gg_PIT_global: Plots Density Distributions of PIT-values for Global...

In recalibratiNN: Quantile Recalibration for Regression Models

Plots Density Distributions of PIT-values for Global Calibration Diagnostics

Description

This function generates a ggplot visual representation of the density of Probability Integral Transform (PIT) values globally. For advanced customization of the plot layers, refer to the ggplot2 User Guide.

Usage

gg_PIT_global(
  pit,
  type = "density",
  fill = "steelblue4",
  alpha = 0.8,
  print_p = TRUE
)

Arguments

pit	Vector of PIT values to be plotted.
type	Character string specifying the type of plot: either "density" or "histogram". This determines the representation style of the PIT values.
fill	Character string defining the fill color of the plot. Default is 'steelblue4'.
alpha	Numeric value for the opacity of the plot fill, with 0 being fully transparent and 1 being fully opaque. Default is 0.8.
print_p	Logical value indicating whether to print the p-value from the Kolmogorov-Smirnov test. Useful for statistical diagnostics.

Details

This function also tests the PIT-values for uniformity using the Kolmogorov-Smirnov test (ks.test). The p-value from the test is printed on the plot if print_p is set to TRUE.

Value

A ggplot object depicting a density graph of PIT-values, which can be further customized.

Examples

n <- 10000
split <- 0.8

# generating heterocedastic data
mu <- function(x1){
10 + 5*x1^2
}

sigma_v <- function(x1){
30*x1
}

x <- runif(n, 1, 10)
y <- rnorm(n, mu(x), sigma_v(x))

x_train <- x[1:(n*split)]
y_train <- y[1:(n*split)]

x_cal <- x[(n*split+1):n]
y_cal <- y[(n*split+1):n]

model <- lm(y_train ~ x_train)

y_hat <- predict(model, newdata=data.frame(x_train=x_cal))

MSE_cal <- mean((y_hat - y_cal)^2)

pit <- PIT_global(ycal=y_cal, yhat=y_hat, mse=MSE_cal)

gg_PIT_global(pit)

recalibratiNN documentation built on April 4, 2025, 4:14 a.m.