gg_PIT_global {recalibratiNN}

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)