lintr

R/r2.R

@@ -896,12 +896,10 @@ r2.DirichletRegModel <- function(model, ...) {
       m <- sum(w * f / sum(w))
       mss <- sum(w * (f - m)^2)
     }
+  } else if (is.null(w)) {
+    mss <- sum(f^2)
   } else {
-    if (is.null(w)) {
-      mss <- sum(f^2)
-    } else {
-      mss <- sum(w * f^2)
-    }
+    mss <- sum(w * f^2)
   }
   if (is.null(w)) {
     rss <- sum(r^2)

R/r2_bayes.R

@@ -201,28 +201,26 @@ r2_posterior.brmsfit <- function(model, verbose = TRUE, ...) {
           })
           names(br2) <- res
         }
+      } else if (mi$is_mixed) {
+        br2 <- list(
+          R2_Bayes = as.vector(rstantools::bayes_R2(
+            model,
+            re.form = NULL,
+            re_formula = NULL,
+            summary = FALSE
+          )),
+          R2_Bayes_marginal = as.vector(rstantools::bayes_R2(
+            model,
+            re.form = NA,
+            re_formula = NA,
+            summary = FALSE
+          ))
+        )
+        names(br2$R2_Bayes) <- rep("Conditional R2", length(br2$R2_Bayes))
+        names(br2$R2_Bayes_marginal) <- rep("Marginal R2", length(br2$R2_Bayes))
       } else {
-        if (mi$is_mixed) {
-          br2 <- list(
-            R2_Bayes = as.vector(rstantools::bayes_R2(
-              model,
-              re.form = NULL,
-              re_formula = NULL,
-              summary = FALSE
-            )),
-            R2_Bayes_marginal = as.vector(rstantools::bayes_R2(
-              model,
-              re.form = NA,
-              re_formula = NA,
-              summary = FALSE
-            ))
-          )
-          names(br2$R2_Bayes) <- rep("Conditional R2", length(br2$R2_Bayes))
-          names(br2$R2_Bayes_marginal) <- rep("Marginal R2", length(br2$R2_Bayes))
-        } else {
-          br2 <- list(R2_Bayes = as.vector(rstantools::bayes_R2(model, summary = FALSE)))
-          names(br2$R2_Bayes) <- rep("R2", length(br2$R2_Bayes))
-        }
+        br2 <- list(R2_Bayes = as.vector(rstantools::bayes_R2(model, summary = FALSE)))
+        names(br2$R2_Bayes) <- rep("R2", length(br2$R2_Bayes))
       }
 
       br2
@@ -400,7 +398,7 @@ as.data.frame.r2_bayes <- function(x, ...) {
 
   # remove sig and g cols
   params_theta <- params[, !grepl(pattern = "^sig2$|^g_|^g$", colnames(params))]
-  params_sigma <- sqrt(params[, grepl(pattern = "^sig2$", colnames(params))])
+  params_sigma <- sqrt(params[, colnames(params) == "sig2"])
 
   # Model Matrix
   mm <- insight::get_modelmatrix(model[1])

R/r2_coxsnell.R

@@ -69,20 +69,20 @@ r2_coxsnell.glm <- function(model, verbose = TRUE, ...) {
   if (is.null(info)) {
     info <- suppressWarnings(insight::model_info(model, verbose = FALSE))
   }
+  # Cox & Snell's R2 is not defined for binomial models that are not Bernoulli models
   if (info$is_binomial && !info$is_bernoulli && class(model)[1] == "glm") {
     if (verbose) {
       insight::format_alert("Can't calculate accurate R2 for binomial models that are not Bernoulli models.")
     }
     return(NULL)
-  } else {
-    # if no deviance, return NA
-    if (is.null(model$deviance)) {
-      return(NULL)
-    }
-    r2_coxsnell <- (1 - exp((model$deviance - model$null.deviance) / insight::n_obs(model, disaggregate = TRUE)))
-    names(r2_coxsnell) <- "Cox & Snell's R2"
-    r2_coxsnell
   }
+  # if no deviance, return NULL
+  if (is.null(model$deviance)) {
+    return(NULL)
+  }
+  r2_coxsnell <- (1 - exp((model$deviance - model$null.deviance) / insight::n_obs(model, disaggregate = TRUE)))
+  names(r2_coxsnell) <- "Cox & Snell's R2"
+  r2_coxsnell
 }
 
