also use remotes datawizard (#971)

* also use remotes datawizard

* lintr, fix group arg

* fix issues

* Update DESCRIPTION

* fix tests

* fix vignette

* Update DESCRIPTION

* fix lintr

DESCRIPTION

@@ -218,4 +218,4 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight
+Remotes: easystats/insight, easystats/datawizard, easystats/performance, easystats/bayestestR

R/standardize_info.R

@@ -52,7 +52,7 @@ standardize_info.default <- function(model,
   types <- parameters_type(model)
   # model_matrix <- as.data.frame(stats::model.matrix(model))
   model_matrix <- as.data.frame(insight::get_modelmatrix(model))
-  data <- insight::get_data(model, source = "mf", verbose = FALSE)
+  model_data <- insight::get_data(model, source = "mf", verbose = FALSE)
   wgts <- insight::get_weights(model, na_rm = TRUE)
 
   # validation check for ZI
@@ -94,7 +94,7 @@ standardize_info.default <- function(model,
   # Response - Smart
   out <- merge(
     out,
-    .std_info_response_smart(model, mi, data, model_matrix, types, robust = robust, w = wgts),
+    .std_info_response_smart(model, mi, data = model_data, model_matrix, types, robust = robust, w = wgts),
     by = "Parameter", all = TRUE
   )
 
@@ -109,7 +109,7 @@ standardize_info.default <- function(model,
   out <- merge(
     out,
     .std_info_predictors_smart(model,
-      data,
+      data = model_data,
       params,
       types,
       robust = robust,
@@ -134,7 +134,7 @@ standardize_info.default <- function(model,
         model, mi,
         params,
         model_matrix,
-        data,
+        data = model_data,
         types = types$Type,
         robust = robust,
         two_sd = two_sd,
@@ -181,11 +181,11 @@ standardize_info.default <- function(model,
   # Get deviations for all parameters
   means <- deviations <- rep(NA_real_, times = length(params))
   for (i in seq_along(params)) {
-    var <- params[i]
+    variable <- params[i]
     info <- .std_info_predictor_smart(
       data = data,
-      variable = types[types$Parameter == var, "Variable"],
-      type = types[types$Parameter == var, "Type"],
+      variable = types[types$Parameter == variable, "Variable"],
+      type = types[types$Parameter == variable, "Type"],
       robust = robust,
       two_sd = two_sd,
       weights = w
@@ -213,7 +213,7 @@ standardize_info.default <- function(model,
                                       two_sd = FALSE,
                                       weights = NULL,
                                       ...) {
-  if (type == "intercept") {
+  if (type == "intercept") { # nolint
     info <- list(sd = 0, mean = 0)
   } else if (type == "numeric") {
     info <- .compute_std_info(
@@ -272,12 +272,12 @@ standardize_info.default <- function(model,
   # Get deviations for all parameters
   means <- deviations <- rep(NA_real_, length = length(names(model_matrix)))
   for (i in seq_along(names(model_matrix))) {
-    var <- names(model_matrix)[i]
+    variable <- names(model_matrix)[i]
     if (types[i, "Type"] == "intercept") {
       means[i] <- deviations[i] <- 0
     } else {
       std_info <- .compute_std_info(
-        data = model_matrix, variable = var,
+        data = model_matrix, variable = variable,
         robust = robust, two_sd = two_sd, weights = w
       )
       deviations[i] <- std_info$sd
@@ -337,9 +337,9 @@ standardize_info.default <- function(model,
     }
     means <- deviations <- rep(NA_real_, length = length(names(model_matrix)))
     for (i in seq_along(names(model_matrix))) {
-      var <- names(model_matrix)[i]
-      if (any(types$Parameter == var) && types$Link[types$Parameter == var] == "Difference") {
-        parent_var <- types$Variable[types$Parameter == var]
+      variable <- names(model_matrix)[i]
+      if (any(types$Parameter == variable) && types$Link[types$Parameter == variable] == "Difference") {
+        parent_var <- types$Variable[types$Parameter == variable]
         intercept <- unique(data[[parent_var]])[1]
         response_at_intercept <- response[data[[parent_var]] == intercept]
         weights_at_intercept <- if (length(w)) w[data[[parent_var]] == intercept] else NULL
@@ -433,7 +433,7 @@ standardize_info.default <- function(model,
   is_within <- logical(length = length(params))
   is_within[] <- NA
   for (i in seq_along(params)) {
-    if (types[i] == "intercept") {
+    if (types[i] == "intercept") { # nolint
       is_within[i] <- FALSE
     } else if (types[i] == "numeric") {
       is_within[i] <- insight::clean_names(params[i]) %in% within_vars
@@ -459,7 +459,7 @@ standardize_info.default <- function(model,
 
