -for which a range of length \code{length} (evenly spread from given minimum to -maximum) is created. -\item more than two numeric values \code{at = "Sepal.Length = [2,3,4,5]"}, in which -case these values are used as representative values. -\item a "token" that creates pre-defined representative values: -\itemize{ -\item for mean and -/+ 1 SD around the mean: \code{"x = [sd]"} -\item for median and -/+ 1 MAD around the median: \code{"x = [mad]"} -\item for Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): \code{"x = [fivenum]"} -\item for terciles, including minimum and maximum: \code{"x = [terciles]"} -\item for terciles, excluding minimum and maximum: \code{"x = [terciles2]"} -\item for quartiles, including minimum and maximum: \code{"x = [quartiles]"} -\item for quartiles, excluding minimum and maximum: \code{"x = [quartiles2]"} -\item for minimum and maximum value: \code{"x = [minmax]"} -\item for 0 and the maximum value: \code{"x = [zeromax]"} -} -} - -For \strong{factor} variables, the value(s) inside the brackets should indicate -one or more factor levels, like \code{at = "Species = [setosa, versicolor]"}. -\strong{Note}: the \code{length} argument will be ignored when using brackets-tokens. - -The remaining variables not specified in \code{at} will be fixed (see also arguments -\code{factors} and \code{numerics}).} +\item{at}{Deprecated. Use \code{by} instead.} \item{target}{Deprecated name. Please use \code{at} instead.} From ec00fdceafccf6b1664be8bd8646ac11fc3a5086 Mon Sep 17 00:00:00 2001 From: Daniel Date: Wed, 22 May 2024 20:55:19 +0200 Subject: [PATCH 02/24] use remotes --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1cc2e17e..92efb858 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: modelbased Title: Estimation of Model-Based Predictions, Contrasts and Means -Version: 0.8.7 +Version: 0.8.7.1 Authors@R: c(person(given = "Dominique", family = "Makowski", @@ -76,3 +76,4 @@ Config/Needs/website: rstudio/bslib, r-lib/pkgdown, easystats/easystatstemplate +Remotes: easystats/insight, easystats/datawizard, easystats/parameters, easystats/performance From 04458114b01850b490a928a57da8a65c29896f92 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 08:12:38 +0200 Subject: [PATCH 03/24] fixes --- R/estimate_means.R | 36 ++++++----- R/get_emcontrasts.R | 49 ++++++++------- R/get_emmeans.R | 36 +++++------ R/get_emtrends.R | 50 ++++++++------- R/get_marginalcontrasts.R | 16 ++--- R/get_marginaleffects.R | 28 ++++++--- R/get_marginalmeans.R | 30 ++++----- man/estimate_means.Rd | 22 ++++--- man/get_emmeans.Rd | 32 +++++----- man/get_marginaleffects.Rd | 20 ++++-- tests/testthat/test-estimate_contrasts.R | 80 ++++++++++++------------ tests/testthat/test-glmmTMB.R | 14 ++--- 12 files changed, 231 insertions(+), 182 deletions(-) diff --git a/R/estimate_means.R b/R/estimate_means.R index 042d89cc..f870cb14 100644 --- a/R/estimate_means.R +++ b/R/estimate_means.R @@ -20,12 +20,12 @@ #' #' estimate_means(model) #' estimate_means(model, fixed = "Sepal.Width") -#' estimate_means(model, at = c("Species", "Sepal.Width"), length = 2) -#' estimate_means(model, at = "Species=c('versicolor', 'setosa')") -#' estimate_means(model, at = "Sepal.Width=c(2, 4)") -#' estimate_means(model, at = c("Species", "Sepal.Width=0")) -#' estimate_means(model, at = "Sepal.Width", length = 5) -#' estimate_means(model, at = "Sepal.Width=c(2, 4)") +#' estimate_means(model, by = c("Species", "Sepal.Width"), length = 2) +#' estimate_means(model, by = "Species=c('versicolor', 'setosa')") +#' estimate_means(model, by = "Sepal.Width=c(2, 4)") +#' estimate_means(model, by = c("Species", "Sepal.Width=0")) +#' estimate_means(model, by = "Sepal.Width", length = 5) +#' estimate_means(model, by = "Sepal.Width=c(2, 4)") #' #' # Methods that can be applied to it: #' means <- estimate_means(model, fixed = "Sepal.Width") @@ -42,24 +42,30 @@ #' #' model <- lmer(Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor), data = data) #' estimate_means(model) -#' estimate_means(model, at = "Sepal.Width", length = 3) +#' estimate_means(model, by = "Sepal.Width", length = 3) #' } #' @return A data frame of estimated marginal means. #' @export estimate_means <- function(model, - at = "auto", + by = "auto", fixed = NULL, transform = "response", ci = 0.95, backend = "emmeans", + at = NULL, ...) { + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } + if (backend == "emmeans") { # Emmeans ------------------------------------------------------------------ - estimated <- get_emmeans(model, at, fixed, transform = transform, ...) + estimated <- get_emmeans(model, by, fixed, transform = transform, ...) means <- .format_emmeans_means(estimated, model, ci, transform, ...) } else { # Marginalmeans ------------------------------------------------------------ - estimated <- .get_marginalmeans(model, at, ci = ci, ...) + estimated <- .get_marginalmeans(model, by, ci = ci, ...) means <- .format_marginaleffects_means(estimated, model, ...) } @@ -88,14 +94,14 @@ estimate_means <- function(model, # Table Formating ---------------------------------------------------------- -.estimate_means_footer <- function(x, at = NULL, type = "means", p_adjust = NULL) { +.estimate_means_footer <- function(x, by = NULL, type = "means", p_adjust = NULL) { table_footer <- paste("\nMarginal", type) # Levels - if (!is.null(at) && length(at) > 0) { - table_footer <- paste0(table_footer, " estimated at ", toString(at)) + if (!is.null(by) && length(by) > 0) { + table_footer <- paste0(table_footer, " estimated at ", toString(by)) } else { - table_footer <- paste0(table_footer, " estimated at ", attr(x, "at")) + table_footer <- paste0(table_footer, " estimated at ", attr(x, "by")) } # P-value adjustment footer @@ -107,6 +113,6 @@ estimate_means <- function(model, } } - if (all(table_footer == "")) table_footer <- NULL + if (all(table_footer == "")) table_footer <- NULL # nolint c(table_footer, "blue") } diff --git a/R/get_emcontrasts.R b/R/get_emcontrasts.R index 17d0d71e..24ce04a3 100644 --- a/R/get_emcontrasts.R +++ b/R/get_emcontrasts.R @@ -23,41 +23,48 @@ #' # Can fixate the numeric at a specific value #' get_emcontrasts(model, fixed = "Petal.Width") #' # Or modulate it -#' get_emcontrasts(model, at = "Petal.Width", length = 4) +#' get_emcontrasts(model, by = "Petal.Width", length = 4) #' } #' @export get_emcontrasts <- function(model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, transform = "none", method = "pairwise", + at = NULL, ...) { # check if available insight::check_if_installed("emmeans") + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } + # Guess arguments - args <- .guess_emcontrasts_arguments(model, contrast, at, fixed, ...) + my_args <- .guess_emcontrasts_arguments(model, contrast, by, fixed, ...) # Run emmeans estimated <- emmeans::emmeans( model, - specs = args$emmeans_specs, - at = args$emmeans_at, + specs = my_args$emmeans_specs, + at = my_args$emmeans_at, type = transform, ... ) # Find by variables - by <- args$emmeans_specs[!args$emmeans_specs %in% args$contrast] - if (length(by) == 0) by <- NULL + emm_by <- my_args$emmeans_specs[!my_args$emmeans_specs %in% my_args$contrast] + if (length(emm_by) == 0) emm_by <- NULL - contrasts <- emmeans::contrast(estimated, by = by, method = method, ...) + out <- emmeans::contrast(estimated, by = emm_by, method = method, ...) - attr(contrasts, "contrast") <- args$contrast - attr(contrasts, "at") <- args$at - attr(contrasts, "fixed") <- args$fixed - contrasts + attr(out, "contrast") <- my_args$contrast + attr(out, "at") <- my_args$by + attr(out, "by") <- my_args$by + attr(out, "fixed") <- my_args$fixed + out } #' @rdname get_emmeans @@ -72,26 +79,24 @@ model_emcontrasts <- get_emcontrasts #' @keywords internal .guess_emcontrasts_arguments <- function(model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, ...) { # Gather info predictors <- insight::find_predictors(model, effects = "fixed", flatten = TRUE, ...) - data <- insight::get_data(model) + model_data <- insight::get_data(model) # Guess arguments if (is.null(contrast)) { - contrast <- predictors[!sapply(data[predictors], is.numeric)][1] + contrast <- predictors[!sapply(model_data[predictors], is.numeric)][1] if (!length(contrast) || is.na(contrast)) { contrast <- predictors[1] } - message('No variable was specified for contrast estimation. Selecting `contrast = "', contrast, '"`.') - } else { - if (all(contrast == "all")) { - contrast <- predictors - } + insight::format_alert('No variable was specified for contrast estimation. Selecting `contrast = "', contrast, '"`.') # nolint + } else if (all(contrast == "all")) { + contrast <- predictors } - args <- list(contrast = contrast, at = at, fixed = fixed) - .format_emmeans_arguments(model, args, data, ...) + my_args <- list(contrast = contrast, by = by, fixed = fixed) + .format_emmeans_arguments(model, args = my_args, data = model_data, ...) } diff --git a/R/get_emmeans.R b/R/get_emmeans.R index 988eb3da..e0f58938 100644 --- a/R/get_emmeans.R +++ b/R/get_emmeans.R @@ -172,33 +172,33 @@ model_emmeans <- get_emmeans data <- data[insight::find_predictors(model, effects = "fixed", flatten = TRUE, ...)] # Deal with 'at' - if (is.null(args$at)) { + if (is.null(args$by)) { args$data_matrix <- NULL - } else if (is.data.frame(args$at)) { - args$data_matrix <- args$at - args$at <- names(args$at) - } else if (is.list(args$at)) { - args$data_matrix <- expand.grid(args$at) - args$at <- names(args$data_matrix) - } else if (inherits(args$at, "formula")) { - args$data_matrix <- stats::model.frame(args$at, data = data) - args$at <- names(args$data_matrix) + } else if (is.data.frame(args$by)) { + args$data_matrix <- args$by + args$by <- names(args$by) + } else if (is.list(args$by)) { + args$data_matrix <- expand.grid(args$by) + args$by <- names(args$data_matrix) + } else if (inherits(args$by, "formula")) { + args$data_matrix <- stats::model.frame(args$by, data = data) + args$by <- names(args$data_matrix) } else { - if (!is.null(args$at) && all(args$at == "all")) { + if (!is.null(args$by) && all(args$by == "all")) { target <- insight::find_predictors(model, effects = "fixed", flatten = TRUE) target <- target[!target %in% args$fixed] } else { - target <- args$at + target <- args$by } - datagrid <- insight::get_datagrid(data, at = target, ...) - args$at <- attributes(datagrid)$at_specs$varname - args$data_matrix <- as.data.frame(datagrid[args$at]) - if (length(args$at) == 0) args$at <- NULL # Post-clean + datagrid <- insight::get_datagrid(data, by = target, ...) + args$by <- attributes(datagrid)$at_specs$varname + args$data_matrix <- as.data.frame(datagrid[args$by]) + if (length(args$by) == 0) args$by <- NULL # Post-clean } # Deal with 'contrast' if (!is.null(args$contrast)) { - contrast <- insight::get_datagrid(data, at = args$contrast, ...) + contrast <- insight::get_datagrid(data, by = args$contrast, ...) args$contrast <- attributes(contrast)$at_specs$varname contrast <- as.data.frame(contrast[args$contrast]) if (is.null(args$data_matrix)) { @@ -211,7 +211,7 @@ model_emmeans <- get_emmeans # Deal with 'fixed' if (!is.null(args$fixed)) { - fixed <- insight::get_datagrid(data[args$fixed], at = NULL, ...) + fixed <- insight::get_datagrid(data[args$fixed], by = NULL, ...) if (is.null(args$data_matrix)) { args$data_matrix <- fixed } else { diff --git a/R/get_emtrends.R b/R/get_emtrends.R index d29334b0..6623c664 100644 --- a/R/get_emtrends.R +++ b/R/get_emtrends.R @@ -8,46 +8,52 @@ #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) #' #' get_emtrends(model) -#' get_emtrends(model, at = "Species") -#' get_emtrends(model, at = "Petal.Length") -#' get_emtrends(model, at = c("Species", "Petal.Length")) +#' get_emtrends(model, by = "Species") +#' get_emtrends(model, by = "Petal.Length") +#' get_emtrends(model, by = c("Species", "Petal.Length")) #' #' model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) #' get_emtrends(model) -#' get_emtrends(model, at = "Sepal.Width") +#' get_emtrends(model, by = "Sepal.Width") #' } #' @export get_emtrends <- function(model, trend = NULL, - at = NULL, + by = NULL, fixed = NULL, levels = NULL, modulate = NULL, + at = NULL, ...) { # Deprecation + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } if (!is.null(levels) || !is.null(modulate)) { - warning("The `levels` and `modulate` arguments are deprecated. Please use `at` instead.", call. = FALSE) - at <- c(levels, modulate) + insight::format_warning("The `levels` and `modulate` arguments are deprecated. Please use `by` instead.") # nolint + by <- c(levels, modulate) } # check if available insight::check_if_installed("emmeans") # Guess arguments - args <- .guess_emtrends_arguments(model, trend, at, fixed, ...) + my_args <- .guess_emtrends_arguments(model, trend, by, fixed, ...) # Run emtrends estimated <- emmeans::emtrends( model, - specs = args$emmeans_specs, - var = args$trend, - at = args$emmeans_at, + specs = my_args$emmeans_specs, + var = my_args$trend, + at = my_args$emmeans_at, ... ) - attr(estimated, "trend") <- args$trend - attr(estimated, "at") <- args$at - attr(estimated, "fixed") <- args$fixed + attr(estimated, "trend") <- my_args$trend + attr(estimated, "at") <- my_args$by + attr(estimated, "by") <- my_args$by + attr(estimated, "fixed") <- my_args$fixed estimated } @@ -62,26 +68,26 @@ model_emtrends <- get_emtrends #' @keywords internal .guess_emtrends_arguments <- function(model, trend = NULL, - at = NULL, + by = NULL, fixed = NULL, ...) { # Gather info predictors <- insight::find_predictors(model, effects = "fixed", flatten = TRUE, ...) - data <- insight::get_data(model) + model_data <- insight::get_data(model) # Guess arguments if (is.null(trend)) { - trend <- predictors[sapply(data[predictors], is.numeric)][1] + trend <- predictors[sapply(model_data[predictors], is.numeric)][1] if (!length(trend) || is.na(trend)) { - stop("Model contains no numeric predictor. Please specify 'trend'.", call. = FALSE) + insight::format_error("Model contains no numeric predictor. Please specify `trend`.") } - message('No numeric variable was specified for slope estimation. Selecting `trend = "', trend, '"`.') + insight::format_alert('No numeric variable was specified for slope estimation. Selecting `trend = "', trend, '"`.') } if (length(trend) > 1) { - message("More than one numeric variable was selected for slope estimation. Keeping only ", trend[1], ".") + insight::format_alert("More than one numeric variable was selected for slope estimation. Keeping only ", trend[1], ".") trend <- trend[1] } - args <- list(trend = trend, at = at, fixed = fixed) - .format_emmeans_arguments(model, args, data, ...) + my_args <- list(trend = trend, by = by, fixed = fixed) + .format_emmeans_arguments(model, args = my_args, data = model_data, ...) } diff --git a/R/get_marginalcontrasts.R b/R/get_marginalcontrasts.R index d2d9edc8..b57561b4 100644 --- a/R/get_marginalcontrasts.R +++ b/R/get_marginalcontrasts.R @@ -9,12 +9,12 @@ # # estimate_means(model) # estimate_means(model, fixed = "Sepal.Width") -# estimate_means(model, at = c("Species", "Sepal.Width"), length = 2) -# estimate_means(model, at = "Species=c('versicolor', 'setosa')") -# estimate_means(model, at = "Sepal.Width=c(2, 4)") -# estimate_means(model, at = c("Species", "Sepal.Width=0")) -# estimate_means(model, at = "Sepal.Width", length = 5) -# estimate_means(model, at = "Sepal.Width=c(2, 4)") +# estimate_means(model, by = c("Species", "Sepal.Width"), length = 2) +# estimate_means(model, by = "Species=c('versicolor', 'setosa')") +# estimate_means(model, by = "Sepal.Width=c(2, 4)") +# estimate_means(model, by = c("Species", "Sepal.Width=0")) +# estimate_means(model, by = "Sepal.Width", length = 5) +# estimate_means(model, by = "Sepal.Width=c(2, 4)") # # # Methods that can be applied to it: # means <- estimate_means(model, fixed = "Sepal.Width") @@ -29,13 +29,13 @@ # # model <- lmer(Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor), data = data) # estimate_means(model) -# estimate_means(model, at = "Sepal.Width", length = 3) +# estimate_means(model, by = "Sepal.Width", length = 3) # } # } # } # #' @keywords internal # .get_marginalmeans <- function(model, -# at = "auto", +# by = "auto", # fixed = NULL, # transform = "response", # ci = 0.95, diff --git a/R/get_marginaleffects.R b/R/get_marginaleffects.R index 8a0a6783..7481eb0e 100644 --- a/R/get_marginaleffects.R +++ b/R/get_marginaleffects.R @@ -9,19 +9,26 @@ #' if (require("marginaleffects")) { #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) #' -#' get_marginaleffects(model, trend = "Petal.Length", at = "Species") -#' get_marginaleffects(model, trend = "Petal.Length", at = "Petal.Length") -#' get_marginaleffects(model, trend = "Petal.Length", at = c("Species", "Petal.Length")) +#' get_marginaleffects(model, trend = "Petal.Length", by = "Species") +#' get_marginaleffects(model, trend = "Petal.Length", by = "Petal.Length") +#' get_marginaleffects(model, trend = "Petal.Length", by = c("Species", "Petal.Length")) #' } #' @export get_marginaleffects <- function(model, trend = NULL, - at = NULL, + by = NULL, fixed = NULL, + at = NULL, ...) { # check if available insight::check_if_installed("marginaleffects") + # Deprecation + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at + } + # Guess arguments if (is.null(trend)) { trend <- insight::find_predictors(model, effects = "fixed", flatten = TRUE)[1] @@ -30,21 +37,22 @@ get_marginaleffects <- function(model, ) } - if (is.null(at)) { - at <- insight::find_predictors(model, effects = "fixed", flatten = TRUE) - at <- at[!at %in% trend] + if (is.null(by)) { + by <- insight::find_predictors(model, effects = "fixed", flatten = TRUE) + by <- by[!by %in% trend] } - newdata <- insight::get_datagrid(model, at = at, ...) + newdata <- insight::get_datagrid(model, by = by, ...) - fixed <- names(newdata)[!names(newdata) %in% c(at, trend)] + fixed <- names(newdata)[!names(newdata) %in% c(by, trend)] if (length(fixed) == 0) fixed <- NULL # Compute stuff estimated <- marginaleffects::slopes(model, variables = trend, newdata = newdata, ...) attr(estimated, "trend") <- trend - attr(estimated, "at") <- at + attr(estimated, "at") <- by + attr(estimated, "by") <- by attr(estimated, "fixed") <- fixed estimated } diff --git a/R/get_marginalmeans.R b/R/get_marginalmeans.R index ecd81135..4a141289 100644 --- a/R/get_marginalmeans.R +++ b/R/get_marginalmeans.R @@ -1,6 +1,6 @@ #' @keywords internal .get_marginalmeans <- function(model, - at = "auto", + by = "auto", ci = 0.95, marginal = FALSE, ...) { @@ -8,10 +8,10 @@ insight::check_if_installed("marginaleffects") # Guess arguments - args <- .guess_arguments_means(model, at, ...) + my_args <- .guess_arguments_means(model, by, ...) # Get corresponding datagrid (and deal with particular ats) - datagrid <- insight::get_datagrid(model, at = args$at, ...) + datagrid <- insight::get_datagrid(model, by = my_args$by, ...) # Drop random effects datagrid <- datagrid[insight::find_predictors(model, effects = "fixed", flatten = TRUE)] at_specs <- attributes(datagrid)$at_specs @@ -36,7 +36,8 @@ conf_level = ci ) } - attr(means, "at") <- args$at + attr(means, "at") <- my_args$by + attr(means, "by") <- my_args$by means } @@ -57,27 +58,28 @@ params <- datawizard::data_restoretype(params, insight::get_data(model)) # Store info - attr(params, "at") <- attr(means, "at") + attr(params, "at") <- attr(means, "by") + attr(params, "by") <- attr(means, "by") params } # Guess ------------------------------------------------------------------- #' @keywords internal -.guess_arguments_means <- function(model, at = NULL, ...) { +.guess_arguments_means <- function(model, by = NULL, ...) { # Gather info and data from model predictors <- insight::find_predictors(model, flatten = TRUE, ...) - data <- insight::get_data(model) + model_data <- insight::get_data(model) - # Guess arguments ('at' and 'fixed') - if (identical(at, "auto")) { + # Guess arguments ('by' and 'fixed') + if (identical(by, "auto")) { # Find categorical predictors - at <- predictors[!vapply(data[predictors], is.numeric, logical(1))] - if (!length(at) || all(is.na(at))) { - insight::format_error("Model contains no categorical factor. Please specify 'at'.") + by <- predictors[!vapply(model_data[predictors], is.numeric, logical(1))] + if (!length(by) || all(is.na(by))) { + insight::format_error("Model contains no categorical factor. Please specify 'by'.") } - insight::format_alert("We selected `at = c(", toString(paste0('"', at, '"')), ")`.") + insight::format_alert("We selected `by = c(", toString(paste0('"', by, '"')), ")`.") } - list(at = at) + list(by = by) } diff --git a/man/estimate_means.Rd b/man/estimate_means.Rd index 660b8f24..64040e93 100644 --- a/man/estimate_means.Rd +++ b/man/estimate_means.Rd @@ -6,17 +6,23 @@ \usage{ estimate_means( model, - at = "auto", + by = "auto", fixed = NULL, transform = "response", ci = 0.95, backend = "emmeans", + at = NULL, ... ) } \arguments{ \item{model}{A statistical model.} +\item{by}{The predictor variable(s) at which to evaluate the desired effect +/ mean / contrasts. Other predictors of the model that are not included +here will be collapsed and "averaged" over (the effect will be estimated +across them).} + \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} @@ -113,12 +119,12 @@ model <- lm(Petal.Length ~ Sepal.Width * Species, data = iris) estimate_means(model) estimate_means(model, fixed = "Sepal.Width") -estimate_means(model, at = c("Species", "Sepal.Width"), length = 2) -estimate_means(model, at = "Species=c('versicolor', 'setosa')") -estimate_means(model, at = "Sepal.Width=c(2, 4)") -estimate_means(model, at = c("Species", "Sepal.Width=0")) -estimate_means(model, at = "Sepal.Width", length = 5) -estimate_means(model, at = "Sepal.Width=c(2, 4)") +estimate_means(model, by = c("Species", "Sepal.Width"), length = 2) +estimate_means(model, by = "Species=c('versicolor', 'setosa')") +estimate_means(model, by = "Sepal.Width=c(2, 4)") +estimate_means(model, by = c("Species", "Sepal.Width=0")) +estimate_means(model, by = "Sepal.Width", length = 5) +estimate_means(model, by = "Sepal.Width=c(2, 4)") # Methods that can be applied to it: means <- estimate_means(model, fixed = "Sepal.Width") @@ -135,7 +141,7 @@ data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B") model <- lmer(Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor), data = data) estimate_means(model) -estimate_means(model, at = "Sepal.Width", length = 3) +estimate_means(model, by = "Sepal.Width", length = 3) } \dontshow{\}) # examplesIf} } diff --git a/man/get_emmeans.Rd b/man/get_emmeans.Rd index d0662b52..b8a714fd 100644 --- a/man/get_emmeans.Rd +++ b/man/get_emmeans.Rd @@ -13,20 +13,22 @@ get_emcontrasts( model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, transform = "none", method = "pairwise", + at = NULL, ... ) model_emcontrasts( model, contrast = NULL, - at = NULL, + by = NULL, fixed = NULL, transform = "none", method = "pairwise", + at = NULL, ... ) @@ -55,20 +57,22 @@ model_emmeans( get_emtrends( model, trend = NULL, - at = NULL, + by = NULL, fixed = NULL, levels = NULL, modulate = NULL, + at = NULL, ... ) model_emtrends( model, trend = NULL, - at = NULL, + by = NULL, fixed = NULL, levels = NULL, modulate = NULL, + at = NULL, ... ) } @@ -78,6 +82,11 @@ model_emtrends( \item{contrast}{A character vector indicating the name of the variable(s) for which to compute the contrasts.} +\item{by}{The predictor variable(s) at which to evaluate the desired effect +/ mean / contrasts. Other predictors of the model that are not included +here will be collapsed and "averaged" over (the effect will be estimated +across them).} + \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} @@ -96,11 +105,6 @@ probabilities.} \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} -\item{by}{The predictor variable(s) at which to evaluate the desired effect -/ mean / contrasts. Other predictors of the model that are not included -here will be collapsed and "averaged" over (the effect will be estimated -across them).} - \item{levels, modulate, at}{Deprecated, use \code{by} instead.} \item{trend}{A character indicating the name of the variable @@ -133,7 +137,7 @@ if (require("emmeans", quietly = TRUE)) { # Can fixate the numeric at a specific value get_emcontrasts(model, fixed = "Petal.Width") # Or modulate it - get_emcontrasts(model, at = "Petal.Width", length = 4) + get_emcontrasts(model, by = "Petal.Width", length = 4) } model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris) @@ -158,12 +162,12 @@ if (require("emmeans")) { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) get_emtrends(model) - get_emtrends(model, at = "Species") - get_emtrends(model, at = "Petal.Length") - get_emtrends(model, at = c("Species", "Petal.Length")) + get_emtrends(model, by = "Species") + get_emtrends(model, by = "Petal.Length") + get_emtrends(model, by = c("Species", "Petal.Length")) model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) get_emtrends(model) - get_emtrends(model, at = "Sepal.Width") + get_emtrends(model, by = "Sepal.Width") } } diff --git a/man/get_marginaleffects.Rd b/man/get_marginaleffects.Rd index 5b8a47a3..b344146e 100644 --- a/man/get_marginaleffects.Rd +++ b/man/get_marginaleffects.Rd @@ -4,7 +4,14 @@ \alias{get_marginaleffects} \title{Easy marginaleffects} \usage{ -get_marginaleffects(model, trend = NULL, at = NULL, fixed = NULL, ...) +get_marginaleffects( + model, + trend = NULL, + by = NULL, + fixed = NULL, + at = NULL, + ... +) } \arguments{ \item{model}{A statistical model.} @@ -12,6 +19,11 @@ get_marginaleffects(model, trend = NULL, at = NULL, fixed = NULL, ...) \item{trend}{A character indicating the name of the variable for which to compute the slopes.} +\item{by}{The predictor variable(s) at which to evaluate the desired effect +/ mean / contrasts. Other predictors of the model that are not included +here will be collapsed and "averaged" over (the effect will be estimated +across them).} + \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} @@ -25,8 +37,8 @@ Work-in-progress. if (require("marginaleffects")) { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) - get_marginaleffects(model, trend = "Petal.Length", at = "Species") - get_marginaleffects(model, trend = "Petal.Length", at = "Petal.Length") - get_marginaleffects(model, trend = "Petal.Length", at = c("Species", "Petal.Length")) + get_marginaleffects(model, trend = "Petal.Length", by = "Species") + get_marginaleffects(model, trend = "Petal.Length", by = "Petal.Length") + get_marginaleffects(model, trend = "Petal.Length", by = c("Species", "Petal.Length")) } } diff --git a/tests/testthat/test-estimate_contrasts.R b/tests/testthat/test-estimate_contrasts.R index 9a2db2b0..381ea981 100644 --- a/tests/testthat/test-estimate_contrasts.R +++ b/tests/testthat/test-estimate_contrasts.R @@ -8,10 +8,10 @@ test_that("estimate_contrasts - Frequentist", { model <- lm(Sepal.Width ~ Species, data = dat) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) - estim <- suppressMessages(estimate_contrasts(model, at = "Species=c('versicolor', 'virginica')")) - expect_equal(dim(estim), c(1, 9)) + estim <- suppressMessages(estimate_contrasts(model, by = "Species=c('versicolor', 'virginica')")) + expect_identical(dim(estim), c(1L, 9L)) # Two factors dat <- iris @@ -21,29 +21,29 @@ test_that("estimate_contrasts - Frequentist", { model <- lm(Sepal.Width ~ Species * fac, data = dat) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) estim <- suppressMessages(estimate_contrasts(model, levels = "Species")) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) estim <- suppressMessages(estimate_contrasts(model, fixed = "fac")) - expect_equal(dim(estim), c(3, 10)) + expect_identical(dim(estim), c(3L, 10L)) # One factor and one continuous model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) estim <- suppressMessages(estimate_contrasts(model, fixed = "Petal.Width")) - expect_equal(dim(estim), c(3, 10)) - estim <- suppressMessages(estimate_contrasts(model, at = "Petal.Width", length = 4)) - expect_equal(dim(estim), c(12, 10)) + expect_identical(dim(estim), c(3L, 10L)) + estim <- suppressMessages(estimate_contrasts(model, by = "Petal.Width", length = 4)) + expect_identical(dim(estim), c(12L, 10L)) # Contrast between continuous model <- lm(Sepal.Width ~ Petal.Length, data = iris) - estim <- suppressMessages(estimate_contrasts(model, at = "Petal.Length=c(2.3, 3)")) - expect_equal(dim(estim), c(1, 9)) - estim <- suppressMessages(estimate_contrasts(model, at = "Petal.Length=c(2, 3, 4)")) - expect_equal(dim(estim), c(3, 9)) + estim <- suppressMessages(estimate_contrasts(model, by = "Petal.Length=c(2.3, 3)")) + expect_identical(dim(estim), c(1L, 9L)) + estim <- suppressMessages(estimate_contrasts(model, by = "Petal.Length=c(2, 3, 4)")) + expect_identical(dim(estim), c(3L, 9L)) # Three factors @@ -52,12 +52,12 @@ test_that("estimate_contrasts - Frequentist", { dat <<- dat model <- lm(mpg ~ gear * vs * am, data = dat) - estim <- suppressMessages(estimate_contrasts(model, at = "all")) - expect_equal(dim(estim), c(12, 11)) + estim <- suppressMessages(estimate_contrasts(model, by = "all")) + expect_identical(dim(estim), c(12L, 11L)) estim <- suppressMessages(estimate_contrasts(model, contrast = c("vs", "am"), fixed = "gear")) - expect_equal(dim(estim), c(6, 10)) - estim <- suppressMessages(estimate_contrasts(model, contrast = c("vs", "am"), at = "gear='5'")) - expect_equal(dim(estim), c(1, 10)) + expect_identical(dim(estim), c(6L, 10L)) + estim <- suppressMessages(estimate_contrasts(model, contrast = c("vs", "am"), by = "gear='5'")) + expect_identical(dim(estim), c(1L, 10L)) dat <- iris @@ -68,14 +68,14 @@ test_that("estimate_contrasts - Frequentist", { model <- lm(Petal.Width ~ factor1 * factor2 * factor3, data = dat) - estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2", "factor3"), at = "all")) - expect_equal(dim(estim), c(28, 9)) + estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2", "factor3"), by = "all")) + expect_identical(dim(estim), c(28L, 9L)) estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2"), fixed = "factor3")) - expect_equal(dim(estim), c(6, 10)) - estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2"), at = "factor3='F'")) - expect_equal(dim(estim), c(6, 10)) - estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2"), at = "factor3")) - expect_equal(dim(estim), c(12, 10)) + expect_identical(dim(estim), c(6L, 10L)) + estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2"), by = "factor3='F'")) + expect_identical(dim(estim), c(6L, 10L)) + estim <- suppressMessages(estimate_contrasts(model, contrast = c("factor1", "factor2"), by = "factor3")) + expect_identical(dim(estim), c(12L, 10L)) # Mixed models @@ -84,7 +84,7 @@ test_that("estimate_contrasts - Frequentist", { model <- lme4::lmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = data) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) # GLM - binomial @@ -94,9 +94,9 @@ test_that("estimate_contrasts - Frequentist", { model <- glm(y ~ Species, family = "binomial", data = dat) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) estim <- suppressMessages(estimate_contrasts(model, transform = "response")) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) # GLM - poisson dat <- data.frame( @@ -107,7 +107,7 @@ test_that("estimate_contrasts - Frequentist", { model <- glm(counts ~ treatment, data = dat, family = poisson()) estim <- suppressMessages(estimate_contrasts(model, transform = "response")) - expect_equal(dim(estim), c(3, 9)) + expect_identical(dim(estim), c(3L, 9L)) }) @@ -131,9 +131,9 @@ test_that("estimate_contrasts - Bayesian", { ) ) estim <- suppressMessages(estimate_contrasts(model, contrast = "all")) - expect_equal(dim(estim), c(15, 7)) + expect_identical(dim(estim), c(15L, 7L)) estim <- suppressMessages(estimate_contrasts(model, fixed = "Petal.Length_factor")) - expect_equal(dim(estim), c(3, 8)) + expect_identical(dim(estim), c(3L, 8L)) model <- suppressWarnings( rstanarm::stan_glm( @@ -145,11 +145,11 @@ test_that("estimate_contrasts - Bayesian", { ) ) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 7)) + expect_identical(dim(estim), c(3L, 7L)) estim <- suppressMessages(estimate_contrasts(model, fixed = "Petal.Width")) - expect_equal(dim(estim), c(3, 8)) - estim <- suppressMessages(estimate_contrasts(model, at = "Petal.Width", length = 4)) - expect_equal(dim(estim), c(12, 8)) + expect_identical(dim(estim), c(3L, 8L)) + estim <- suppressMessages(estimate_contrasts(model, by = "Petal.Width", length = 4)) + expect_identical(dim(estim), c(12L, 8L)) # GLM dat <- iris @@ -161,14 +161,14 @@ test_that("estimate_contrasts - Bayesian", { )) estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(3, 7)) + expect_identical(dim(estim), c(3L, 