 #' @export
@@ -95,22 +95,22 @@ r2_coxsnell.glmmTMB <- function(model, verbose = TRUE, ...) {
   if (is.null(info)) {
     info <- suppressWarnings(insight::model_info(model, verbose = FALSE))
   }
+  # Cox & Snell's R2 is not defined for binomial models that are not Bernoulli models
   if (info$is_binomial && !info$is_bernoulli) {
     if (verbose) {
       insight::format_alert("Can't calculate accurate R2 for binomial models that are not Bernoulli models.")
     }
     return(NULL)
-  } else {
-    dev <- stats::deviance(model)
-    # if no deviance, return NA
-    if (is.null(dev)) {
-      return(NULL)
-    }
-    null_dev <- stats::deviance(insight::null_model(model))
-    r2_coxsnell <- (1 - exp((dev - null_dev) / insight::n_obs(model, disaggregate = TRUE)))
-    names(r2_coxsnell) <- "Cox & Snell's R2"
-    r2_coxsnell
   }
+  dev <- stats::deviance(model)
+  # if no deviance, return NULL
+  if (is.null(dev)) {
+    return(NULL)
+  }
+  null_dev <- stats::deviance(insight::null_model(model))
+  r2_coxsnell <- (1 - exp((dev - null_dev) / insight::n_obs(model, disaggregate = TRUE)))
+  names(r2_coxsnell) <- "Cox & Snell's R2"
+  r2_coxsnell
 }
 
 

R/test_bf.R

@@ -9,21 +9,21 @@ test_bf <- function(...) {
 #' @export
 test_bf.default <- function(..., reference = 1, text_length = NULL) {
   # Attribute class to list and get names from the global environment
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
-  names(objects) <- match.call(expand.dots = FALSE)$`...`
+  my_objects <- insight::ellipsis_info(..., only_models = TRUE)
+  names(my_objects) <- match.call(expand.dots = FALSE)[["..."]]
 
   # validation checks (will throw error if non-valid objects)
-  .test_performance_checks(objects, multiple = FALSE)
+  .test_performance_checks(objects = my_objects, multiple = FALSE)
 
-  if (length(objects) == 1 && isTRUE(insight::is_model(objects))) {
+  if (length(my_objects) == 1 && isTRUE(insight::is_model(my_objects))) {
     insight::format_error(
-      "`test_bf()` is designed to compare multiple models together. For a single model, you might want to run `bayestestR::bf_parameters()` instead."
+      "`test_bf()` is designed to compare multiple models together. For a single model, you might want to run `bayestestR::bf_parameters()` instead." # nolint
     )
   }
 
   # If a suitable class is found, run the more specific method on it
-  if (inherits(objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
-    test_bf(objects, reference = reference, text_length = text_length)
+  if (inherits(my_objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
+    test_bf(my_objects, reference = reference, text_length = text_length)
   } else {
     insight::format_error("The models cannot be compared for some reason :/")
   }
@@ -87,9 +87,9 @@ test_bf.ListModels <- function(objects, reference = 1, text_length = NULL, ...)
 
   if (all(bayesian_models)) {
     "yes"
-  } else if (!all(bayesian_models)) {
-    "no"
-  } else {
+  } else if (any(bayesian_models)) {
     "mixed"
+  } else {
+    "no"
   }
 }

R/test_likelihoodratio.R

@@ -22,29 +22,29 @@ test_lrt <- test_likelihoodratio
 #' @export
 test_likelihoodratio.default <- function(..., estimator = "OLS", verbose = TRUE) {
   # Attribute class to list
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
+  my_objects <- insight::ellipsis_info(..., only_models = TRUE)
 
   # validation checks (will throw error if non-valid objects)
-  objects <- .test_performance_checks(objects, verbose = verbose)
+  my_objects <- .test_performance_checks(my_objects, verbose = verbose)
 