     dm <- datawizard::demean(cbind(id, temp_d),
       select = colnames(temp_d),
-      group = "id"
+      by = "id"
     )
     dm <- dm[, paste0(colnames(temp_d), "_between"), drop = FALSE]
 

tests/testthat/test-marginaleffects.R

@@ -5,7 +5,7 @@ skip_if_not_installed("rstanarm")
 test_that("marginaleffects()", {
   # Frequentist
   x <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
-  model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
+  model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, by = "Species"), variables = "Petal.Length")
   out <- parameters(model)
   expect_identical(nrow(out), 1L)
   cols <- c("Parameter", "Comparison", "Coefficient", "SE", "Statistic", "p", "S", "CI", "CI_low", "CI_high")
@@ -23,7 +23,7 @@ test_that("marginaleffects()", {
       chains = 1
     )
   )
-  model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
+  model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, by = "Species"), variables = "Petal.Length")
   expect_identical(nrow(parameters(model)), 1L)
 })
 
@@ -46,7 +46,7 @@ test_that("comparisons()", {
   data(iris)
   # Frequentist
   x <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
-  m <- marginaleffects::avg_comparisons(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
+  m <- marginaleffects::avg_comparisons(x, newdata = insight::get_datagrid(x, by = "Species"), variables = "Petal.Length")
   expect_identical(nrow(parameters(m)), 1L)
   out <- parameters(m, exponentiate = TRUE)
   expect_equal(out$Coefficient, 1.393999, tolerance = 1e-4)
@@ -63,7 +63,7 @@ test_that("comparisons()", {
   )
   m <- marginaleffects::avg_slopes(
     x,
-    newdata = insight::get_datagrid(x, at = "Species"),
+    newdata = insight::get_datagrid(x, by = "Species"),
     variables = "Petal.Length"
   )
   expect_identical(nrow(parameters(m)), 1L)
@@ -85,7 +85,7 @@ test_that("multiple contrasts: Issue #779", {
   cmp <- suppressWarnings(marginaleffects::comparisons(
     mod,
     variables = c("gear", "cyl"),
-    newdata = insight::get_datagrid(mod, at = c("gear", "cyl")),
+    newdata = insight::get_datagrid(mod, by = c("gear", "cyl")),
     cross = TRUE
   ))
   cmp <- suppressWarnings(parameters(cmp))

tests/testthat/test-model_parameters.fixest.R

@@ -16,7 +16,7 @@ test_that("model_parameters.fixest", {
 
   qol_cancer <- cbind(
     qol_cancer,
-    datawizard::demean(qol_cancer, select = c("phq4", "QoL"), group = "ID")
+    datawizard::demean(qol_cancer, select = c("phq4", "QoL"), by = "ID")
   )
 
   m1 <- fixest::feols(QoL ~ time + phq4 | ID, data = qol_cancer)

tests/testthat/test-model_parameters.mixed.R

@@ -108,7 +108,7 @@ test_that("model_parameters.mixed-all_pars", {
 data("qol_cancer")
 qol_cancer <- cbind(
   qol_cancer,
-  demean(qol_cancer, select = c("phq4", "QoL"), group = "ID")
+  demean(qol_cancer, select = c("phq4", "QoL"), by = "ID")
 )
 model <- lme4::lmer(
   QoL ~ time + phq4_within + phq4_between + (1 | ID),

tests/testthat/test-standardize_parameters.R

@@ -150,7 +150,7 @@ test_that("standardize_parameters (lm with ci)", {
 # aov ---------------------------------------------------------------------
 test_that("standardize_parameters (aov)", {
   dat2 <- iris
-  dat2$Cat1 <- rep(c("A", "B"), length.out = nrow(dat2))
+  dat2$Cat1 <- rep_len(c("A", "B"), nrow(dat2))
   dat3 <<- dat2
 
   m_aov <- aov(Sepal.Length ~ Species * Cat1, data = dat3)
@@ -198,7 +198,9 @@ test_that("standardize_parameters (with functions /  interactions)", {
   m1 <- lm(exp(cyl) ~ am + sqrt(mpg), mtcars)
   m2 <- lm(cyl_exp ~ am + mpg_sqrt, mtcars)
 
