update model

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.11.0.6
+Version: 0.11.0.7
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

R/check_distribution.R

@@ -34,12 +34,11 @@ NULL
 #' This function uses an internal random forest model to classify the
 #' distribution from a model-family. Currently, following distributions are
 #' trained (i.e. results of `check_distribution()` may be one of the
-#' following): `"bernoulli"`, `"beta"`, `"beta-binomial"`,
-#' `"binomial"`, `"chi"`, `"exponential"`, `"F"`,
-#' `"gamma"`, `"lognormal"`, `"normal"`, `"negative
-#' binomial"`, `"negative binomial (zero-inflated)"`, `"pareto"`,
-#' `"poisson"`, `"poisson (zero-inflated)"`, `"uniform"` and
-#' `"weibull"`.
+#' following): `"bernoulli"`, `"beta"`, `"beta-binomial"`, `"binomial"`,
+#' `"cauchy"`, `"chi"`, `"exponential"`, `"F"`, `"gamma"`, `"half-cauchy"`,
+#' `"inverse-gamma"`, `"lognormal"`, `"normal"`, `"negative binomial"`,
+#' `"negative binomial (zero-inflated)"`, `"pareto"`, `"poisson"`,
+#' `"poisson (zero-inflated)"`, `"tweedie"`, `"uniform"` and `"weibull"`.
 #' \cr \cr
 #' Note the similarity between certain distributions according to shape, skewness,
 #' etc. Thus, the predicted distribution may not be perfectly representing the
@@ -222,6 +221,7 @@ check_distribution.numeric <- function(model) {
     Min = min(x),
     Max = max(x),
     Proportion_Positive = sum(x >= 0) / length(x),
+    Proportion_Zero = sum(x == 0) / length(x),
     Integer = all(.is_integer(x))
   )
 }

WIP/generate_distribution.R

@@ -134,12 +134,25 @@ for (di in seq_along(distrs)) {
     # x_scaled <- parameters::normalize(x, verbose = FALSE)
 
     if (length(x) >= 10) {
+      mode <- tryCatch(
+        bayestestR::map_estimate(x, bw = "nrd0"),
+        error = function(e) NULL
+      )
+      if (is.null(mode)) {
+        mode <- tryCatch(
+          bayestestR::map_estimate(x, bw = "kernel"),
+          error = function(e) NULL
+        )
+      }
+      if (is.null(mode)) {
+        mode <- datawizard::distribution_mode(x)
+      }
       # Extract features
       data <- data.frame(
         "SD" = sd(x),
         "MAD" = mad(x, constant = 1),
         "Mean_Median_Distance" = mean(x) - median(x),
-        "Mean_Mode_Distance" = mean(x) - as.numeric(bayestestR::map_estimate(x, bw = "nrd0")),
+        "Mean_Mode_Distance" = mean(x) - as.numeric(mode),
         "SD_MAD_Distance" = sd(x) - mad(x, constant = 1),
         "Var_Mean_Distance" = var(x) - mean(x),
         "Range_SD" = diff(range(x)) / sd(x),
@@ -152,8 +165,8 @@ for (di in seq_along(distrs)) {
         "Min" = min(x),
         "Max" = max(x),
         "Proportion_Positive" = sum(x >= 0) / length(x),
-        "Integer" = all(.is.integer(x))
-        # "Proportion_Zero" = sum(x == 0) / length(x)
+        "Integer" = all(.is.integer(x)),
+        "Proportion_Zero" = sum(x == 0) / length(x)
         # "Proportion_Minimum" = sum(x == min(x)) / length(x),
         # "Proportion_Maximum" = sum(x == max(x)) / length(x)
       )