   # different default when mixed model or glm is included
   if (missing(estimator)) {
-    mixed_models <- sapply(objects, insight::is_mixed_model)
-    if (all(mixed_models) && all(sapply(objects, .is_lmer_reml)) && isTRUE(attributes(objects)$same_fixef)) {
+    mixed_models <- sapply(my_objects, insight::is_mixed_model)
+    if (all(mixed_models) && all(sapply(my_objects, .is_lmer_reml)) && isTRUE(attributes(my_objects)$same_fixef)) {
       estimator <- "REML"
-    } else if (any(mixed_models) || !all(attributes(objects)$is_linear)) {
+    } else if (any(mixed_models) || !all(attributes(my_objects)$is_linear)) {
       estimator <- "ML"
     }
   }
 
   # ensure proper object names
-  objects <- .check_objectnames(objects, sapply(match.call(expand.dots = FALSE)$`...`, as.character))
+  my_objects <- .check_objectnames(my_objects, sapply(match.call(expand.dots = FALSE)[["..."]], as.character))
 
   # If a suitable class is found, run the more specific method on it
-  if (inherits(objects, "ListNestedRegressions")) {
-    test_likelihoodratio(objects, estimator = estimator)
-  } else if (inherits(objects, "ListLavaan")) {
-    test_likelihoodratio_ListLavaan(..., objects = objects) # Because lavaanLRT requires the ellipsis
+  if (inherits(my_objects, "ListNestedRegressions")) {
+    test_likelihoodratio(my_objects, estimator = estimator)
+  } else if (inherits(my_objects, "ListLavaan")) {
+    test_likelihoodratio_ListLavaan(..., objects = my_objects) # Because lavaanLRT requires the ellipsis
   } else {
     insight::format_error(
       "The models are not nested, which is a prerequisite for `test_likelihoodratio()`.",
@@ -106,7 +106,7 @@ test_likelihoodratio.ListNestedRegressions <- function(objects, estimator = "ML"
   same_fixef <- attributes(objects)$same_fixef
 
   # sort by df
-  if (!all(sort(dfs) == dfs) && !all(sort(dfs) == rev(dfs))) {
+  if (is.unsorted(dfs) && is.unsorted(rev(dfs))) {
     objects <- objects[order(dfs)]
     dfs <- sort(dfs, na.last = TRUE)
   }

R/test_performance.R

@@ -236,17 +236,17 @@ test_performance <- function(..., reference = 1, verbose = TRUE) {
 #' @export
 test_performance.default <- function(..., reference = 1, include_formula = FALSE, verbose = TRUE) {
   # Attribute class to list and get names from the global environment
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
+  my_objects <- insight::ellipsis_info(..., only_models = TRUE)
 
   # validation checks (will throw error if non-valid objects)
-  objects <- .test_performance_checks(objects, verbose = verbose)
+  my_objects <- .test_performance_checks(my_objects, verbose = verbose)
 
   # ensure proper object names
-  objects <- .check_objectnames(objects, sapply(match.call(expand.dots = FALSE)$`...`, as.character))
+  my_objects <- .check_objectnames(my_objects, sapply(match.call(expand.dots = FALSE)[["..."]], as.character))
 
   # If a suitable class is found, run the more specific method on it
-  if (inherits(objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
-    test_performance(objects, reference = reference, include_formula = include_formula)
+  if (inherits(my_objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
+    test_performance(my_objects, reference = reference, include_formula = include_formula)
   } else {
     insight::format_error("The models cannot be compared for some reason :/")
   }
@@ -421,10 +421,10 @@ test_performance.ListNonNestedRegressions <- function(objects,
 
 
 .test_performance_init <- function(objects, include_formula = FALSE) {
-  names <- insight::model_name(objects, include_formula = include_formula)
+  model_names <- insight::model_name(objects, include_formula = include_formula)
   out <- data.frame(
     Name = names(objects),
-    Model = names,
+    Model = model_names,
     stringsAsFactors = FALSE
   )
   row.names(out) <- NULL
@@ -453,7 +453,7 @@ test_performance.ListNonNestedRegressions <- function(objects,
 
   if (same_response && !inherits(objects, "ListLavaan") && isFALSE(attributes(objects)$same_response)) {
     insight::format_error(
-      "The models' dependent variables don't have the same data, which is a prerequisite to compare them. Probably the proportion of missing data differs between models."
+      "The models' dependent variables don't have the same data, which is a prerequisite to compare them. Probably the proportion of missing data differs between models." # nolint
     )
   }
 