-  expect_message(stdX <- standardize_parameters(m1, method = "refit"))
+  expect_message({
+    stdX <- standardize_parameters(m1, method = "refit")
+  })
   expect_false(isTRUE(all.equal(
     stdX[[2]],
     standardize_parameters(m2, method = "refit")[[2]]
@@ -258,7 +260,8 @@ test_that("standardize_parameters (exponentiate)", {
   )
   expect_equal(
     mod_refit[[2]][-1],
-    exp(standardize_parameters(mod_b, method = "basic")[[2]])[-1]
+    exp(standardize_parameters(mod_b, method = "basic")[[2]])[-1],
+    tolerance = 1e-5
   )
 
 
@@ -270,15 +273,18 @@ test_that("standardize_parameters (exponentiate)", {
 
   expect_equal(
     mod_refit[[2]][-1],
-    standardize_parameters(mod_b, method = "basic", exponentiate = TRUE)[[2]][-1]
+    standardize_parameters(mod_b, method = "basic", exponentiate = TRUE)[[2]][-1],
+    tolerance = 1e-5
   )
   expect_equal(
     mod_refit[[2]][-1],
-    standardize_parameters(mod_b, method = "posthoc", exponentiate = TRUE)[[2]][-1]
+    standardize_parameters(mod_b, method = "posthoc", exponentiate = TRUE)[[2]][-1],
+    tolerance = 1e-5
   )
   expect_equal(
     mod_refit[[2]][-1],
-    exp(standardize_parameters(mod_b, method = "basic")[[2]])[-1]
+    exp(standardize_parameters(mod_b, method = "basic")[[2]])[-1],
+    tolerance = 1e-5
   )
 })
 
@@ -289,12 +295,12 @@ test_that("standardize_parameters (Bayes)", {
   skip_if_not_installed("rstanarm")
 
   set.seed(1234)
-  suppressWarnings(
+  suppressWarnings({
     model <- rstanarm::stan_glm(Sepal.Length ~ Species + Petal.Width,
       data = iris,
       iter = 500, refresh = 0
     )
-  )
+  })
 
   expect_equal(
     suppressWarnings(standardize_parameters(model, method = "refit")$Std_Median[1:4]),
@@ -309,7 +315,7 @@ test_that("standardize_parameters (Bayes)", {
   )
 
   posts <- standardize_posteriors(model, method = "posthoc")
-  expect_equal(dim(posts), c(1000, 4))
+  expect_identical(dim(posts), c(1000L, 4L))
   expect_s3_class(posts, "data.frame")
 })
 
@@ -324,7 +330,7 @@ test_that("standardize_parameters (Pseudo - GLMM)", {
     X = rnorm(1000),
     Z = rnorm(1000),
     C = sample(letters[1:3], size = 1000, replace = TRUE),
-    ID = sort(rep(letters, length.out = 1000))
+    ID = sort(rep_len(letters, 1000))
   )
   dat <- transform(dat, Y = X + Z + rnorm(1000))
   dat <- cbind(dat, datawizard::demean(dat, c("X", "Z"), "ID"))
@@ -340,7 +346,7 @@ test_that("standardize_parameters (Pseudo - GLMM)", {
 
   ## Correctly identify within and between terms
   dev_resp <- standardize_info(m, include_pseudo = TRUE)$Deviation_Response_Pseudo
-  expect_equal(insight::n_unique(dev_resp[c(2, 4, 5, 6)]), 1)
+  expect_identical(insight::n_unique(dev_resp[c(2, 4, 5, 6)]), 1L)
   expect_true(dev_resp[2] != dev_resp[3])
 
