In egeminiani/GJRM: Generalised Joint Regression Modelling

knitr::opts_chunk$set(
  warning = FALSE,
  message = FALSE,
  collapse = TRUE,
  comment = "#>"
)

Generate data with double sample selection.

library(GJRM)
set.seed(0)

n <- 5000

Sigma <- matrix(c(1,   0.5, 0.4,
                  0.5,   1, 0.6,
                  0.4, 0.6,   1 ), 3, 3)

u <- rMVN(n, rep(0,3), Sigma)

f1   <- function(x) cos(pi*2*x) + sin(pi*x)
f2   <- function(x) x+exp(-30*(x-0.5)^2) 

x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
x4 <- runif(n)

y1 <-  1    + 1.5*x1 -     x2 + 0.8*x3 - f1(x4) + u[, 1] > 0
y2 <-  1    - 2.5*x1 + 1.2*x2 +     x3          + u[, 2] > 0
y3 <-  1.58 + 1.5*x1 - f2(x2)                   + u[, 3] > 0

dataSim <- data.frame(y1, y2, y3, x1, x2, x3, x4)

f.l <- list(y1 ~ x1 + x2 + x3 + s(x4), 
            y2 ~ x1 + x2 + x3, 
            y3 ~ x1 + s(x2))   

out <- gjrm(f.l, data = dataSim, Model = "TSS",
            margins = c("probit", "probit", "probit"))
conv.check(out)
summary(out)
plot(out, eq = 1)
plot(out, eq = 3)
prev(out)
prev(out, type = "univariate")
prev(out, type = "naive")