7L)) estim <- suppressMessages(estimate_contrasts(model, transform = "response")) - expect_equal(dim(estim), c(3, 7)) + expect_identical(dim(estim), c(3L, 7L)) estim <- suppressWarnings(suppressMessages(estimate_contrasts(model, test = "bf"))) - expect_equal(dim(estim), c(3, 6)) + expect_identical(dim(estim), c(3L, 6L)) estim <- suppressWarnings(suppressMessages(estimate_contrasts(model, transform = "response", test = "bf"))) - expect_equal(dim(estim), c(3, 6)) + expect_identical(dim(estim), c(3L, 6L)) }) diff --git a/tests/testthat/test-glmmTMB.R b/tests/testthat/test-glmmTMB.R index ab844581..61d72c8b 100644 --- a/tests/testthat/test-glmmTMB.R +++ b/tests/testthat/test-glmmTMB.R @@ -28,14 +28,14 @@ test_that("estimate_means - glmmTMB", { test_that("estimate_contrasts - glmmTMB", { estim <- suppressMessages(estimate_contrasts(model)) - expect_equal(dim(estim), c(1, 9)) + expect_identical(dim(estim), c(1L, 9L)) expect_equal(estim$Difference, -1.141923, tolerance = 1e-1) - expect_equal(c(estim$Level1[1], estim$Level2[1]), c("yes", "no")) + expect_identical(c(estim$Level1[1], estim$Level2[1]), c("yes", "no")) estim <- suppressMessages(estimate_contrasts(model, component = "zi")) - expect_equal(dim(estim), c(1, 9)) + expect_identical(dim(estim), c(1L, 9L)) expect_equal(estim$Difference, 1.736067, tolerance = 1e-1) - expect_equal(c(estim$Level1[1], estim$Level2[1]), c("yes", "no")) + expect_identical(c(estim$Level1[1], estim$Level2[1]), c("yes", "no")) }) test_that("estimate_slope - glmmTMB", { @@ -50,15 +50,15 @@ test_that("estimate_slope - glmmTMB", { test_that("estimate_response - glmmTMB", { estim <- suppressMessages(estimate_expectation(model2)) - expect_equal(dim(estim), c(nrow(data), 8)) + expect_identical(dim(estim), c(nrow(data), 8L)) }) test_that("estimate_link - glmmTMB", { estim <- suppressMessages(estimate_link(model2, preserve_range = FALSE)) - expect_equal(dim(estim), c(20, 7)) + expect_identical(dim(estim), c(20L, 7L)) }) test_that("estimate_response - glmmTMB", { estim <- suppressMessages(estimate_expectation(model2)) - expect_equal(dim(estim), c(644, 8)) + expect_identical(dim(estim), c(644L, 8L)) }) From e5f5ad7c2523fd78058b6a19641dc1e8a9cc29ca Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 08:20:25 +0200 Subject: [PATCH 04/24] updates --- README.Rmd | 8 +- README.md | 19 ++- man/figures/unnamed-chunk-10-1.png | Bin 55420 -> 21297 bytes man/figures/unnamed-chunk-12-1.png | Bin 93686 -> 13298 bytes man/figures/unnamed-chunk-14-1.png | Bin 89473 -> 25031 bytes man/figures/unnamed-chunk-15-1.png | Bin 244912 -> 36138 bytes man/figures/unnamed-chunk-16-1.png | Bin 336784 -> 78946 bytes man/figures/unnamed-chunk-17-1.png | Bin 248573 -> 41037 bytes man/figures/unnamed-chunk-3-1.png | Bin 151153 -> 21428 bytes man/figures/unnamed-chunk-4-1.png | Bin 129461 -> 21716 bytes man/figures/unnamed-chunk-6-1.png | Bin 75513 -> 17089 bytes man/figures/unnamed-chunk-8-1.png | Bin 96680 -> 16303 bytes man/figures/unnamed-chunk-9-1.png | Bin 108319 -> 13683 bytes .../testthat/test-attributes_visualisation.R | 16 +-- tests/testthat/test-brms.R | 8 +- tests/testthat/test-estimate_means.R | 108 +++++++++--------- tests/testthat/test-estimate_predicted.R | 2 +- tests/testthat/test-estimate_slopes.R | 12 +- tests/testthat/test-get_marginaleffects.R | 4 +- tests/testthat/test-glmmTMB.R | 2 +- vignettes/derivatives.Rmd | 10 +- vignettes/describe_nonlinear.Rmd | 2 +- vignettes/estimate_contrasts.Rmd | 2 +- vignettes/estimate_slopes.Rmd | 6 +- vignettes/modelisation_approach.Rmd | 2 +- 25 files changed, 105 insertions(+), 96 deletions(-) diff --git a/README.Rmd b/README.Rmd index 4b4aa44f..f31d029b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -207,12 +207,12 @@ plot(contrasts, estimate_means(model)) + ```{r} model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) -estimate_contrasts(model, at = "Petal.Length", length = 3) +estimate_contrasts(model, by = "Petal.Length", length = 3) ``` ```{r} # Recompute contrasts with a higher precision (for a smoother plot) -contrasts <- estimate_contrasts(model, at = "Petal.Length", length = 20) +contrasts <- estimate_contrasts(model, by = "Petal.Length", length = 20) # Add Contrast column by concatenating contrasts$Contrast <- paste(contrasts$Level1, "-", contrasts$Level2) @@ -302,7 +302,7 @@ model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris) # 1. Compute derivatives deriv <- estimate_slopes(model, trend = "Petal.Length", - at = "Petal.Length", + by = "Petal.Length", length = 100 ) @@ -363,7 +363,7 @@ For instance, the plot below shows that the effect of `hp` (the y-axis) is signi ```{r} model <- lm(mpg ~ hp * wt, data = mtcars) -slopes <- estimate_slopes(model, trend = "hp", at = "wt") +slopes <- estimate_slopes(model, trend = "hp", by = "wt") plot(slopes) ``` diff --git a/README.md b/README.md index 4fa0d68f..1ce9cc8b 100644 --- a/README.md +++ b/README.md @@ -163,7 +163,10 @@ means ## versicolor | 2.77 | 0.05 | [2.68, 2.86] ## virginica | 2.97 | 0.05 | [2.88, 3.07] ## -## Marginal means estimated at Species +## Marginal means estimated at +``` + +``` r # 3. Plot ggplot(iris, aes(x = Species, y = Sepal.Width)) + @@ -233,7 +236,7 @@ contrasts ``` r model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) -estimate_contrasts(model, at = "Petal.Length", length = 3) +estimate_contrasts(model, by = "Petal.Length", length = 3) ## Marginal Contrasts Analysis ## ## Level1 | Level2 | Petal.Length | Difference | 95% CI | SE | t(144) | p @@ -254,7 +257,7 @@ estimate_contrasts(model, at = "Petal.Length", length = 3) ``` r # Recompute contrasts with a higher precision (for a smoother plot) -contrasts <- estimate_contrasts(model, at = "Petal.Length", length = 20) +contrasts <- estimate_contrasts(model, by = "Petal.Length", length = 20) # Add Contrast column by concatenating contrasts$Contrast <- paste(contrasts$Level1, "-", contrasts$Level2) @@ -306,6 +309,9 @@ head(pred1, n = 5) ## 5.00 | 2.19 | 0.10 | [1.99, 2.39] | -0.79 | 1.40 ## ## Variable predicted: Petal.Length +``` + +``` r # Same for model 2 model2 <- lm(Petal.Length ~ Sepal.Length * Species, data = iris) @@ -354,6 +360,9 @@ random ## cyl | 6 | drat | -0.09 | 0.54 | [-1.15, 0.98] ## cyl | 8 | (Intercept) | 3.32 | 0.73 | [ 1.89, 4.74] ## cyl | 8 | drat | -2.15 | 0.47 | [-3.07, -1.23] +``` + +``` r plot(random) ``` @@ -391,7 +400,7 @@ model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris) # 1. Compute derivatives deriv <- estimate_slopes(model, trend = "Petal.Length", - at = "Petal.Length", + by = "Petal.Length", length = 100 ) @@ -465,7 +474,7 @@ is significantly negative only when `wt` is low (`< ~4`). ``` r model <- lm(mpg ~ hp * wt, data = mtcars) -slopes <- estimate_slopes(model, trend = "hp", at = "wt") +slopes <- estimate_slopes(model, trend = "hp", by = "wt") plot(slopes) ``` diff --git a/man/figures/unnamed-chunk-10-1.png b/man/figures/unnamed-chunk-10-1.png index f9edfe307d32183f7ce838101bd0304685b12d28..02771cd006ca7368bdba7f87a6bc58e0da855297 100644 GIT binary patch literal 21297 zcmeG^3sh6rvRv&gqDcHBqNLUjRg|Dqqx?=AR9dTIMMVn|6%8r~D)J5FRQWyS+g3&7 zlN+g`B1A=gLK2aJs6ar)2mvDBM1BDh^5vZKc2M8#65oCJ>eaX2;;c;;Ihj4P&&=%E zvuEb)Q=2#0O&mXKyq1>M#P7dbvqekm?; z@OxQIS!+xg7H6RqN3n<-LKFv(#pT51@Z3dJ;Liau>KyrQr?-}t{$$PH-ySZM zKw4T~Xnnut+pYdd-N~_ZXEz_Z9hxvH^oF}DapR*4WAqNkeLr?f-L$$tZtpf;T6Ozi z!{u>FUr+k&_Ns$>?|+xwxp(q>?DSHxfS({wu1&Tq5FYRos;w_j<*VuTC%2~ur$}Nl 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a/tests/testthat/test-attributes_visualisation.R b/tests/testthat/test-attributes_visualisation.R index 7cc27833..6751932e 100644 --- a/tests/testthat/test-attributes_visualisation.R +++ b/tests/testthat/test-attributes_visualisation.R @@ -3,15 +3,15 @@ test_that("attributes_means", { model <- lm(Sepal.Length ~ Species * Sepal.Width, data = iris) estim <- suppressMessages(estimate_means(model)) - expect_identical(attributes(estim)$at, "Species") - expect_identical(attributes(estim)$fixed, NULL) + expect_identical(attributes(estim)$by, "Species") + expect_null(attributes(estim)$fixed) estim <- suppressMessages(estimate_means(model, fixed = "Sepal.Width")) - expect_identical(attributes(estim)$at, "Species") + expect_identical(attributes(estim)$by, "Species") expect_identical(attributes(estim)$fixed, "Sepal.Width") - estim <- suppressMessages(estimate_means(model, at = "all")) - expect_identical(attributes(estim)$at, c("Species", "Sepal.Width")) + estim <- suppressMessages(estimate_means(model, by = "all")) + expect_identical(attributes(estim)$by, c("Species", "Sepal.Width")) }) @@ -22,13 +22,13 @@ test_that("attributes_contrasts", { estim <- suppressMessages(estimate_contrasts(model)) expect_identical(attributes(estim)$contrast, "Species") - expect_identical(attributes(estim)$at, NULL) - expect_identical(attributes(estim)$fixed, NULL) + expect_null(attributes(estim)$by) + expect_null(attributes(estim)$fixed) estim <- suppressMessages(estimate_contrasts(model, fixed = "Sepal.Width")) expect_identical(attributes(estim)$contrast, "Species") expect_identical(attributes(estim)$fixed, "Sepal.Width") - expect_identical(attributes(estim)$modulate, NULL) + expect_null(attributes(estim)$modulate) }) diff --git a/tests/testthat/test-brms.R b/tests/testthat/test-brms.R index 2350ae10..a71177b7 100644 --- a/tests/testthat/test-brms.R +++ b/tests/testthat/test-brms.R @@ -5,7 +5,7 @@ test_that("estimate_means - brms", { skip_if_not_installed("emmeans") model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000) estim <- estimate_means(model) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) }) test_that("estimate_relation - brms", { @@ -13,7 +13,7 @@ test_that("estimate_relation - brms", { skip_if_not_installed("emmeans") model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000) estim <- estimate_relation(model, preserve_range = FALSE) - expect_equal(dim(estim), c(30, 6)) + expect_identical(dim(estim), c(30L, 6L)) # estim <- estimate_relation(model, preserve_range=FALSE, iterations = 10) # expect_equal(dim(estim), c(30, 6)) @@ -23,6 +23,6 @@ test_that("estimate_means - brms", { skip_if_not_installed("brms") skip_if_not_installed("emmeans") model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000) - estim <- estimate_slopes(model, at = "Species") - expect_equal(dim(estim), c(3, 5)) + estim <- estimate_slopes(model, by = "Species") + expect_identical(dim(estim), c(3L, 5L)) }) diff --git a/tests/testthat/test-estimate_means.R b/tests/testthat/test-estimate_means.R index 5eee7896..e686d873 100644 --- a/tests/testthat/test-estimate_means.R +++ b/tests/testthat/test-estimate_means.R @@ -13,26 +13,26 @@ test_that("estimate_means() - core", { # Simple model <- lm(vs ~ cyl, data = dat) estim1 <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim1), c(3, 5)) + expect_identical(dim(estim1), c(3L, 5L)) estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects")) - expect_equal(dim(estim2), c(3, 5)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + expect_identical(dim(estim2), c(3L, 5L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) # Interaction (factor * continuous) model <- lm(mpg ~ wt * gear, data = dat) estim1 <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim1), c(3, 5)) + expect_identical(dim(estim1), c(3L, 5L)) estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects")) - expect_equal(dim(estim2), c(3, 6)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + expect_identical(dim(estim2), c(3L, 6L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) # At specific levels model <- lm(Sepal.Width ~ Species, data = iris) - estim1 <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'virginica')")) - expect_equal(dim(estim1), c(2, 5)) - estim2 <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'virginica')", backend = "marginaleffects")) - expect_equal(dim(estim2), c(2, 5)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + estim1 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')")) + expect_identical(dim(estim1), c(2L, 5L)) + estim2 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')", backend = "marginaleffects")) + expect_identical(dim(estim2), c(2L, 5L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) # Interactions between factors dat <- iris @@ -40,24 +40,24 @@ test_that("estimate_means() - core", { dat <<- dat model <- lm(Sepal.Width ~ Species * Petal.Length_factor, data = dat) - estim1 <- suppressMessages(estimate_means(model, at = "all")) - expect_equal(dim(estim1), c(6, 6)) - estim2 <- suppressWarnings(suppressMessages(estimate_means(model, at = "all", backend = "marginaleffects"))) - expect_equal(dim(estim2), c(6, 6)) + estim1 <- suppressMessages(estimate_means(model, by = "all")) + expect_identical(dim(estim1), c(6L, 6L)) + estim2 <- suppressWarnings(suppressMessages(estimate_means(model, by = "all", backend = "marginaleffects"))) + expect_identical(dim(estim2), c(6L, 6L)) # No interaction (two factors) model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris) estim1 <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim1), c(3, 5)) + expect_identical(dim(estim1), c(3L, 5L)) estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects")) - expect_equal(dim(estim2), c(3, 6)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + expect_identical(dim(estim2), c(3L, 6L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) # At specific levels of continuous - estim1 <- suppressMessages(estimate_means(model, at = "Sepal.Width")) + estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width")) expect_equal(dim(estim1), c(10, 5)) - estim2 <- suppressMessages(estimate_means(model, at = "Sepal.Width", backend = "marginaleffects")) + estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width", backend = "marginaleffects")) expect_equal(dim(estim2), c(10, 6)) # Note that the absolute values are different here... for unclear reasons expect_true(max(diff(estim1$Mean) - diff(estim2$Mean)) < 1e-10) @@ -69,44 +69,44 @@ test_that("estimate_means() - core", { model <- glm(y ~ Species, family = "binomial", data = dat) estim <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) estim <- suppressMessages(estimate_means(model, transform = "response")) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) expect_true(all(estim$Probability >= 0) & all(estim$Probability <= 1)) model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris) estim <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) - estim <- suppressMessages(estimate_means(model, at = "all")) + estim <- suppressMessages(estimate_means(model, by = "all")) expect_equal(dim(estim), c(30, 6)) # In formula modification # FIXME: this got broken but it seems just to tedious to fix. Don't use in formula transforms. # model <- lm(mpg ~ wt * as.factor(gear), data = mtcars) # estim <- suppressMessages(estimate_means(model)) - # expect_equal(dim(estim), c(3, 5)) + # expect_equal(dim(estim), c(3L, 5L)) # One continuous and one factor model <- lm(Petal.Length ~ Species * Sepal.Width, data = iris) estim <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) estim <- suppressMessages(estimate_means(model, fixed = "Sepal.Width")) - expect_equal(dim(estim), c(3, 6)) - estim <- suppressMessages(estimate_means(model, at = c("Species", "Sepal.Width"), length = 2)) - expect_equal(dim(estim), c(6, 6)) - estim <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'setosa')")) - expect_equal(dim(estim), c(2, 5)) - estim <- suppressMessages(estimate_means(model, at = "Sepal.Width=c(2, 4)")) - expect_equal(dim(estim), c(2, 5)) - estim <- suppressMessages(estimate_means(model, at = c("Species", "Sepal.Width=0"))) - expect_equal(dim(estim), c(3, 6)) - estim <- suppressMessages(estimate_means(model, at = "Sepal.Width", length = 5)) + expect_identical(dim(estim), c(3L, 6L)) + estim <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width"), length = 2)) + expect_identical(dim(estim), c(6L, 6L)) + estim <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'setosa')")) + expect_identical(dim(estim), c(2L, 5L)) + estim <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)")) + expect_identical(dim(estim), c(2L, 5L)) + estim <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width=0"))) + expect_identical(dim(estim), c(3L, 6L)) + estim <- suppressMessages(estimate_means(model, by = "Sepal.Width", length = 5)) expect_equal(dim(estim), c(5, 5)) - estim <- suppressMessages(estimate_means(model, at = "Sepal.Width=c(2, 