 
@@ -354,16 +360,18 @@ test_that("standardize_parameters (Pseudo - GLMM)", {
 
   m0 <- lme4::lmer(Y ~ 1 + (1 | ID), data = dat)
   m0v <- insight::get_variance(m0)
-  SD_y <- c(sqrt(m0v$var.residual), sqrt(m0v$var.intercept))
+  SD_y <- sqrt(c(m0v$var.residual, m0v$var.intercept))
   SD_y <- SD_y[c(1, 2, 1, 1, 1)]
 
   expect_equal(
     data.frame(Deviation_Response_Pseudo = c(SD_y[2], SD_y), Deviation_Pseudo = c(0, SD_x)),
-    standardize_info(m, include_pseudo = TRUE)[, c("Deviation_Response_Pseudo", "Deviation_Pseudo")]
+    standardize_info(m, include_pseudo = TRUE)[, c("Deviation_Response_Pseudo", "Deviation_Pseudo")],
+    tolerance = 1e-5
   )
   expect_equal(
     standardize_parameters(m, method = "pseudo")$Std_Coefficient[-1],
-    unname(b * SD_x / SD_y)
+    unname(b * SD_x / SD_y),
+    tolerance = 1e-5
   )
 
 
@@ -463,8 +471,8 @@ test_that("include_response | (g)lm", {
   par_z2 <- standardize_parameters(m, method = "basic", include_response = FALSE)
 
   expect_equal(coef(m_z), par_z1$Std_Coefficient, ignore_attr = TRUE)
-  expect_equal(par_z1$Std_Coefficient[-1], par_z2$Std_Coefficient[-1])
-  expect_equal(par_z0$Std_Coefficient * sd(iris$Sepal.Length), par_z2$Std_Coefficient)
+  expect_equal(par_z1$Std_Coefficient[-1], par_z2$Std_Coefficient[-1], tolerance = 1e-5)
+  expect_equal(par_z0$Std_Coefficient * sd(iris$Sepal.Length), par_z2$Std_Coefficient, tolerance = 1e-5)
 
   # glm ---
   m <- glm(am ~ mpg, mtcars, family = binomial())
@@ -485,14 +493,14 @@ test_that("include_response | parameters", {
   pars <- model_parameters(m, effects = "fixed")
   pars_z0 <- standardize_parameters(pars, method = "basic")
   pars_z1 <- standardize_parameters(pars, method = "basic", include_response = FALSE)
-  expect_equal(pars_z0$Std_Coefficient[-1] * sd(iris$Sepal.Length), pars_z1$Std_Coefficient[-1])
+  expect_equal(pars_z0$Std_Coefficient[-1] * sd(iris$Sepal.Length), pars_z1$Std_Coefficient[-1], tolerance = 1e-5)
 
   # boot ---
   skip_if_not_installed("boot")
   pars <- bootstrap_parameters(m)
   pars_z0 <- standardize_parameters(pars, method = "basic")
   pars_z1 <- standardize_parameters(pars, method = "basic", include_response = FALSE)
-  expect_equal(pars_z0$Std_Coefficient[-1] * sd(iris$Sepal.Length), pars_z1$Std_Coefficient[-1])
+  expect_equal(pars_z0$Std_Coefficient[-1] * sd(iris$Sepal.Length), pars_z1$Std_Coefficient[-1], tolerance = 1e-5)
 })
 
 
@@ -504,8 +512,12 @@ test_that("include_response | bayes", {
   iris$Sepal.Length <- iris$Sepal.Length * 5
   m <- rstanarm::stan_glm(Sepal.Length ~ Petal.Length + Petal.Width, data = iris, refresh = 0)
 
-  expect_warning(m_z <- datawizard::standardize(m, include_response = FALSE))
-  expect_warning(par_z1 <- standardize_posteriors(m, include_response = FALSE))
+  expect_warning({
+    m_z <- datawizard::standardize(m, include_response = FALSE)
+  })
+  expect_warning({
+    par_z1 <- standardize_posteriors(m, include_response = FALSE)
+  })
   par_z0 <- standardize_posteriors(m, method = "basic")
   par_z2 <- standardize_posteriors(m, method = "basic", include_response = FALSE)