R/test_vuong.R

@@ -8,17 +8,17 @@ test_vuong <- function(..., verbose = TRUE) {
 #' @export
 test_vuong.default <- function(..., reference = 1, verbose = TRUE) {
   # Attribute class to list and get names from the global environment
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
+  my_objects <- insight::ellipsis_info(..., only_models = TRUE)
 
   # validation checks (will throw error if non-valid objects)
-  objects <- .test_performance_checks(objects, verbose = verbose)
+  my_objects <- .test_performance_checks(my_objects, verbose = verbose)
 
   # ensure proper object names
-  objects <- .check_objectnames(objects, sapply(match.call(expand.dots = FALSE)$`...`, as.character))
+  my_objects <- .check_objectnames(my_objects, sapply(match.call(expand.dots = FALSE)[["..."]], as.character))
 
   # If a suitable class is found, run the more specific method on it
-  if (inherits(objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
-    test_vuong(objects, reference = reference)
+  if (inherits(my_objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
+    test_vuong(my_objects, reference = reference)
   } else {
     insight::format_error("The models cannot be compared for some reason :/")
   }

R/test_wald.R

@@ -8,17 +8,17 @@ test_wald <- function(..., verbose = TRUE) {
 #' @export
 test_wald.default <- function(..., verbose = TRUE) {
   # Attribute class to list and get names from the global environment
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
+  my_objects <- insight::ellipsis_info(..., only_models = TRUE)
 
   # validation checks (will throw error if non-valid objects)
-  objects <- .test_performance_checks(objects, verbose = verbose)
+  my_objects <- .test_performance_checks(my_objects, verbose = verbose)
 
   # ensure proper object names
-  objects <- .check_objectnames(objects, sapply(match.call(expand.dots = FALSE)$`...`, as.character))
+  my_objects <- .check_objectnames(my_objects, sapply(match.call(expand.dots = FALSE)[["..."]], as.character))
 
   # If a suitable class is found, run the more specific method on it
-  if (inherits(objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
-    test_wald(objects)
+  if (inherits(my_objects, c("ListNestedRegressions", "ListNonNestedRegressions", "ListLavaan"))) {
+    test_wald(my_objects)
   } else {
     insight::format_error("The models cannot be compared for some reason :/")
   }
@@ -37,10 +37,10 @@ test_wald.ListNestedRegressions <- function(objects, verbose = TRUE, ...) {
       )
     }
     return(test_likelihoodratio(objects))
-  } else {
-    out <- .test_wald(objects, test = "F")
   }
 
+  out <- .test_wald(objects, test = "F")
+
   attr(out, "is_nested") <- TRUE
   class(out) <- c("test_performance", class(out))
   out
@@ -60,7 +60,7 @@ test_wald.ListNonNestedRegressions <- function(objects, verbose = TRUE, ...) {
   dfs <- sapply(objects, insight::get_df, type = "residual")
 
   # sort by df
-  if (!all(sort(dfs) == dfs) && !all(sort(dfs) == rev(dfs))) {
+  if (is.unsorted(dfs) && is.unsorted(rev(dfs))) {
     objects <- objects[order(dfs)]
     dfs <- sort(dfs, na.last = TRUE)
   }
@@ -78,18 +78,18 @@ test_wald.ListNonNestedRegressions <- function(objects, verbose = TRUE, ...) {
 
   # Find reference-model related stuff
   refmodel <- order(dfs)[1]
-  scale <- dev[refmodel] / dfs[refmodel]
+  my_scale <- dev[refmodel] / dfs[refmodel]
 
   # test = "F"
   if (test == "F") {
-    f_value <- (dev_diff / dfs_diff) / scale
+    f_value <- (dev_diff / dfs_diff) / my_scale
     f_value[!is.na(f_value) & f_value < 0] <- NA # rather than p = 0
-    out$`F` <- f_value
+    out[["F"]] <- f_value
     p <- stats::pf(f_value, abs(dfs_diff), dfs[refmodel], lower.tail = FALSE)
 
     # test = "LRT"
   } else {
-    chi2 <- dev_diff / scale * sign(dfs_diff)
+    chi2 <- dev_diff / my_scale * sign(dfs_diff)
     chi2[!is.na(chi2) & chi2 < 0] <- NA # rather than p = 0
     out$Chi2 <- chi2
     p <- stats::pchisq(chi2, abs(dfs_diff), lower.tail = FALSE)