4)")) - expect_equal(dim(estim), c(2, 5)) + estim <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)")) + expect_identical(dim(estim), c(2L, 5L)) # Two factors dat <- iris @@ -114,12 +114,12 @@ test_that("estimate_means() - core", { dat <<- dat model <- lm(Petal.Length ~ Species * Petal.Length_factor, data = dat) - estim <- suppressMessages(estimate_means(model, at = "all")) - expect_equal(dim(estim), c(6, 6)) - estim <- suppressMessages(estimate_means(model, at = "Petal.Length_factor")) - expect_equal(dim(estim), c(2, 5)) - estim <- suppressMessages(estimate_means(model, at = "Petal.Length_factor='B'")) - expect_true(as.character(unique(estim$Petal.Length_factor)) == "B") + estim <- suppressMessages(estimate_means(model, by = "all")) + expect_identical(dim(estim), c(6L, 6L)) + estim <- suppressMessages(estimate_means(model, by = "Petal.Length_factor")) + expect_identical(dim(estim), c(2L, 5L)) + estim <- suppressMessages(estimate_means(model, by = "Petal.Length_factor='B'")) + expect_identical(as.character(unique(estim$Petal.Length_factor)), "B") # Three factors @@ -132,7 +132,7 @@ test_that("estimate_means() - core", { expect_equal(dim(estim), c(12, 7)) estim <- suppressMessages(estimate_means(model, fixed = "am")) expect_equal(dim(estim), c(6, 7)) - estim <- suppressMessages(estimate_means(model, at = c("gear='5'", "vs"))) + estim <- suppressMessages(estimate_means(model, by = c("gear='5'", "vs"))) expect_equal(dim(estim), c(2, 7)) dat <- iris @@ -156,13 +156,13 @@ test_that("estimate_means() - core", { model <- glm(Petal.Length_factor ~ Species, data = dat, family = "binomial") estim <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) estim <- suppressMessages(estimate_means(model, transform = "none")) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) model <- glm(Petal.Length ~ Species, data = iris, family = "Gamma") estim <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim), c(3, 5)) + expect_identical(dim(estim), c(3L, 5L)) }) test_that("estimate_means() - mixed models", { @@ -176,15 +176,15 @@ test_that("estimate_means() - mixed models", { model <- lme4::lmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = dat) estim1 <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim1), c(3, 5)) + expect_identical(dim(estim1), c(3L, 5L)) estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects")) - expect_equal(dim(estim2), c(3, 5)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + expect_identical(dim(estim2), c(3L, 5L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) model <- lme4::glmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = dat, family = "Gamma") estim1 <- suppressMessages(estimate_means(model)) - expect_equal(dim(estim1), c(3, 5)) + expect_identical(dim(estim1), c(3L, 5L)) estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects")) - expect_equal(dim(estim2), c(3, 5)) - expect_true(max(estim1$Mean - estim2$Mean) < 1e-10) + expect_identical(dim(estim2), c(3L, 5L)) + expect_lt(max(estim1$Mean - estim2$Mean), 1e-10) }) diff --git a/tests/testthat/test-estimate_predicted.R b/tests/testthat/test-estimate_predicted.R index 530887a2..17ee21e5 100644 --- a/tests/testthat/test-estimate_predicted.R +++ b/tests/testthat/test-estimate_predicted.R @@ -137,7 +137,7 @@ test_that("estimate_response - Frequentist", { expect_equal(dim(estim), c(32, 4)) model <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial") - estim <- estimate_link(model, at = "wt") + estim <- estimate_link(model, by = "wt") expect_equal(dim(estim), c(10, 6)) diff --git a/tests/testthat/test-estimate_slopes.R b/tests/testthat/test-estimate_slopes.R index 56dd3a79..301783fb 100644 --- a/tests/testthat/test-estimate_slopes.R +++ b/tests/testthat/test-estimate_slopes.R @@ -6,21 +6,21 @@ test_that("estimate_slopes", { estim <- suppressMessages(estimate_slopes(model)) expect_equal(dim(estim), c(1, 8)) - estim <- suppressMessages(estimate_slopes(model, at = "Species")) + estim <- suppressMessages(estimate_slopes(model, by = "Species")) expect_equal(dim(estim), c(3, 9)) - estim <- suppressMessages(estimate_slopes(model, at = "Petal.Length")) + estim <- suppressMessages(estimate_slopes(model, by = "Petal.Length")) expect_equal(dim(estim), c(10, 9)) - estim <- suppressMessages(estimate_slopes(model, at = c("Species", "Petal.Length"))) + estim <- suppressMessages(estimate_slopes(model, by = c("Species", "Petal.Length"))) expect_equal(dim(estim), c(30, 10)) model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) - estim <- suppressMessages(estimate_slopes(model, at = "Sepal.Width")) + estim <- suppressMessages(estimate_slopes(model, by = "Sepal.Width")) expect_equal(dim(estim), c(10, 9)) - estim <- suppressMessages(estimate_slopes(model, at = "Sepal.Width", length = 5)) + estim <- suppressMessages(estimate_slopes(model, by = "Sepal.Width", length = 5)) expect_equal(dim(estim), c(5, 9)) - estim <- suppressMessages(estimate_slopes(model, at = "Sepal.Width = c(1, 2, 3)")) + estim <- suppressMessages(estimate_slopes(model, by = "Sepal.Width = c(1, 2, 3)")) expect_equal(dim(estim), c(3, 9)) }) diff --git a/tests/testthat/test-get_marginaleffects.R b/tests/testthat/test-get_marginaleffects.R index d0e8826c..15dbbc9a 100644 --- a/tests/testthat/test-get_marginaleffects.R +++ b/tests/testthat/test-get_marginaleffects.R @@ -4,7 +4,7 @@ test_that("get_marginaleffects", { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) - expect_equal(nrow(get_marginaleffects(model, trend = "Petal.Length", at = "Species")), 3L) + expect_equal(nrow(get_marginaleffects(model, trend = "Petal.Length", by = "Species")), 3L) - # get_marginaleffects(model, trend = "Petal.Length", at = "Species", length = 10) + # get_marginaleffects(model, trend = "Petal.Length", by = "Species", length = 10) }) diff --git a/tests/testthat/test-glmmTMB.R b/tests/testthat/test-glmmTMB.R index 61d72c8b..da0988a8 100644 --- a/tests/testthat/test-glmmTMB.R +++ b/tests/testthat/test-glmmTMB.R @@ -41,7 +41,7 @@ test_that("estimate_contrasts - glmmTMB", { test_that("estimate_slope - glmmTMB", { estim <- suppressMessages(estimate_slopes(model2, trend = "cover", - at = "mined", regrid = "response" + by = "mined", regrid = "response" )) estim2 <- as.data.frame(emmeans::emtrends(model2, "mined", var = "cover", regrid = "response")) expect_equal(estim$Coefficient, estim2$cover.trend, tolerance = 1e-2) diff --git a/vignettes/derivatives.Rmd b/vignettes/derivatives.Rmd index c7b72897..af66bd34 100644 --- a/vignettes/derivatives.Rmd +++ b/vignettes/derivatives.Rmd @@ -143,7 +143,7 @@ We know that the slope is `12.75` (from our parameters analysis). Does it change To compute the derivative, we can use the `estimate_slopes()` function, and specify that we want to know: the trend of *x* **over the course of** ("at") itself. ```{r} -deriv <- modelbased::estimate_slopes(model_lm, trend = "x", at = "x") +deriv <- modelbased::estimate_slopes(model_lm, trend = "x", by = "x") plot(deriv) + # add a dashed line at 0 to show absence of effect geom_hline(yintercept = 0, linetype = "dashed") @@ -162,7 +162,7 @@ Such as GAMs. Lets' to the same for our GAM model: ```{r, eval=FALSE} -deriv <- modelbased::estimate_slopes(model_gam, trend = "x", at = "x") +deriv <- modelbased::estimate_slopes(model_gam, trend = "x", by = "x") plot(deriv, line = list(color = "blue")) + geom_hline(yintercept = 0, linetype = "dashed") @@ -205,7 +205,7 @@ Instead, we can rely on our *good ol'* derivatives to obtain the "linear slope" ```{r} -deriv <- modelbased::estimate_slopes(model_poly, trend = "x", at = "x") +deriv <- modelbased::estimate_slopes(model_poly, trend = "x", by = "x") plot(deriv) + geom_hline(yintercept = 0, linetype = "dashed") @@ -228,7 +228,7 @@ model_gam2 <- mgcv::gam(y2 ~ s(x), data = data) plot(modelbased::estimate_relation(model_gam2, length = 100), line = list(color = "blue")) # Increase precision -deriv <- modelbased::estimate_slopes(model_gam2, trend = "x", at = "x", length = 100) +deriv <- modelbased::estimate_slopes(model_gam2, trend = "x", by = "x", length = 100) plot(deriv, line = list(color = "blue")) + geom_hline(yintercept = 0, linetype = "dashed") @@ -250,7 +250,7 @@ model_gam3 <- mgcv::gam(y3 ~ s(x), data = data) plot(modelbased::estimate_relation(model_gam3, length = 100), line = list(color = "blue")) -deriv <- modelbased::estimate_slopes(model_gam3, trend = "x", at = "x", length = 100) +deriv <- modelbased::estimate_slopes(model_gam3, trend = "x", by = "x", length = 100) plot(deriv, line = list(color = "blue")) + geom_hline(yintercept = 0, linetype = "dashed") diff --git a/vignettes/describe_nonlinear.Rmd b/vignettes/describe_nonlinear.Rmd index 06c3c760..9b7ee988 100644 --- a/vignettes/describe_nonlinear.Rmd +++ b/vignettes/describe_nonlinear.Rmd @@ -159,7 +159,7 @@ We can visualize the link between the Delay and the Memory score by using the ```{r} library(modelbased) -estim <- estimate_relation(model, at = "Delay", ci = c(0.50, 0.69, 0.89, 0.97)) +estim <- estimate_relation(model, by = "Delay", ci = c(0.50, 0.69, 0.89, 0.97)) ggplot(estim, aes(x = Delay, y = Predicted)) + geom_jitter(data = df, aes(y = Memory), width = 0.2, height = 0.2) + diff --git a/vignettes/estimate_contrasts.Rmd b/vignettes/estimate_contrasts.Rmd index 2d36d06e..785e66ce 100644 --- a/vignettes/estimate_contrasts.Rmd +++ b/vignettes/estimate_contrasts.Rmd @@ -111,7 +111,7 @@ continuous variable. Based on the model above (including the interaction with `Petal.Width`, that we will then visualise. ```{r, fig} -contrasts <- estimate_contrasts(model, at = "Petal.Width", length = 100) +contrasts <- estimate_contrasts(model, by = "Petal.Width", length = 100) # Create a variable with the two levels concatenated contrasts$Contrast <- paste(contrasts$Level1, "-", contrasts$Level2) diff --git a/vignettes/estimate_slopes.Rmd b/vignettes/estimate_slopes.Rmd index a8589493..a365d937 100644 --- a/vignettes/estimate_slopes.Rmd +++ b/vignettes/estimate_slopes.Rmd @@ -85,7 +85,7 @@ We can see that the effect of `Petal.Length`, **marginalized over Species**, is ```{r} -slopes <- estimate_slopes(model, trend = "Petal.Length", at = "Species") +slopes <- estimate_slopes(model, trend = "Petal.Length", by = "Species") slopes plot(slopes) @@ -105,7 +105,7 @@ library(modelbased) model <- lm(mpg ~ hp * wt, data = mtcars) -slopes <- estimate_slopes(model, trend = "hp", at = "wt") +slopes <- estimate_slopes(model, trend = "hp", by = "wt") plot(slopes) ``` @@ -144,7 +144,7 @@ Because the meaning of these parameters is somewhat disconnected with our need f # Compute derivative deriv <- estimate_slopes(model, trend = "Petal.Length", - at = "Petal.Length", + by = "Petal.Length", length = 100 ) diff --git a/vignettes/modelisation_approach.Rmd b/vignettes/modelisation_approach.Rmd index 39a83fad..869390a3 100644 --- a/vignettes/modelisation_approach.Rmd +++ b/vignettes/modelisation_approach.Rmd @@ -160,7 +160,7 @@ What can we conclude from that? Absolutely **nothing**! We need to investigate i ## Post-hoc comparison tests ```{r} -posthoc <- model_emmeans(results, at = c("Condition", "Category")) %>% +posthoc <- model_emmeans(results, by = c("Condition", "Category")) %>% pairs() parameters(posthoc) ``` From 276c4f3e85c754d9b82d34240a37ed48a4757426 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 08:25:10 +0200 Subject: [PATCH 05/24] fix? --- R/estimate_contrasts.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/estimate_contrasts.R b/R/estimate_contrasts.R index 8b3beda2..82debbce 100644 --- a/R/estimate_contrasts.R +++ b/R/estimate_contrasts.R @@ -112,7 +112,7 @@ estimate_contrasts <- function(model, # Summarize and clean if (insight::model_info(model)$is_bayesian) { - out <- cbind(estimated@grid, bayestestR::describe_posterior(estimated, ci = ci, ...)) + out <- cbind(estimated@grid, bayestestR::describe_posterior(estimated, ci = ci, verbose = FALSE, ...)) out <- .clean_names_bayesian(out, model, transform, type = "contrast") } else { out <- as.data.frame(merge( From cc0062db0cd3ef4818996f15f0f2aee82e23764d Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 10:49:57 +0200 Subject: [PATCH 06/24] fix --- R/get_emmeans.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/get_emmeans.R b/R/get_emmeans.R index e0f58938..6bc17b97 100644 --- a/R/get_emmeans.R +++ b/R/get_emmeans.R @@ -130,7 +130,8 @@ model_emmeans <- get_emmeans info <- attributes(estimated) - attr(means, "at") <- info$at + attr(means, "at") <- info$by + attr(means, "by") <- info$by attr(means, "fixed") <- info$fixed means } From e47a0fdd7804c8930d9715cc253129d394df20dc Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 10:50:49 +0200 Subject: [PATCH 07/24] fix --- tests/testthat/test-estimate_predicted.R | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/tests/testthat/test-estimate_predicted.R b/tests/testthat/test-estimate_predicted.R index 17ee21e5..35089622 100644 --- a/tests/testthat/test-estimate_predicted.R +++ b/tests/testthat/test-estimate_predicted.R @@ -166,11 +166,9 @@ test_that("estimate_response - VisMatrix", { skip_if_not_installed("mgcv") m <- lm(Sepal.Length ~ Petal.Length * Petal.Width, data = iris) - vm <- visualisation_matrix(m, at = c("Petal.Length", "Petal.Width = seq(-3, 3)")) + vm <- visualisation_matrix(m, by = c("Petal.Length", "Petal.Width = seq(-3, 3)")) estim <- estimate_relation(vm) - expect_equal(dim(estim), c(70, 6)) - expect_equal( - colnames(estim), - c("Petal.Length", "Petal.Width", "Predicted", "SE", "CI_low", "CI_high") + expect_identical(dim(estim), c(70L, 6L)) + expect_named(estim, c("Petal.Length", "Petal.Width", "Predicted", "SE", "CI_low", "CI_high") ) }) From 285865463308384e92393d219d71e233bcb9a6f5 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 10:58:56 +0200 Subject: [PATCH 08/24] update --- R/visualisation_matrix.R | 39 +++++---- R/visualisation_recipe.estimate_means.R | 32 ++++---- R/visualisation_recipe.estimate_slopes.R | 32 ++++---- man/visualisation_matrix.Rd | 79 +++++++++++++++---- ...visualisation_recipe.estimate_predicted.Rd | 16 ++-- 5 files changed, 125 insertions(+), 73 deletions(-) diff --git a/R/visualisation_matrix.R b/R/visualisation_matrix.R index 98461f92..80dc97b1 100644 --- a/R/visualisation_matrix.R +++ b/R/visualisation_matrix.R @@ -4,7 +4,7 @@ #' function. Same arguments apply. #' #' @inheritParams insight::get_datagrid -#' @param target Deprecated name. Please use `at` instead. +#' @param target,at Deprecated name. Please use `by` instead. #' #' @return Reference grid data frame. #' @@ -15,24 +15,24 @@ #' data <- rbind(iris, iris[149, ], make.row.names = FALSE) #' #' # Single variable is of interest; all others are "fixed" -#' visualisation_matrix(data, at = "Sepal.Length") -#' visualisation_matrix(data, at = "Sepal.Length", length = 3) -#' visualisation_matrix(data, at = "Sepal.Length", range = "ci", ci = 0.90) -#' visualisation_matrix(data, at = "Sepal.Length", factors = "mode") +#' visualisation_matrix(data, by = "Sepal.Length") +#' visualisation_matrix(data, by = "Sepal.Length", length = 3) +#' visualisation_matrix(data, by = "Sepal.Length", range = "ci", ci = 0.90) +#' visualisation_matrix(data, by = "Sepal.Length", factors = "mode") #' #' # Multiple variables are of interest, creating a combination -#' visualisation_matrix(data, at = c("Sepal.Length", "Species"), length = 3) -#' visualisation_matrix(data, at = c(1, 3), length = 3) -#' visualisation_matrix(data, at = c("Sepal.Length", "Species"), preserve_range = TRUE) -#' visualisation_matrix(data, at = c("Sepal.Length", "Species"), numerics = 0) -#' visualisation_matrix(data, at = c("Sepal.Length = 3", "Species")) -#' visualisation_matrix(data, at = c("Sepal.Length = c(3, 1)", "Species = 'setosa'")) +#' visualisation_matrix(data, by = c("Sepal.Length", "Species"), length = 3) +#' visualisation_matrix(data, by = c(1, 3), length = 3) +#' visualisation_matrix(data, by = c("Sepal.Length", "Species"), preserve_range = TRUE) +#' visualisation_matrix(data, by = c("Sepal.Length", "Species"), numerics = 0) +#' visualisation_matrix(data, by = c("Sepal.Length = 3", "Species")) +#' visualisation_matrix(data, by = c("Sepal.Length = c(3, 1)", "Species = 'setosa'")) #' #' # with list-style at-argument -#' visualisation_matrix(data, at = list(Sepal.Length = c(1, 3), Species = "setosa")) +#' visualisation_matrix(data, by = list(Sepal.Length = c(1, 3), Species = "setosa")) #' #' # Standardize -#' vizdata <- visualisation_matrix(data, at = "Sepal.Length") +#' vizdata <- visualisation_matrix(data, by = "Sepal.Length") #' standardize(vizdata) #' @export visualisation_matrix <- function(x, ...) { @@ -46,20 +46,25 @@ visualisation_matrix <- function(x, ...) { #' @rdname visualisation_matrix #' @export visualisation_matrix.data.frame <- function(x, - at = "all", + by = "all", target = NULL, + at = NULL, factors = "reference", numerics = "mean", preserve_range = FALSE, reference = x, ...) { if (!is.null(target)) { - insight::format_warning("The 'target' argument name is deprecated in favour of 'at'. Please replace 'target' with 'at'.") - at <- target + insight::format_warning("The 'target` argument name is deprecated in favour of `by`. Please replace `target` with `by`.") # nolint + by <- target + } + if (!is.null(at)) { + insight::format_warning("The `at` argument is deprecated and will be removed in the future. Please use `by` instead.") # nolint + by <- at } insight::get_datagrid(x, - at = at, + by = by, factors = factors, numerics = numerics, preserve_range = preserve_range, diff --git a/R/visualisation_recipe.estimate_means.R b/R/visualisation_recipe.estimate_means.R index 8ece2f84..c062f32f 100644 --- a/R/visualisation_recipe.estimate_means.R +++ b/R/visualisation_recipe.estimate_means.R @@ -23,14 +23,14 @@ #' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) #' #' model <- lm(mpg ~ new_factor * cyl * wt, data = data) -#' x <- estimate_means(model, at = c("new_factor", "cyl")) +#' x <- estimate_means(model, by =c("new_factor", "cyl")) #' plot(visualisation_recipe(x)) #' #' # Modulations -------------- -#' x <- estimate_means(model, at = c("new_factor", "wt")) +#' x <- estimate_means(model, by =c("new_factor", "wt")) #' plot(visualisation_recipe(x)) #' -#' # x <- estimate_means(model, at = c("new_factor", "cyl", "wt")) +#' # x <- estimate_means(model, by =c("new_factor", "cyl", "wt")) #' # plot(visualisation_recipe(x)) # TODO: broken #' #' #' # GLMs --------------------- @@ -54,17 +54,17 @@ visualisation_recipe.estimate_means <- function(x, # Main aesthetics ----------------- - data <- as.data.frame(x) + vis_data <- as.data.frame(x) y <- info$response color <- NULL alpha <- NULL - levels <- info$at[info$at %in% names(data[!sapply(data, is.numeric)])] - modulate <- info$at[info$at %in% names(data[sapply(data, is.numeric)])] - x1 <- levels[1] - if (length(levels) > 1L) { - color <- levels[2] - if (length(levels) > 2L) { + by_levels <- info$at[info$at %in% names(vis_data[!sapply(vis_data, is.numeric)])] + modulate <- info$at[info$at %in% names(vis_data[sapply(vis_data, is.numeric)])] + x1 <- by_levels[1] + if (length(by_levels) > 1L) { + color <- by_levels[2] + if (length(by_levels) > 2L) { # TODO: add facetting (needs updating see::geom_from_list to work with facets) insight::format_warning("Cannot deal with more than 2 levels variables for now. Other ones will be omitted.") } @@ -120,7 +120,7 @@ visualisation_recipe.estimate_means <- function(x, # Line layers[[paste0("l", l)]] <- .visualisation_means_line( - data, + vis_data, x1, y = info$coef_name[1], color = color, @@ -131,7 +131,7 @@ visualisation_recipe.estimate_means <- function(x, # Pointrange layers[[paste0("l", l)]] <- .visualisation_means_pointrange( - data, + vis_data, x1, y = info$coef_name[1], color = color, @@ -145,7 +145,7 @@ visualisation_recipe.estimate_means <- function(x, # Out class(layers) <- unique(c("visualisation_recipe", "see_visualisation_recipe", class(layers))) - attr(layers, "data") <- data + attr(layers, "data") <- vis_data layers } @@ -229,16 +229,16 @@ visualisation_recipe.estimate_means <- function(x, .visualisation_means_labs <- function(info, x1, y, labs = NULL) { if (all(info$coef_name == "Probability")) { - title <- "Estimated Mean Probabilities" + vis_title <- "Estimated Mean Probabilities" } else { - title <- "Estimated Means" + vis_title <- "Estimated Means" } out <- list( geom = "labs", x = x1, y = y, - title = paste0(title, " (", format(insight::find_formula(info$model)), ")") + title = paste0(vis_title, " (", format(insight::find_formula(info$model)), ")") ) if (!is.null(labs)) out <- utils::modifyList(out, labs) # Update with additional args out diff --git a/R/visualisation_recipe.estimate_slopes.R b/R/visualisation_recipe.estimate_slopes.R index 028e981b..0f03bc61 100644 --- a/R/visualisation_recipe.estimate_slopes.R +++ b/R/visualisation_recipe.estimate_slopes.R @@ -6,18 +6,18 @@ #' # ============================================== #' if (require("ggplot2")) { #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) -#' x <- estimate_slopes(model, trend = "Petal.Length", at = "Species") +#' x <- estimate_slopes(model, trend = "Petal.Length", by ="Species") #' #' layers <- visualisation_recipe(x) #' layers #' plot(layers) #' #' model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) -#' x <- estimate_slopes(model, at = "Sepal.Width", length = 20) +#' x <- estimate_slopes(model, by ="Sepal.Width", length = 20) #' plot(visualisation_recipe(x)) #' #' model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris) -#' x <- estimate_slopes(model, at = c("Sepal.Width", "Species")) +#' x <- estimate_slopes(model, by =c("Sepal.Width", "Species")) #' plot(visualisation_recipe(x)) #' } #' \donttest{ @@ -27,11 +27,11 @@ #' data$Petal.Length <- data$Petal.Length^2 #' #' model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length), data = data) -#' x <- estimate_slopes(model, at = c("Petal.Width", "Petal.Length"), length = 20) +#' x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length"), length = 20) #' plot(visualisation_recipe(x)) #' #' model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length, by = Species), data = data) -#' x <- estimate_slopes(model, at = c("Petal.Width", "Petal.Length", "Species"), length = 10) +#' x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length", "Species"), length = 10) #' plot(visualisation_recipe(x)) #' } #' } @@ -80,19 +80,17 @@ visualisation_recipe.estimate_slopes <- function(x, if (length(facs) > 0) { facet <- facs[1] } + } else if (length(facs) > 0) { + color <- facs[1] + fill <- facs[1] + group_ribbon <- facs[1] + group_line <- facs[1] + alpha <- "Confidence" } else { - if (length(facs) > 0) { - color <- facs[1] - fill <- facs[1] - group_ribbon <- facs[1] - group_line <- facs[1] - alpha <- "Confidence" - } else { - group_ribbon <- ".group" - group_line <- 1 - fill <- "Confidence" - color <- NULL - } + group_ribbon <- ".group" + group_line <- 1 + fill <- "Confidence" + color <- NULL } } diff --git a/man/visualisation_matrix.Rd b/man/visualisation_matrix.Rd index dec6f37d..28c0850e 100644 --- a/man/visualisation_matrix.Rd +++ b/man/visualisation_matrix.Rd @@ -11,8 +11,9 @@ visualisation_matrix(x, ...) \method{visualisation_matrix}{data.frame}( x, - at = "all", + by = "all", target = NULL, + at = NULL, factors = "reference", numerics = "mean", preserve_range = FALSE, @@ -30,9 +31,57 @@ visualisation_matrix(x, ...) \item{...}{Arguments passed to or from other methods (for instance, \code{length} or \code{range} to control the spread of numeric variables.).} -\item{at}{Deprecated. Use \code{by} instead.} +\item{by}{Indicates the \emph{focal predictors} (variables) for the reference grid +and at which values focal predictors should be represented. If not specified +otherwise, representative values for numeric variables or predictors are +evenly distributed from the minimum to the maximum, with a total number of +\code{length} values covering that range (see 'Examples'). Possible options for +\code{by} are: +\itemize{ +\item \code{"all"}, which will include all variables or predictors. +\item a character vector of one or more variable or predictor names, like +\code{c("Species", "Sepal.Width")}, which will create a grid of all combinations +of unique values. For factors, will use all levels, for numeric variables, +will use a range of length \code{length} (evenly spread from minimum to maximum) +and for character vectors, will use all unique values. +\item a list of named elements, indicating focal predictors and their representative +values, e.g. \code{by = list(Sepal.Length = c(2, 4), Species = "setosa")}. +\item a string with assignments, e.g. \code{by = "Sepal.Length = 2"} or +\code{by = c("Sepal.Length = 2", "Species = 'setosa'")} - note the usage of single +and double quotes to assign strings within strings. +} + +There is a special handling of assignments with \emph{brackets}, i.e. values +defined inside \code{[} and \verb{]}.For \strong{numeric} variables, the value(s) inside +the brackets should either be +\itemize{ +\item two values, indicating minimum and maximum (e.g. \code{by = "Sepal.Length = [0, 5]"}), +for which a range of length \code{length} (evenly spread from given minimum to +maximum) is created. +\item more than two numeric values \code{by = "Sepal.Length = [2,3,4,5]"}, in which +case these values are used as representative values. +\item a "token" that creates pre-defined representative values: +\itemize{ +\item for mean and -/+ 1 SD around the mean: \code{"x = [sd]"} +\item for median and -/+ 1 MAD around the median: \code{"x = [mad]"} +\item for Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): \code{"x = [fivenum]"} +\item for terciles, including minimum and maximum: \code{"x = [terciles]"} +\item for terciles, excluding minimum and maximum: \code{"x = [terciles2]"} +\item for quartiles, including minimum and maximum: \code{"x = [quartiles]"} +\item for quartiles, excluding minimum and maximum: \code{"x = [quartiles2]"} +\item for minimum and maximum value: \code{"x = [minmax]"} +\item for 0 and the maximum value: \code{"x = [zeromax]"} +} +} + +For \strong{factor} variables, the value(s) inside the brackets should indicate +one or more factor levels, like \code{by = "Species = [setosa, versicolor]"}. +\strong{Note}: the \code{length} argument will be ignored when using brackets-tokens. + +The remaining variables not specified in \code{by} will be fixed (see also arguments +\code{factors} and \code{numerics}).} -\item{target}{Deprecated name. Please use \code{at} instead.} +\item{target, at}{Deprecated name. Please use \code{by} instead.} \item{factors}{Type of summary for factors. Can be \code{"reference"} (set at the reference level), \code{"mode"} (set at the most common level) or \code{"all"} to @@ -66,23 +115,23 @@ library(modelbased) data <- rbind(iris, iris[149, ], make.row.names = FALSE) # Single variable is of interest; all others are "fixed" -visualisation_matrix(data, at = "Sepal.Length") -visualisation_matrix(data, at = "Sepal.Length", length = 3) -visualisation_matrix(data, at = "Sepal.Length", range = "ci", ci = 0.90) -visualisation_matrix(data, at = "Sepal.Length", factors = "mode") +visualisation_matrix(data, by = "Sepal.Length") +visualisation_matrix(data, by = "Sepal.Length", length = 3) +visualisation_matrix(data, by = "Sepal.Length", range = "ci", ci = 0.90) +visualisation_matrix(data, by = "Sepal.Length", factors = "mode") # Multiple variables are of interest, creating a combination -visualisation_matrix(data, at = c("Sepal.Length", "Species"), length = 3) -visualisation_matrix(data, at = c(1, 3), length = 3) -visualisation_matrix(data, at = c("Sepal.Length", "Species"), preserve_range = TRUE) -visualisation_matrix(data, at = c("Sepal.Length", "Species"), numerics = 0) -visualisation_matrix(data, at = c("Sepal.Length = 3", "Species")) -visualisation_matrix(data, at = c("Sepal.Length = c(3, 1)", "Species = 'setosa'")) +visualisation_matrix(data, by = c("Sepal.Length", "Species"), length = 3) +visualisation_matrix(data, by = c(1, 3), length = 3) +visualisation_matrix(data, by = c("Sepal.Length", "Species"), preserve_range = TRUE) +visualisation_matrix(data, by = c("Sepal.Length", "Species"), numerics = 0) +visualisation_matrix(data, by = c("Sepal.Length = 3", "Species")) +visualisation_matrix(data, by = c("Sepal.Length = c(3, 1)", "Species = 'setosa'")) # with list-style at-argument -visualisation_matrix(data, at = list(Sepal.Length = c(1, 3), Species = "setosa")) +visualisation_matrix(data, by = list(Sepal.Length = c(1, 3), Species = "setosa")) # Standardize -vizdata <- visualisation_matrix(data, at = "Sepal.Length") +vizdata <- visualisation_matrix(data, by = "Sepal.Length") standardize(vizdata) } diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index 14190120..152784e8 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -126,14 +126,14 @@ data$cyl <- as.factor(data$cyl) data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) model <- lm(mpg ~ new_factor * cyl * wt, data = data) -x <- estimate_means(model, at = c("new_factor", "cyl")) +x <- estimate_means(model, by =c("new_factor", "cyl")) plot(visualisation_recipe(x)) # Modulations -------------- -x <- estimate_means(model, at = c("new_factor", "wt")) +x <- estimate_means(model, by =c("new_factor", "wt")) plot(visualisation_recipe(x)) -# x <- estimate_means(model, at = c("new_factor", "cyl", "wt")) +# x <- estimate_means(model, by =c("new_factor", "cyl", "wt")) # plot(visualisation_recipe(x)) # TODO: broken #' # GLMs --------------------- @@ -257,18 +257,18 @@ if (require("ggplot2") && require("rstanarm")) { # ============================================== if (require("ggplot2")) { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) - x <- estimate_slopes(model, trend = "Petal.Length", at = "Species") + x <- estimate_slopes(model, trend = "Petal.Length", by ="Species") layers <- visualisation_recipe(x) layers plot(layers) model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) - x <- estimate_slopes(model, at = "Sepal.Width", length = 20) + x <- estimate_slopes(model, by ="Sepal.Width", length = 20) plot(visualisation_recipe(x)) model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris) - x <- estimate_slopes(model, at = c("Sepal.Width", "Species")) + x <- estimate_slopes(model, by =c("Sepal.Width", "Species")) plot(visualisation_recipe(x)) } \donttest{ @@ -278,11 +278,11 @@ if (require("mgcv")) { data$Petal.Length <- data$Petal.Length^2 model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length), data = data) - x <- estimate_slopes(model, at = c("Petal.Width", "Petal.Length"), length = 20) + x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length"), length = 20) plot(visualisation_recipe(x)) model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length, by = Species), data = data) - x <- estimate_slopes(model, at = c("Petal.Width", "Petal.Length", "Species"), length = 10) + x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length", "Species"), length = 10) plot(visualisation_recipe(x)) } } From a8e79e500cc69bd51b40861199c0a7c97a398355 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 10:59:43 +0200 Subject: [PATCH 09/24] update vignette --- vignettes/visualisation_matrix.Rmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/vignettes/visualisation_matrix.Rmd b/vignettes/visualisation_matrix.Rmd index 435aa290..2a60db32 100644 --- a/vignettes/visualisation_matrix.Rmd +++ b/vignettes/visualisation_matrix.Rmd @@ -158,7 +158,7 @@ The above way of constructing the reference grid, i.e., by providing a single column of data to the function, is almost equivalent to the following: ```{r} -vizdata <- visualisation_matrix(iris, at = "Sepal.Length") +vizdata <- visualisation_matrix(iris, by = "Sepal.Length") vizdata ``` @@ -170,7 +170,7 @@ interested. By default, **factors** are fixed by their **"reference" level** and **numeric variables** are fixed at their **mean**. However, this can be easily changed: ```{r} -vizdata <- visualisation_matrix(iris, at = "Sepal.Length", numerics = "min") +vizdata <- visualisation_matrix(iris, by = "Sepal.Length", numerics = "min") vizdata ``` @@ -186,7 +186,7 @@ Let's visualise the regression line for each of the levels of `Species`: ```{r} model <- lm(Sepal.Width ~ Sepal.Length * Species, data = iris) -vizdata <- visualisation_matrix(iris, at = c("Sepal.Length", "Species"), length = 5) +vizdata <- visualisation_matrix(iris, by = c("Sepal.Length", "Species"), length = 5) vizdata$Predicted <- insight::get_predicted(model, vizdata) vizdata @@ -206,7 +206,7 @@ improve the precision toward the edges): ```{r} vizdata <- visualisation_matrix(iris, - at = c("Sepal.Length", "Species"), + by = c("Sepal.Length", "Species"), length = 100, preserve_range = TRUE ) @@ -330,7 +330,7 @@ Let's use the same model as above, and then obtain a data grid with specific val ```{r message=FALSE, warning=FALSE} vizdata <- lm(Sepal.Length ~ Petal.Length * Petal.Width, data = iris) %>% - visualisation_matrix(at = c("Petal.Length", "Petal.Width = seq(-3, 3)")) %>% + visualisation_matrix(by = c("Petal.Length", "Petal.Width = seq(-3, 3)")) %>% unstandardize(vizdata, select = "Petal.Width") %>% estimate_relation(vizdata) From 760fd2274c03093f8570891fd5bc959d353ffde8 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 11:13:16 +0200 Subject: [PATCH 10/24] docs --- R/get_emmeans.R | 3 ++- man/estimate_contrasts.Rd | 2 ++ man/estimate_means.Rd | 2 ++ man/estimate_slopes.Rd | 2 ++ man/get_emmeans.Rd | 4 +++- man/get_marginaleffects.Rd | 2 ++ 6 files changed, 13 insertions(+), 2 deletions(-) diff --git a/R/get_emmeans.R b/R/get_emmeans.R index 6bc17b97..018535e2 100644 --- a/R/get_emmeans.R +++ b/R/get_emmeans.R @@ -20,12 +20,13 @@ #' Thus for a logistic model, `"none"` will give estimations expressed in #' log-odds (probabilities on logit scale) and `"response"` in terms of #' probabilities. -#' @param levels,modulate,at Deprecated, use `by` instead. +#' @param levels,modulate Deprecated, use `by` instead. #' @param by The predictor variable(s) at which to evaluate the desired effect #' / mean / contrasts. Other predictors of the model that are not included #' here will be collapsed and "averaged" over (the effect will be estimated #' across them). #' @param ... Other arguments passed for instance to [insight::get_datagrid()]. +#' @param at Deprecated, use `by` instead. #' #' @examples #' model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris) diff --git a/man/estimate_contrasts.Rd b/man/estimate_contrasts.Rd index f2b64f47..790a74d5 100644 --- a/man/estimate_contrasts.Rd +++ b/man/estimate_contrasts.Rd @@ -54,6 +54,8 @@ section in the \code{emmeans::test} documentation.