PIT_global {recalibratiNN}

Obtain the PIT-values of a model.

Description

A function to obtain the (possibly uncalibrated) PIT-values of any fitted model that assumes a normal distribution for the output, such as (but not limited to), a lm() or a neural network that used the Mean Squared Error as the loss function.

Usage

PIT_global(ycal, yhat, mse)

Arguments

ycal

observations of the recalibration set

yhat

predictions of the recalibration set from the uncalibrated model

mse

Mean Squared Error of validation set.

Value

Vector of PIT-values

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_global(ycal=y_cal, yhat=y_hat, mse=MSE_cal)