gg_CD_global {recalibratiNN}

Plots Cumulative Distributions of PIT-values for global calibration diagnose.

Description

ggplot to visualize predicted vs empirical cumulative distributions of PIT-values.

Usage

gg_CD_global(pit, ycal, yhat, mse)

Arguments

pit

vector of global PIT-values

ycal

vector of y calibration set

yhat

vector of predicted y on calibration set

mse

Mean Squared Error from calibration set

Value

a ggplot point graph

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( y_cal, y_hat,  MSE_cal)

gg_CD_global(pit,y_cal, y_hat, MSE_cal)