} \item{adjust}{Deprecated in favour of \code{p_adjust}.} +\item{at}{Deprecated, use \code{by} instead.} + \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} } \value{ diff --git a/man/estimate_means.Rd b/man/estimate_means.Rd index 64040e93..dbf797a9 100644 --- a/man/estimate_means.Rd +++ b/man/estimate_means.Rd @@ -42,6 +42,8 @@ probabilities.} \item{backend}{Whether to use 'emmeans' or 'marginaleffects' as a backend. The latter is experimental and some features might not work.} +\item{at}{Deprecated, use \code{by} instead.} + \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} } \value{ diff --git a/man/estimate_slopes.Rd b/man/estimate_slopes.Rd index 0cff75f8..a1e5d18c 100644 --- a/man/estimate_slopes.Rd +++ b/man/estimate_slopes.Rd @@ -19,6 +19,8 @@ across them).} \item{ci}{Confidence Interval (CI) level. Default to \code{0.95} (\verb{95\%}).} +\item{at}{Deprecated, use \code{by} instead.} + \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} } \value{ diff --git a/man/get_emmeans.Rd b/man/get_emmeans.Rd index b8a714fd..24dd2969 100644 --- a/man/get_emmeans.Rd +++ b/man/get_emmeans.Rd @@ -103,9 +103,11 @@ probabilities.} \item{method}{Contrast method. See same argument in \link[emmeans:contrast]{emmeans::contrast}.} +\item{at}{Deprecated, use \code{by} instead.} + \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} -\item{levels, modulate, at}{Deprecated, use \code{by} instead.} +\item{levels, modulate}{Deprecated, use \code{by} instead.} \item{trend}{A character indicating the name of the variable for which to compute the slopes.} diff --git a/man/get_marginaleffects.Rd b/man/get_marginaleffects.Rd index b344146e..8f83facf 100644 --- a/man/get_marginaleffects.Rd +++ b/man/get_marginaleffects.Rd @@ -27,6 +27,8 @@ across them).} \item{fixed}{A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.} +\item{at}{Deprecated, use \code{by} instead.} + \item{...}{Other arguments passed for instance to \code{\link[insight:get_datagrid]{insight::get_datagrid()}}.} } \description{ From 11789348b34df3f467599fffaa6bf1492a05f169 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 11:15:20 +0200 Subject: [PATCH 11/24] styler --- R/visualisation_recipe.estimate_means.R | 4 ++-- R/visualisation_recipe.estimate_slopes.R | 10 +++++----- man/visualisation_recipe.estimate_predicted.Rd | 14 +++++++------- tests/testthat/test-estimate_predicted.R | 3 +-- 4 files changed, 15 insertions(+), 16 deletions(-) diff --git a/R/visualisation_recipe.estimate_means.R b/R/visualisation_recipe.estimate_means.R index c062f32f..9a99ba43 100644 --- a/R/visualisation_recipe.estimate_means.R +++ b/R/visualisation_recipe.estimate_means.R @@ -23,11 +23,11 @@ #' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) #' #' model <- lm(mpg ~ new_factor * cyl * wt, data = data) -#' x <- estimate_means(model, by =c("new_factor", "cyl")) +#' x <- estimate_means(model, by = c("new_factor", "cyl")) #' plot(visualisation_recipe(x)) #' #' # Modulations -------------- -#' x <- estimate_means(model, by =c("new_factor", "wt")) +#' x <- estimate_means(model, by = c("new_factor", "wt")) #' plot(visualisation_recipe(x)) #' #' # x <- estimate_means(model, by =c("new_factor", "cyl", "wt")) diff --git a/R/visualisation_recipe.estimate_slopes.R b/R/visualisation_recipe.estimate_slopes.R index 0f03bc61..f801a526 100644 --- a/R/visualisation_recipe.estimate_slopes.R +++ b/R/visualisation_recipe.estimate_slopes.R @@ -6,18 +6,18 @@ #' # ============================================== #' if (require("ggplot2")) { #' model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) -#' x <- estimate_slopes(model, trend = "Petal.Length", by ="Species") +#' x <- estimate_slopes(model, trend = "Petal.Length", by = "Species") #' #' layers <- visualisation_recipe(x) #' layers #' plot(layers) #' #' model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) -#' x <- estimate_slopes(model, by ="Sepal.Width", length = 20) +#' x <- estimate_slopes(model, by = "Sepal.Width", length = 20) #' plot(visualisation_recipe(x)) #' #' model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris) -#' x <- estimate_slopes(model, by =c("Sepal.Width", "Species")) +#' x <- estimate_slopes(model, by = c("Sepal.Width", "Species")) #' plot(visualisation_recipe(x)) #' } #' \donttest{ @@ -27,11 +27,11 @@ #' data$Petal.Length <- data$Petal.Length^2 #' #' model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length), data = data) -#' x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length"), length = 20) +#' x <- estimate_slopes(model, by = c("Petal.Width", "Petal.Length"), length = 20) #' plot(visualisation_recipe(x)) #' #' model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length, by = Species), data = data) -#' x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length", "Species"), length = 10) +#' x <- estimate_slopes(model, by = c("Petal.Width", "Petal.Length", "Species"), length = 10) #' plot(visualisation_recipe(x)) #' } #' } diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index 152784e8..ed153808 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -126,11 +126,11 @@ data$cyl <- as.factor(data$cyl) data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) model <- lm(mpg ~ new_factor * cyl * wt, data = data) -x <- estimate_means(model, by =c("new_factor", "cyl")) +x <- estimate_means(model, by = c("new_factor", "cyl")) plot(visualisation_recipe(x)) # Modulations -------------- -x <- estimate_means(model, by =c("new_factor", "wt")) +x <- estimate_means(model, by = c("new_factor", "wt")) plot(visualisation_recipe(x)) # x <- estimate_means(model, by =c("new_factor", "cyl", "wt")) @@ -257,18 +257,18 @@ if (require("ggplot2") && require("rstanarm")) { # ============================================== if (require("ggplot2")) { model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris) - x <- estimate_slopes(model, trend = "Petal.Length", by ="Species") + x <- estimate_slopes(model, trend = "Petal.Length", by = "Species") layers <- visualisation_recipe(x) layers plot(layers) model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris) - x <- estimate_slopes(model, by ="Sepal.Width", length = 20) + x <- estimate_slopes(model, by = "Sepal.Width", length = 20) plot(visualisation_recipe(x)) model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris) - x <- estimate_slopes(model, by =c("Sepal.Width", "Species")) + x <- estimate_slopes(model, by = c("Sepal.Width", "Species")) plot(visualisation_recipe(x)) } \donttest{ @@ -278,11 +278,11 @@ if (require("mgcv")) { data$Petal.Length <- data$Petal.Length^2 model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length), data = data) - x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length"), length = 20) + x <- estimate_slopes(model, by = c("Petal.Width", "Petal.Length"), length = 20) plot(visualisation_recipe(x)) model <- mgcv::gam(Sepal.Width ~ t2(Petal.Width, Petal.Length, by = Species), data = data) - x <- estimate_slopes(model, by =c("Petal.Width", "Petal.Length", "Species"), length = 10) + x <- estimate_slopes(model, by = c("Petal.Width", "Petal.Length", "Species"), length = 10) plot(visualisation_recipe(x)) } } diff --git a/tests/testthat/test-estimate_predicted.R b/tests/testthat/test-estimate_predicted.R index 35089622..1617520c 100644 --- a/tests/testthat/test-estimate_predicted.R +++ b/tests/testthat/test-estimate_predicted.R @@ -169,6 +169,5 @@ test_that("estimate_response - VisMatrix", { vm <- visualisation_matrix(m, by = c("Petal.Length", "Petal.Width = seq(-3, 3)")) estim <- estimate_relation(vm) expect_identical(dim(estim), c(70L, 6L)) - expect_named(estim, c("Petal.Length", "Petal.Width", "Predicted", "SE", "CI_low", "CI_high") - ) + expect_named(estim, c("Petal.Length", "Petal.Width", "Predicted", "SE", "CI_low", "CI_high")) }) From 568a5bb7453e12839ebb6fd34bc98ab6d04d6e5a Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 11:24:08 +0200 Subject: [PATCH 12/24] conditional examples --- R/visualisation_recipe.estimate_grouplevel.R | 40 ++-- R/visualisation_recipe.estimate_means.R | 2 +- R/visualisation_recipe.estimate_predicted.R | 149 +++++++------ R/visualisation_recipe.estimate_slopes.R | 2 +- ...visualisation_recipe.estimate_predicted.Rd | 199 +++++++++--------- 5 files changed, 192 insertions(+), 200 deletions(-) diff --git a/R/visualisation_recipe.estimate_grouplevel.R b/R/visualisation_recipe.estimate_grouplevel.R index b1f72acb..b827e076 100644 --- a/R/visualisation_recipe.estimate_grouplevel.R +++ b/R/visualisation_recipe.estimate_grouplevel.R @@ -1,33 +1,29 @@ #' @rdname visualisation_recipe.estimate_predicted #' -#' @examples +#' @examplesIf require("see") && require("lme4") && require("emmeans") #' # ============================================== #' # estimate_grouplevel #' # ============================================== -#' if (require("see") && require("lme4")) { -#' data <- lme4::sleepstudy -#' data <- rbind(data, data) -#' data$Newfactor <- rep(c("A", "B", "C", "D")) +#' data <- lme4::sleepstudy +#' data <- rbind(data, data) +#' data$Newfactor <- rep(c("A", "B", "C", "D")) #' -#' # 1 random intercept -#' model <- lmer(Reaction ~ Days + (1 | Subject), data = data) -#' x <- estimate_grouplevel(model) -#' layers <- visualisation_recipe(x) -#' layers -#' plot(layers) -#' } -#' \donttest{ -#' if (require("see") && require("lme4")) { -#' # 2 random intercepts -#' model <- lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data) -#' x <- estimate_grouplevel(model) -#' plot(visualisation_recipe(x)) +#' # 1 random intercept +#' model <- lme4::lmer(Reaction ~ Days + (1 | Subject), data = data) +#' x <- estimate_grouplevel(model) +#' layers <- visualisation_recipe(x) +#' layers +#' plot(layers) #' +#' \donttest{ +#' # 2 random intercepts +#' model <- lme4::lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data) +#' x <- estimate_grouplevel(model) +#' plot(visualisation_recipe(x)) #' -#' model <- lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data) -#' x <- estimate_grouplevel(model) -#' plot(visualisation_recipe(x)) -#' } +#' model <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data) +#' x <- estimate_grouplevel(model) +#' plot(visualisation_recipe(x)) #' } #' @export visualisation_recipe.estimate_grouplevel <- function(x, diff --git a/R/visualisation_recipe.estimate_means.R b/R/visualisation_recipe.estimate_means.R index 9a99ba43..a85c0325 100644 --- a/R/visualisation_recipe.estimate_means.R +++ b/R/visualisation_recipe.estimate_means.R @@ -1,6 +1,6 @@ #' @rdname visualisation_recipe.estimate_predicted #' -#' @examplesIf require("ggplot2", quietly = TRUE) && require("emmeans", quietly = TRUE) +#' @examplesIf require("ggplot2") && require("emmeans") && require("see") #' # Simple Model --------------- #' x <- estimate_means(lm(Sepal.Width ~ Species, data = iris)) #' layers <- visualisation_recipe(x) diff --git a/R/visualisation_recipe.estimate_predicted.R b/R/visualisation_recipe.estimate_predicted.R index fa1cc3d6..e7617fe5 100644 --- a/R/visualisation_recipe.estimate_predicted.R +++ b/R/visualisation_recipe.estimate_predicted.R @@ -14,103 +14,100 @@ #' aesthetics and parameters for the geoms (see customization example). #' @param ... Other arguments passed to other functions. #' -#' @examples +#' @examplesIf require("ggplot2") && require("emmeans") && require("see") #' # ============================================== #' # estimate_relation, estimate_response, ... #' # ============================================== -#' if (require("ggplot2")) { -#' # Simple Model --------------- -#' x <- estimate_relation(lm(mpg ~ wt, data = mtcars)) -#' layers <- visualisation_recipe(x) -#' layers -#' plot(layers) -#' } +#' # Simple Model --------------- +#' x <- estimate_relation(lm(mpg ~ wt, data = mtcars)) +#' layers <- visualisation_recipe(x) +#' layers +#' plot(layers) +#' #' \donttest{ -#' if (require("ggplot2")) { -#' # Customize aesthetics ---------- +#' # Customize aesthetics ---------- #' -#' layers <- visualisation_recipe(x, -#' point = list(color = "red", alpha = 0.6, size = 3), -#' line = list(color = "blue", size = 3), -#' ribbon = list(fill = "green", alpha = 0.7), -#' labs = list(subtitle = "Oh yeah!") -#' ) -#' layers -#' plot(layers) +#' layers <- visualisation_recipe(x, +#' point = list(color = "red", alpha = 0.6, size = 3), +#' line = list(color = "blue", size = 3), +#' ribbon = list(fill = "green", alpha = 0.7), +#' labs = list(subtitle = "Oh yeah!") +#' ) +#' layers +#' plot(layers) #' -#' # Customize raw data ------------- +#' # Customize raw data ------------- #' -#' plot(visualisation_recipe(x, show_data = "none")) -#' plot(visualisation_recipe(x, show_data = c("density_2d", "points"))) -#' plot(visualisation_recipe(x, show_data = "density_2d_filled")) -#' plot(visualisation_recipe(x, show_data = "density_2d_polygon")) -#' plot(visualisation_recipe(x, show_data = "density_2d_raster")) + -#' scale_x_continuous(expand = c(0, 0)) + -#' scale_y_continuous(expand = c(0, 0)) +#' plot(visualisation_recipe(x, show_data = "none")) +#' plot(visualisation_recipe(x, show_data = c("density_2d", "points"))) +#' plot(visualisation_recipe(x, show_data = "density_2d_filled")) +#' plot(visualisation_recipe(x, show_data = "density_2d_polygon")) +#' plot(visualisation_recipe(x, show_data = "density_2d_raster")) + +#' scale_x_continuous(expand = c(0, 0)) + +#' scale_y_continuous(expand = c(0, 0)) #' -#' # Single predictors examples ----------- +#' # Single predictors examples ----------- #' -#' plot(estimate_relation(lm(Sepal.Length ~ Sepal.Width, data = iris))) -#' plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris))) +#' plot(estimate_relation(lm(Sepal.Length ~ Sepal.Width, data = iris))) +#' plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris))) #' -#' # 2-ways interaction ------------ +#' # 2-ways interaction ------------ #' -#' # Numeric * numeric -#' x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Numeric * numeric +#' x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # Numeric * factor -#' x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Numeric * factor +#' x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # Factor * numeric -#' x <- estimate_relation(lm(Sepal.Width ~ Species * Sepal.Length, data = iris)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Factor * numeric +#' x <- estimate_relation(lm(Sepal.Width ~ Species * Sepal.Length, data = iris)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # 3-ways interaction ------------ +#' # 3-ways interaction ------------ #' -#' data <- mtcars -#' data$vs <- as.factor(data$vs) -#' data$cyl <- as.factor(data$cyl) -#' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) +#' data <- mtcars +#' data$vs <- as.factor(data$vs) +#' data$cyl <- as.factor(data$cyl) +#' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) #' -#' # Numeric * numeric * numeric -#' x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Numeric * numeric * numeric +#' x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # Numeric * numeric * factor -#' x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Numeric * numeric * factor +#' x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # Numeric * factor * factor -#' x <- estimate_relation(lm(mpg ~ wt * cyl * new_factor, data = data)) -#' layers <- visualisation_recipe(x) -#' plot(layers) +#' # Numeric * factor * factor +#' x <- estimate_relation(lm(mpg ~ wt * cyl * new_factor, data = data)) +#' layers <- visualisation_recipe(x) +#' plot(layers) #' -#' # Factor * numeric * numeric -#' x <- estimate_relation(lm(mpg ~ cyl * qsec * hp, data = data)) -#' layers <- visualisation_recipe(x) -#' plot(layers) + -#' scale_size_continuous(range = c(0.2, 1)) +#' # Factor * numeric * numeric +#' x <- estimate_relation(lm(mpg ~ cyl * qsec * hp, data = data)) +#' layers <- visualisation_recipe(x) +#' plot(layers) + +#' scale_size_continuous(range = c(0.2, 1)) #' -#' # GLMs --------------------- -#' x <- estimate_relation(glm(vs ~ mpg, data = mtcars, family = "binomial")) -#' plot(visualisation_recipe(x)) -#' plot(visualisation_recipe(x, show_data = "jitter", point = list(height = 0.03))) +#' # GLMs --------------------- +#' x <- estimate_relation(glm(vs ~ mpg, data = mtcars, family = "binomial")) +#' plot(visualisation_recipe(x)) +#' plot(visualisation_recipe(x, show_data = "jitter", point = list(height = 0.03))) #' -#' # Multiple CIs --------------------- -#' plot(estimate_relation(lm(mpg ~ disp, data = mtcars), -#' ci = c(.50, .80, .95) -#' )) -#' plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris), -#' ci = c(0.5, 0.7, 0.95) -#' )) -#' } +#' # Multiple CIs --------------------- +#' plot(estimate_relation(lm(mpg ~ disp, data = mtcars), +#' ci = c(.50, .80, .95) +#' )) +#' plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris), +#' ci = c(0.5, 0.7, 0.95) +#' )) #' #' # Bayesian models --------------------- #' if (require("ggplot2") && require("rstanarm")) { diff --git a/R/visualisation_recipe.estimate_slopes.R b/R/visualisation_recipe.estimate_slopes.R index f801a526..90a0f0b9 100644 --- a/R/visualisation_recipe.estimate_slopes.R +++ b/R/visualisation_recipe.estimate_slopes.R @@ -1,6 +1,6 @@ #' @rdname visualisation_recipe.estimate_predicted #' -#' @examples +#' @examplesIf require("ggplot2") && require("emmeans") && require("see") #' # ============================================== #' # estimate_slopes #' # ============================================== diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index ed153808..aa64d71a 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -75,35 +75,33 @@ aesthetics and parameters for the geoms (see customization example).} Visualisation Recipe for 'modelbased' Objects } \examples{ +\dontshow{if (require("see") && require("lme4") && require("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} # ============================================== # estimate_grouplevel # ============================================== -if (require("see") && require("lme4")) { - data <- lme4::sleepstudy - data <- rbind(data, data) - data$Newfactor <- rep(c("A", "B", "C", "D")) - - # 1 random intercept - model <- lmer(Reaction ~ Days + (1 | Subject), data = data) - x <- estimate_grouplevel(model) - layers <- visualisation_recipe(x) - layers - plot(layers) -} -\donttest{ -if (require("see") && require("lme4")) { - # 2 random intercepts - model <- lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data) - x <- estimate_grouplevel(model) - plot(visualisation_recipe(x)) +data <- lme4::sleepstudy +data <- rbind(data, data) +data$Newfactor <- rep(c("A", "B", "C", "D")) + +# 1 random intercept +model <- lme4::lmer(Reaction ~ Days + (1 | Subject), data = data) +x <- estimate_grouplevel(model) +layers <- visualisation_recipe(x) +layers +plot(layers) +\donttest{ +# 2 random intercepts +model <- lme4::lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data) +x <- estimate_grouplevel(model) +plot(visualisation_recipe(x)) - model <- lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data) - x <- estimate_grouplevel(model) - plot(visualisation_recipe(x)) -} +model <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data) +x <- estimate_grouplevel(model) +plot(visualisation_recipe(x)) } -\dontshow{if (require("ggplot2", quietly = TRUE) && require("emmeans", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\dontshow{\}) # examplesIf} +\dontshow{if (require("ggplot2") && require("emmeans") && require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} # Simple Model --------------- x <- estimate_means(lm(Sepal.Width ~ Species, data = iris)) layers <- visualisation_recipe(x) @@ -142,102 +140,100 @@ x <- estimate_means(glm(vs ~ cyl, data = data, family = "binomial")) plot(visualisation_recipe(x)) } \dontshow{\}) # examplesIf} +\dontshow{if (require("ggplot2") && require("emmeans") && require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} # ============================================== # estimate_relation, estimate_response, ... # ============================================== -if (require("ggplot2")) { - # Simple Model --------------- - x <- estimate_relation(lm(mpg ~ wt, data = mtcars)) - layers <- visualisation_recipe(x) - layers - plot(layers) -} +# Simple Model --------------- +x <- estimate_relation(lm(mpg ~ wt, data = mtcars)) +layers <- visualisation_recipe(x) +layers +plot(layers) + \donttest{ -if (require("ggplot2")) { - # Customize aesthetics ---------- - - layers <- visualisation_recipe(x, - point = list(color = "red", alpha = 0.6, size = 3), - line = list(color = "blue", size = 3), - ribbon = list(fill = "green", alpha = 0.7), - labs = list(subtitle = "Oh yeah!") - ) - layers - plot(layers) +# Customize aesthetics ---------- - # Customize raw data ------------- +layers <- visualisation_recipe(x, + point = list(color = "red", alpha = 0.6, size = 3), + line = list(color = "blue", size = 3), + ribbon = list(fill = "green", alpha = 0.7), + labs = list(subtitle = "Oh yeah!") +) +layers +plot(layers) - plot(visualisation_recipe(x, show_data = "none")) - plot(visualisation_recipe(x, show_data = c("density_2d", "points"))) - plot(visualisation_recipe(x, show_data = "density_2d_filled")) - plot(visualisation_recipe(x, show_data = "density_2d_polygon")) - plot(visualisation_recipe(x, show_data = "density_2d_raster")) + - scale_x_continuous(expand = c(0, 0)) + - scale_y_continuous(expand = c(0, 0)) +# Customize raw data ------------- - # Single predictors examples ----------- +plot(visualisation_recipe(x, show_data = "none")) +plot(visualisation_recipe(x, show_data = c("density_2d", "points"))) +plot(visualisation_recipe(x, show_data = "density_2d_filled")) +plot(visualisation_recipe(x, show_data = "density_2d_polygon")) +plot(visualisation_recipe(x, show_data = "density_2d_raster")) + + scale_x_continuous(expand = c(0, 0)) + + scale_y_continuous(expand = c(0, 0)) - plot(estimate_relation(lm(Sepal.Length ~ Sepal.Width, data = iris))) - plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris))) +# Single predictors examples ----------- - # 2-ways interaction ------------ +plot(estimate_relation(lm(Sepal.Length ~ Sepal.Width, data = iris))) +plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris))) - # Numeric * numeric - x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars)) - layers <- visualisation_recipe(x) - plot(layers) +# 2-ways interaction ------------ - # Numeric * factor - x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris)) - layers <- visualisation_recipe(x) - plot(layers) +# Numeric * numeric +x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars)) +layers <- visualisation_recipe(x) +plot(layers) - # Factor * numeric - x <- estimate_relation(lm(Sepal.Width ~ Species * Sepal.Length, data = iris)) - layers <- visualisation_recipe(x) - plot(layers) +# Numeric * factor +x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris)) +layers <- visualisation_recipe(x) +plot(layers) - # 3-ways interaction ------------ +# Factor * numeric +x <- estimate_relation(lm(Sepal.Width ~ Species * Sepal.Length, data = iris)) +layers <- visualisation_recipe(x) +plot(layers) - data <- mtcars - data$vs <- as.factor(data$vs) - data$cyl <- as.factor(data$cyl) - data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) +# 3-ways interaction ------------ - # Numeric * numeric * numeric - x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) - layers <- visualisation_recipe(x) - plot(layers) +data <- mtcars +data$vs <- as.factor(data$vs) +data$cyl <- as.factor(data$cyl) +data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) - # Numeric * numeric * factor - x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) - layers <- visualisation_recipe(x) - plot(layers) +# Numeric * numeric * numeric +x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) +layers <- visualisation_recipe(x) +plot(layers) - # Numeric * factor * factor - x <- estimate_relation(lm(mpg ~ wt * cyl * new_factor, data = data)) - layers <- visualisation_recipe(x) - plot(layers) +# Numeric * numeric * factor +x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) +layers <- visualisation_recipe(x) +plot(layers) - # Factor * numeric * numeric - x <- estimate_relation(lm(mpg ~ cyl * qsec * hp, data = data)) - layers <- visualisation_recipe(x) - plot(layers) + - scale_size_continuous(range = c(0.2, 1)) +# Numeric * factor * factor +x <- estimate_relation(lm(mpg ~ wt * cyl * new_factor, data = data)) +layers <- visualisation_recipe(x) +plot(layers) - # GLMs --------------------- - x <- estimate_relation(glm(vs ~ mpg, data = mtcars, family = "binomial")) - plot(visualisation_recipe(x)) - plot(visualisation_recipe(x, show_data = "jitter", point = list(height = 0.03))) - - # Multiple CIs --------------------- - plot(estimate_relation(lm(mpg ~ disp, data = mtcars), - ci = c(.50, .80, .95) - )) - plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris), - ci = c(0.5, 0.7, 0.95) - )) -} +# Factor * numeric * numeric +x <- estimate_relation(lm(mpg ~ cyl * qsec * hp, data = data)) +layers <- visualisation_recipe(x) +plot(layers) + + scale_size_continuous(range = c(0.2, 1)) + +# GLMs --------------------- +x <- estimate_relation(glm(vs ~ mpg, data = mtcars, family = "binomial")) +plot(visualisation_recipe(x)) +plot(visualisation_recipe(x, show_data = "jitter", point = list(height = 0.03))) + +# Multiple CIs --------------------- +plot(estimate_relation(lm(mpg ~ disp, data = mtcars), + ci = c(.50, .80, .95) +)) +plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris), + ci = c(0.5, 0.7, 0.95) +)) # Bayesian models --------------------- if (require("ggplot2") && require("rstanarm")) { @@ -252,6 +248,8 @@ if (require("ggplot2") && require("rstanarm")) { plot(estimate_relation(model, keep_iterations = 100)) } } +\dontshow{\}) # examplesIf} +\dontshow{if (require("ggplot2") && require("emmeans") && require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} # ============================================== # estimate_slopes # ============================================== @@ -286,4 +284,5 @@ if (require("mgcv")) { plot(visualisation_recipe(x)) } } +\dontshow{\}) # examplesIf} } From 9233f1baa71188f52899c93f7151a4452fd37b34 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 14:52:06 +0200 Subject: [PATCH 13/24] remove non-working example --- R/visualisation_recipe.estimate_means.R | 4 ---- R/visualisation_recipe.estimate_predicted.R | 5 ----- man/visualisation_recipe.estimate_predicted.Rd | 9 --------- 3 files changed, 18 deletions(-) diff --git a/R/visualisation_recipe.estimate_means.R b/R/visualisation_recipe.estimate_means.R index a85c0325..fb37734d 100644 --- a/R/visualisation_recipe.estimate_means.R +++ b/R/visualisation_recipe.estimate_means.R @@ -22,10 +22,6 @@ #' data$cyl <- as.factor(data$cyl) #' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) #' -#' model <- lm(mpg ~ new_factor * cyl * wt, data = data) -#' x <- estimate_means(model, by = c("new_factor", "cyl")) -#' plot(visualisation_recipe(x)) -#' #' # Modulations -------------- #' x <- estimate_means(model, by = c("new_factor", "wt")) #' plot(visualisation_recipe(x)) diff --git a/R/visualisation_recipe.estimate_predicted.R b/R/visualisation_recipe.estimate_predicted.R index e7617fe5..45d344f7 100644 --- a/R/visualisation_recipe.estimate_predicted.R +++ b/R/visualisation_recipe.estimate_predicted.R @@ -75,11 +75,6 @@ #' data$cyl <- as.factor(data$cyl) #' data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) #' -#' # Numeric * numeric * numeric -#' x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) -#' layers <- visualisation_recipe(x) -#' plot(layers) -#' #' # Numeric * numeric * factor #' x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) #' layers <- visualisation_recipe(x) diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index aa64d71a..c9ce6596 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -123,10 +123,6 @@ data <- mtcars data$cyl <- as.factor(data$cyl) data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) -model <- lm(mpg ~ new_factor * cyl * wt, data = data) -x <- estimate_means(model, by = c("new_factor", "cyl")) -plot(visualisation_recipe(x)) - # Modulations -------------- x <- estimate_means(model, by = c("new_factor", "wt")) plot(visualisation_recipe(x)) @@ -201,11 +197,6 @@ data$vs <- as.factor(data$vs) data$cyl <- as.factor(data$cyl) data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars))) -# Numeric * numeric * numeric -x <- estimate_relation(lm(mpg ~ wt * qsec * hp, data = data), length = c(5, 3, 20)) -layers <- visualisation_recipe(x) -plot(layers) - # Numeric * numeric * factor x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data)) layers <- visualisation_recipe(x) From 9cfd7b58d69bef1e5966ef2981f87f1a3f461492 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 15:17:54 +0200 Subject: [PATCH 14/24] fix --- R/visualisation_recipe.estimate_means.R | 2 +- R/visualisation_recipe.estimate_predicted.R | 2 +- man/visualisation_recipe.estimate_predicted.Rd | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/visualisation_recipe.estimate_means.R b/R/visualisation_recipe.estimate_means.R index fb37734d..7e8732c1 100644 --- a/R/visualisation_recipe.estimate_means.R +++ b/R/visualisation_recipe.estimate_means.R @@ -6,7 +6,7 @@ #' layers <- visualisation_recipe(x) #' layers #' plot(layers) -#' \donttest{ +#' \dontrun{ #' # Customize aesthetics #' layers <- visualisation_recipe(x, #' jitter = list(width = 0.03, color = "red"), diff --git a/R/visualisation_recipe.estimate_predicted.R b/R/visualisation_recipe.estimate_predicted.R index 45d344f7..051a6612 100644 --- a/R/visualisation_recipe.estimate_predicted.R +++ b/R/visualisation_recipe.estimate_predicted.R @@ -24,7 +24,7 @@ #' layers #' plot(layers) #' -#' \donttest{ +#' \dontun{ #' # Customize aesthetics ---------- #' #' layers <- visualisation_recipe(x, diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index c9ce6596..8a2b912d 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -107,7 +107,7 @@ x <- estimate_means(lm(Sepal.Width ~ Species, data = iris)) layers <- visualisation_recipe(x) layers plot(layers) -\donttest{ +\dontrun{ # Customize aesthetics layers <- visualisation_recipe(x, jitter = list(width = 0.03, color = "red"), @@ -146,7 +146,7 @@ layers <- visualisation_recipe(x) layers plot(layers) -\donttest{ +\dontun{ # Customize aesthetics ---------- layers <- visualisation_recipe(x, From 4e590a8fecee14d0b6e2f564917e8a78a663cf3c Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 15:42:22 +0200 Subject: [PATCH 15/24] typo --- R/visualisation_recipe.estimate_predicted.R | 2 +- man/visualisation_recipe.estimate_predicted.