lintr

R/check_distribution.R

@@ -191,33 +191,33 @@ check_distribution.numeric <- function(model) {
   # validation check, remove missings
   x <- x[!is.na(x)]
 
-  mode <- NULL
+  mode_value <- NULL
   # find mode for integer, or MAP for distributions
   if (all(.is_integer(x))) {
-    mode <- datawizard::distribution_mode(x)
+    mode_value <- datawizard::distribution_mode(x)
   } else {
     # this might fail, so we wrap in ".safe()"
-    mode <- tryCatch(
+    mode_value <- tryCatch(
       as.numeric(bayestestR::map_estimate(x, bw = "nrd0")),
       error = function(e) NULL
     )
-    if (is.null(mode)) {
-      mode <- tryCatch(
+    if (is.null(mode_value)) {
+      mode_value <- tryCatch(
         as.numeric(bayestestR::map_estimate(x, bw = "kernel")),
         error = function(e) NULL
       )
     }
   }
 
-  if (is.null(mode)) {
+  if (is.null(mode_value)) {
     mean_mode_diff <- mean(x) - datawizard::distribution_mode(x)
     msg <- "Could not accurately estimate the mode."
     if (!is.null(type)) {
       msg <- paste(msg, "Predicted distribution of the", type, "may be less accurate.")
     }
     insight::format_alert(msg)
   } else {
-    mean_mode_diff <- .safe(mean(x) - mode)
+    mean_mode_diff <- .safe(mean(x) - mode_value)
   }
 
   data.frame(

R/check_homogeneity.R

@@ -165,7 +165,7 @@ check_homogeneity.afex_aov <- function(x, method = "levene", ...) {
     between <- between[!is_covar]
   }
 
-  form <- stats::formula(paste0(dv, "~", paste0(between, collapse = "*")))
+  form <- stats::formula(paste0(dv, "~", paste(between, collapse = "*")))
   test <- car::leveneTest(form, ag_data, center = mean, ...)
 
   p.val <- test[1, "Pr(>F)"]

R/icc.R

@@ -275,11 +275,11 @@ icc <- function(model,
       # this is experimental!
       if (identical(ci_method, "analytical")) {
         result <- .safe(.analytical_icc_ci(model, ci))
-        if (!is.null(result)) {
+        if (is.null(result)) {
+          icc_ci_adjusted <- icc_ci_unadjusted <- NA
+        } else {
           icc_ci_adjusted <- result$ICC_adjusted
           icc_ci_unadjusted <- result$ICC_unadjusted
-        } else {
-          icc_ci_adjusted <- icc_ci_unadjusted <- NA
         }
       } else {
         result <- .bootstrap_icc(model, iterations, tolerance, ci_method, ...)
@@ -428,7 +428,7 @@ print.icc <- function(x, digits = 3, ...) {
   }
 
   # separate lines for multiple R2
-  out <- paste0(out, collapse = "\n")
+  out <- paste(out, collapse = "\n")
 
   cat(out)
   cat("\n")
@@ -693,10 +693,10 @@ print.icc_decomposed <- function(x, digits = 2, ...) {
   }
 
   model_rank <- tryCatch(
-    if (!is.null(model$rank)) {
-      model$rank - df_int
-    } else {
+    if (is.null(model$rank)) {
       insight::n_parameters(model) - df_int
+    } else {
+      model$rank - df_int
     },
     error = function(e) insight::n_parameters(model) - df_int
   )

R/r2_nakagawa.R

@@ -154,7 +154,7 @@ r2_nakagawa <- function(model,
     if (insight::is_empty_object(vars$var.random) || is.na(vars$var.random)) {
       if (verbose) {
         # if no random effect variance, return simple R2
-        insight::print_color("Random effect variances not available. Returned R2 does not account for random effects.\n", "red")
+        insight::print_color("Random effect variances not available. Returned R2 does not account for random effects.\n", "red") # nolint
       }
       r2_marginal <- vars$var.fixed / (vars$var.fixed + vars$var.residual)
       r2_conditional <- NA
@@ -172,11 +172,11 @@ r2_nakagawa <- function(model,
       # this is experimental!
       if (identical(ci_method, "analytical")) {
         result <- .safe(.analytical_icc_ci(model, ci, fun = "r2_nakagawa"))
-        if (!is.null(result)) {
+        if (is.null(result)) {
+          r2_ci_marginal <- r2_ci_conditional <- NA
+        } else {
           r2_ci_marginal <- result$R2_marginal
           r2_ci_conditional <- result$R2_conditional
-        } else {
-          r2_ci_marginal <- r2_ci_conditional <- NA
         }
       } else {
         result <- .bootstrap_r2_nakagawa(model, iterations, tolerance, ci_method, ...)
@@ -266,7 +266,7 @@ print.r2_nakagawa <- function(x, digits = 3, ...) {
   }
 
   # separate lines for multiple R2
-  out <- paste0(out, collapse = "\n")
+  out <- paste(out, collapse = "\n")
 
   cat(out)
   cat("\n")