gg_CD_local {recalibratiNN}

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

Description

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

Usage

gg_CD_local(
  pit_local,
  psz = 0.01,
  abline = "black",
  pal = "Set2",
  facet = FALSE,
  ...
)

Arguments

pit_local

A data frame obtained from PIT_local_lm

psz

double that indicates size of the points that compose the lines. Default is 0.001

abline

Color of horizontal line that indicates density 1. Default is"red"

pal

Palette name from RColorBrewer. Default is "Set2'

facet

logical value in case separate visualization is preferred. Default is F

...

Other parameters to pass ggplot

Value

a ggplot graph

Examples

n <- 10000
split <- 0.8

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_local <- PIT_local(xcal = x_cal, ycal=y_cal, yhat=y_hat, mse=MSE_cal)

gg_CD_local(pit_local)
gg_CD_local(pit_local, facet=TRUE)