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/visualisation_recipe.estimate_predicted.R b/R/visualisation_recipe.estimate_predicted.R index 051a6612..e8f80561 100644 --- a/R/visualisation_recipe.estimate_predicted.R +++ b/R/visualisation_recipe.estimate_predicted.R @@ -24,7 +24,7 @@ #' layers #' plot(layers) #' -#' \dontun{ +#' \dontrun{ #' # Customize aesthetics ---------- #' #' layers <- visualisation_recipe(x, diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd index 8a2b912d..079087fd 100644 --- a/man/visualisation_recipe.estimate_predicted.Rd +++ b/man/visualisation_recipe.estimate_predicted.Rd @@ -146,7 +146,7 @@ layers <- visualisation_recipe(x) layers plot(layers) -\dontun{ +\dontrun{ # Customize aesthetics ---------- layers <- visualisation_recipe(x, From 7bc698a97d18bab62369b950781c0438c0241833 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 17:54:16 +0200 Subject: [PATCH 16/24] fix --- vignettes/modelisation_approach.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/modelisation_approach.Rmd b/vignettes/modelisation_approach.Rmd index 869390a3..cc8d3567 100644 --- a/vignettes/modelisation_approach.Rmd +++ b/vignettes/modelisation_approach.Rmd @@ -7,7 +7,7 @@ output: fig_height: 6 tags: [r, estimate, estimate link, predictions] vignette: > - %\VignetteIndexEntry{The modelisation approach} + %\VignetteIndexEntry{The Modelisation Approach} \usepackage[utf8]{inputenc} %\VignetteEngine{knitr::rmarkdown} editor_options: @@ -81,7 +81,6 @@ statistical modelisation. ```{r} library(poorman) -library(ggplot2) library(emmeans) library(parameters) library(modelbased) @@ -166,6 +165,7 @@ parameters(posthoc) ``` ```{r} +library(ggplot2) data_anova %>% ggplot(aes(x = Category, y = RT, fill = Condition)) + geom_boxplot() + From 551a32561eb9d85abb4b51daa9577e6c98910f39 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 21:31:07 +0200 Subject: [PATCH 17/24] use datawizard --- vignettes/modelisation_approach.Rmd | 33 ++++++++++++++++------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/vignettes/modelisation_approach.Rmd b/vignettes/modelisation_approach.Rmd index cc8d3567..de682afa 100644 --- a/vignettes/modelisation_approach.Rmd +++ b/vignettes/modelisation_approach.Rmd @@ -30,7 +30,7 @@ if (!requireNamespace("ggplot2", quietly = TRUE) || !requireNamespace("see", quietly = TRUE) || !requireNamespace("gganimate", quietly = TRUE) || !requireNamespace("rstanarm", quietly = TRUE) || - !requireNamespace("poorman", quietly = TRUE)) { + !requireNamespace("datawizard", quietly = TRUE)) { knitr::opts_chunk$set(eval = FALSE) } @@ -80,10 +80,11 @@ statistical modelisation. # The Empirical Approach (Classic) ```{r} -library(poorman) +library(datawizard) library(emmeans) library(parameters) library(modelbased) +library(ggplot2) ``` ## Data Simulation @@ -140,15 +141,18 @@ So we will take, for each participant, its 20... ```{r} -data_anova <- data %>% - mutate(Category = case_when( - Experimental_Variable < -1.5 ~ "Low", - Experimental_Variable > 1.5 ~ "High", - TRUE ~ "Middle" - )) %>% - mutate(Category = relevel(as.factor(Category), "Low", "Middle", "High")) %>% - group_by(Participant, Condition, Category) %>% - summarise(RT = mean(RT)) +data_anova <- data +data_anova$Category <- recode_into( + Experimental_Variable < -1.5 ~ "Low", + Experimental_Variable > 1.5 ~ "High", + default = "Middle", + data = data_anova +) +data_anova$Category <- factor(data_anova$Category, levels = c("Low", "Middle", "High")) + +data_anova <- data_anova |> + data_group(c("Participant", "Condition", "Category")) |> + data_summary(RT = mean(RT)) results <- aov(RT ~ Condition * Category + Error(Participant), data = data_anova) parameters(results) @@ -159,14 +163,13 @@ What can we conclude from that? Absolutely **nothing**! We need to investigate i ## Post-hoc comparison tests ```{r} -posthoc <- model_emmeans(results, by = c("Condition", "Category")) %>% +posthoc <- model_emmeans(results, by = c("Condition", "Category")) |> pairs() parameters(posthoc) ``` ```{r} -library(ggplot2) -data_anova %>% +data_anova |> ggplot(aes(x = Category, y = RT, fill = Condition)) + geom_boxplot() + see::theme_modern() @@ -186,7 +189,7 @@ We can use `geom_smooth()`, which can fit non-linear relationships in an empiric ```{r} data %>% - group_by(Participant, Condition) %>% + group_by(Participant, Condition) |> ggplot(aes(x = Experimental_Variable, y = RT, color = Condition)) + geom_jitter(alpha = 0.4) + geom_smooth(method = "loess", se = FALSE) + From a8b05b6130578310cab1ef8913da2830c68aa37e Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 23 May 2024 21:32:44 +0200 Subject: [PATCH 18/24] fix --- README.Rmd | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.Rmd b/README.Rmd index f31d029b..73662eed 100644 --- a/README.Rmd +++ b/README.Rmd @@ -108,7 +108,7 @@ library(modelbased) model <- lm(Sepal.Length ~ Petal.Length * Petal.Width, data = iris) # 2. Create a visualisation matrix with expected Z-score values of Petal.Width -vizdata <- modelbased::visualisation_matrix(model, at = c("Petal.Length", "Petal.Width = c(-1, 0, 1)")) +vizdata <- modelbased::visualisation_matrix(model, by = c("Petal.Length", "Petal.Width = c(-1, 0, 1)")) # 3. Revert from expected SD to actual values vizdata <- unstandardize(vizdata, select = "Petal.Width") diff --git a/README.md b/README.md index 1ce9cc8b..38369479 100644 --- a/README.md +++ b/README.md @@ -110,7 +110,7 @@ library(modelbased) model <- lm(Sepal.Length ~ Petal.Length * Petal.Width, data = iris) # 2. Create a visualisation matrix with expected Z-score values of Petal.Width -vizdata <- modelbased::visualisation_matrix(model, at = c("Petal.Length", "Petal.Width = c(-1, 0, 1)")) +vizdata <- modelbased::visualisation_matrix(model, by = c("Petal.Length", "Petal.Width = c(-1, 0, 1)")) # 3. Revert from expected SD to actual values vizdata <- unstandardize(vizdata, select = "Petal.Width") From 8761ec7351ae30c2059e44be81d036c4874e64de Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 00:07:39 +0200 Subject: [PATCH 19/24] fix --- vignettes/modelisation_approach.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/modelisation_approach.Rmd b/vignettes/modelisation_approach.Rmd index de682afa..d4fe404e 100644 --- a/vignettes/modelisation_approach.Rmd +++ b/vignettes/modelisation_approach.Rmd @@ -188,7 +188,7 @@ of significance (which are abstract). We can use `geom_smooth()`, which can fit non-linear relationships in an empirical way, to give us an idea of the shape of the relationships. ```{r} -data %>% +data |> group_by(Participant, Condition) |> ggplot(aes(x = Experimental_Variable, y = RT, color = Condition)) + geom_jitter(alpha = 0.4) + From 561a2c52fdfbd494f60dae9b0975bf761caaecc2 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 08:46:47 +0200 Subject: [PATCH 20/24] Update modelisation_approach.Rmd --- vignettes/modelisation_approach.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/modelisation_approach.Rmd b/vignettes/modelisation_approach.Rmd index d4fe404e..aaf05f22 100644 --- a/vignettes/modelisation_approach.Rmd +++ b/vignettes/modelisation_approach.Rmd @@ -189,7 +189,7 @@ We can use `geom_smooth()`, which can fit non-linear relationships in an empiric ```{r} data |> - group_by(Participant, Condition) |> + data_group(c("Participant", "Condition")) |> ggplot(aes(x = Experimental_Variable, y = RT, color = Condition)) + geom_jitter(alpha = 0.4) + geom_smooth(method = "loess", se = FALSE) + From 0bd8bd60742b689a968132108295cd9d699746d3 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 09:23:17 +0200 Subject: [PATCH 21/24] add favicons --- _pkgdown.yml => pkgdown/_pkgdown.yml | 0 pkgdown/favicon/apple-touch-icon-120x120.png | Bin 0 -> 17022 bytes pkgdown/favicon/apple-touch-icon-152x152.png | Bin 0 -> 23432 bytes pkgdown/favicon/apple-touch-icon-180x180.png | Bin 0 -> 29274 bytes pkgdown/favicon/apple-touch-icon-60x60.png | Bin 0 -> 6353 bytes pkgdown/favicon/apple-touch-icon-76x76.png | Bin 0 -> 8834 bytes pkgdown/favicon/apple-touch-icon.png | Bin 0 -> 29274 bytes pkgdown/favicon/favicon-16x16.png | Bin 0 -> 1364 bytes pkgdown/favicon/favicon-32x32.png | Bin 0 -> 2676 bytes pkgdown/favicon/favicon.ico | Bin 0 -> 15086 bytes 10 files changed, 0 insertions(+), 0 deletions(-) rename _pkgdown.yml => pkgdown/_pkgdown.yml (100%) create mode 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zogV|y`AydLF7I>;Ko61)v-XZp{+enK2R?&#w0}^}XN>RYj6tQY9~}pp9fKCMuj6Td z)2K;1_+r-B2z>G`>k(iYh7K4;cnK*mB*U=7H9}Z2DE;<6jmO}z8sH0_gXiKo394Y6 zf?|S}FgXisA`Vm?9hkXn{r%wKNw0a&dy)Y{v9orn8vEO(vf~jskee>^d-LMsl z_JmyNW`A2-6x>J-!_d5=OU29y#u?(xhW0g%Zm#f*Ou+Ws^O#=v2WFMr#O|CTxCRCy ze4j4%Z_ursWRxoT%K3?sD3ty;>=-(ZH%!a(U%UX>rh5^Zd;-f7E@80OZ459j$1esC z(bN4dre|D(Rd_u8-+&5^sw!ccL0mrf6@{Ra((d5G_~mmT_B)Eb>9^2d_ZohkT!Inf zFJtJKV)UCzXG)tJ@X0Qvn9qpp{Uj{W@hRlKw3beDbwdi{sDDOKDE6d1z`z|9m^Qrt z8&xc^W}z>JPQQ+^wiWQtx`r%cUBX8u@&8^Fa^L#?p@nRdlh-anLQo_$qbe{?yBsQO zi=nu(3}X&d!y@etGM$}}Kz=Nmbi1kkv19{NG^l>V%MT8b#ZdKp0?lLh;hJzB8TK{} zwfYVHU(&C%C#*1va1xHs*k>iuIWKjG2BQ2Ukao}%>2&7f+6?*cvE*Zy?DDIp-vgZ% zTZs#^^|lCg8=Ui(nd}mL`w5CQ3aIu%Mlk}{TyV_8O-jCcopov<<+eMCywGuq^@+Sl zeLZAvSxK=1o$<(jb1ekNSKNgO)nNeD;`KlZf#!+fQV=Pk}a z9j Date: Fri, 24 May 2024 11:18:17 +0200 Subject: [PATCH 22/24] fix --- R/estimate_predicted.R | 4 +++- man/estimate_expectation.Rd | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/R/estimate_predicted.R b/R/estimate_predicted.R index 6cf92133..038c4620 100644 --- a/R/estimate_predicted.R +++ b/R/estimate_predicted.R @@ -176,7 +176,9 @@ #' # Bayesian models #' \donttest{ #' if (require("rstanarm")) { -#' model <- rstanarm::stan_glm(mpg ~ wt, data = mtcars, refresh = 0, iter = 200) +#' model <- suppressWarnings(rstanarm::stan_glm( +#' mpg ~ wt, data = mtcars, refresh = 0, iter = 200 +#' )) #' estimate_response(model) #' estimate_relation(model) #' } diff --git a/man/estimate_expectation.Rd b/man/estimate_expectation.Rd index f7a220bd..663b76df 100644 --- a/man/estimate_expectation.Rd +++ b/man/estimate_expectation.Rd @@ -236,7 +236,9 @@ if (require("lme4")) { # Bayesian models \donttest{ if (require("rstanarm")) { - model <- rstanarm::stan_glm(mpg ~ wt, data = mtcars, refresh = 0, iter = 200) + model <- suppressWarnings(rstanarm::stan_glm( + mpg ~ wt, data = mtcars, refresh = 0, iter = 200 + )) estimate_response(model) estimate_relation(model) } From ec683fd6e222f5bd8d0521861a6431170350d6b0 Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 11:46:24 +0200 Subject: [PATCH 23/24] fix --- R/estimate_predicted.R | 5 +++-- R/estimate_slopes.R | 2 +- man/estimate_expectation.Rd | 3 ++- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/R/estimate_predicted.R b/R/estimate_predicted.R index 038c4620..1c6c11bc 100644 --- a/R/estimate_predicted.R +++ b/R/estimate_predicted.R @@ -177,7 +177,8 @@ #' \donttest{ #' if (require("rstanarm")) { #' model <- suppressWarnings(rstanarm::stan_glm( -#' mpg ~ wt, data = mtcars, refresh = 0, iter = 200 +#' mpg ~ wt, +#' data = mtcars, refresh = 0, iter = 200 #' )) #' estimate_response(model) #' estimate_relation(model) @@ -209,7 +210,7 @@ estimate_response <- function(...) { # TODO: If estimate_response() is removed, document `NULL` with this text. insight::format_alert( "`estimate_response()` is deprecated.", - "Please use `estimate_expectation()` (for conditional expected values) or `estimate_prediction()` (for individual case predictions) instead." + "Please use `estimate_expectation()` (for conditional expected values) or `estimate_prediction()` (for individual case predictions) instead." # nolint ) estimate_expectation(...) } diff --git a/R/estimate_slopes.R b/R/estimate_slopes.R index d913a295..d3587927 100644 --- a/R/estimate_slopes.R +++ b/R/estimate_slopes.R @@ -197,7 +197,7 @@ summary.estimate_slopes <- function(object, ...) { .estimate_slopes_summarize <- function(data, trend, ...) { # Find beginnings and ends ----------------------- # First row - starting point - centrality_columns <- datawizard::data_find( + centrality_columns <- datawizard::extract_column_names( data, c("Coefficient", "Median", "Mean", "MAP_Estimate"), verbose = FALSE diff --git a/man/estimate_expectation.Rd b/man/estimate_expectation.Rd index 663b76df..144c1a5d 100644 --- a/man/estimate_expectation.Rd +++ b/man/estimate_expectation.Rd @@ -237,7 +237,8 @@ if (require("lme4")) { \donttest{ if (require("rstanarm")) { model <- suppressWarnings(rstanarm::stan_glm( - mpg ~ wt, data = mtcars, refresh = 0, iter = 200 + mpg ~ wt, + data = mtcars, refresh = 0, iter = 200 )) estimate_response(model) estimate_relation(model) From aabb649fddecd28ec8f476ad43ae256cbe5c4a6c Mon Sep 17 00:00:00 2001 From: Daniel Date: Fri, 24 May 2024 13:28:54 +0200 Subject: [PATCH 24/24] fix --- R/estimate_grouplevel.R | 43 +++++++++++++++----------------------- man/estimate_grouplevel.Rd | 43 ++++++++++++++++---------------------- 2 files changed, 35 insertions(+), 51 deletions(-) diff --git a/R/estimate_grouplevel.R b/R/estimate_grouplevel.R index fc15d13a..6a1242bd 100644 --- a/R/estimate_grouplevel.R +++ b/R/estimate_grouplevel.R @@ -18,39 +18,30 @@ #' indices (such as SE and CI), as these are not computable. #' @param ... Other arguments passed to or from other methods. #' -#' @examples +#' @examplesIf require("lme4") && require("see") #' # lme4 model -#' if (require("lme4") && require("see")) { -#' model <- lmer(mpg ~ hp + (1 | carb), data = mtcars) -#' random <- estimate_grouplevel(model) -#' random +#' data(mtcars) +#' model <- lme4::lmer(mpg ~ hp + (1 | carb), data = mtcars) +#' random <- estimate_grouplevel(model) +#' random #' -#' # Visualize random effects -#' plot(random) +#' # Visualize random effects +#' plot(random) #' -#' # Show group-specific effects -#' estimate_grouplevel(model, deviation = FALSE) +#' # Show group-specific effects +#' estimate_grouplevel(model, deviation = FALSE) #' -#' # Reshape to wide data so that it matches the original dataframe... -#' reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE")) +#' # Reshape to wide data so that it matches the original dataframe... +#' reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE")) #' -#' # ... and can be easily combined -#' alldata <- cbind(mtcars, reshaped) +#' # ... and can be easily combined +#' alldata <- cbind(mtcars, reshaped) #' -#' # Use summary() to remove duplicated rows -#' summary(reshaped) +#' # Use summary() to remove duplicated rows +#' summary(reshaped) #' -#' # Compute BLUPs -#' estimate_grouplevel(model, type = "total") -#' } -#' -#' # Bayesian models -#' \donttest{ -#' if (require("rstanarm")) { -#' model <- rstanarm::stan_lmer(mpg ~ hp + (1 | carb) + (1 | gear), data = mtcars, refresh = 0) -#' # Broken estimate_grouplevel(model) -#' } -#' } +#' # Compute BLUPs +#' estimate_grouplevel(model, type = "total") #' @export estimate_grouplevel <- function(model, type = "random", ...) { # Extract params diff --git a/man/estimate_grouplevel.Rd b/man/estimate_grouplevel.Rd index 2c1541b6..ae32515a 100644 --- a/man/estimate_grouplevel.Rd +++ b/man/estimate_grouplevel.Rd @@ -38,36 +38,29 @@ models. Can be reshaped to be of the same dimensions as the original data, which can be useful to add the random effects to the original data. } \examples{ +\dontshow{if (require("lme4") && require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} # lme4 model -if (require("lme4") && require("see")) { - model <- lmer(mpg ~ hp + (1 | carb), data = mtcars) - random <- estimate_grouplevel(model) - random +data(mtcars) +model <- lme4::lmer(mpg ~ hp + (1 | carb), data = mtcars) +random <- estimate_grouplevel(model) +random - # Visualize random effects - plot(random) +# Visualize random effects +plot(random) - # Show group-specific effects - estimate_grouplevel(model, deviation = FALSE) +# Show group-specific effects +estimate_grouplevel(model, deviation = FALSE) - # Reshape to wide data so that it matches the original dataframe... - reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE")) +# Reshape to wide data so that it matches the original dataframe... +reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE")) - # ... and can be easily combined - alldata <- cbind(mtcars, reshaped) +# ... and can be easily combined +alldata <- cbind(mtcars, reshaped) - # Use summary() to remove duplicated rows - summary(reshaped) +# Use summary() to remove duplicated rows +summary(reshaped) - # Compute BLUPs - estimate_grouplevel(model, type = "total") -} - -# Bayesian models -\donttest{ -if (require("rstanarm")) { - model <- rstanarm::stan_lmer(mpg ~ hp + (1 | carb) + (1 | gear), data = mtcars, refresh = 0) - # Broken estimate_grouplevel(model) -} -} +# Compute BLUPs +estimate_grouplevel(model, type = "total") +\dontshow{\}) # examplesIf} }

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