vcrpart-demo: Synthetic data sets

In vcrpart: Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models

Description

Synthetic data for illustrations.

Usage

data(vcrpart_1)
data(vcrpart_2)
data(vcrpart_3)
data(unemp)

Format

y

ordered factor. The response variable

id, PID

factor. The subject identification vector.

wave

numeric. The wave identification vector.

treat

a dummy variable. The treatment effect.

x1, x2

numeric predictor variables.

z1, z2, z3, z2

moderator (partitioning) variables.

GHQL

self rated general happiness.

YEAR

survey year.

UNEMP

unemployed or not.

AGE

age.

FISIT

self-reported financial situation.

GENDER

gender.

UEREGION

regional unemployment.

See Also

olmm, otsplot, tvcm

Examples

## --------------------------------------------------------- #
## generating 'vcrpart_1'
## --------------------------------------------------------- #

## create skeletton
set.seed(1)
vcrpart_1 <- data.frame(id = factor(rep(1:50, each = 4)),
                        wave = rep(1:4, 50),
                        treat = sample(0:1, 200, TRUE))

## add partitioning variables
vcrpart_1$z1 <- rnorm(50)[vcrpart_1$id]
vcrpart_1$z2 <- rnorm(200)
vcrpart_1$z3 <- factor(sample(1:2, 50, TRUE)[vcrpart_1$id])
vcrpart_1$z4 <- factor(sample(1:2, 200, TRUE))

## simulate response
eta <- 2 * vcrpart_1$treat * (vcrpart_1$z4 == "1")
eta <- eta + rnorm(50)[vcrpart_1$id] + rlogis(200)
vcrpart_1$y <- cut(-eta, c(-Inf, -1, 1, Inf), 1:3,
                   ordered_result = TRUE)


## --------------------------------------------------------- #
## generating 'vcrpart_2'
## --------------------------------------------------------- #

set.seed(1)
vcrpart_2 <- data.frame(x1 = rnorm(100),
                        x2 = rnorm(100),
                        z1 = factor(sample(1:3, 100, TRUE)),
                        z2 = factor(sample(1:3, 100, TRUE)))
vcrpart_2$y <- vcrpart_2$x1 * (vcrpart_2$z1 == "2") +
  2 * vcrpart_2$x1 * (vcrpart_2$z1 == "3")
vcrpart_2$y <- vcrpart_2$y + rnorm(100)

## --------------------------------------------------------- #
## generating 'vcrpart_3'
## --------------------------------------------------------- #

set.seed(1)
vcrpart_3 <- data.frame(x1 = rnorm(100),
                        z1 = runif(100, -pi/2, pi/2))
vcrpart_3$y <- vcrpart_3$x1 * sin(vcrpart_3$z1) + rnorm(100)

## --------------------------------------------------------- #
## generating 'unemp'
## --------------------------------------------------------- #

## create skeletton
set.seed(1)
unemp <- data.frame(PID = factor(rep(1:50, each = 4)),
                    UNEMP = rep(c(0, 0, 1, 1), 50),
               	    YEAR = rep(2001:2004, 50))

## add partitioning variables
unemp$AGE <- runif(50, 25, 60)[unemp$PID] + unemp$YEAR - 2000
unemp$FISIT <- ordered(sample(1:5, 200, replace = TRUE))
unemp$GENDER <- factor(sample(c("female", "male"), 50, replace = TRUE)[unemp$PID])
unemp$UEREGION <- runif(50, 0.02, 0.1)[unemp$PID]

## simulate response
eta <- 2 * unemp$UNEMP * (unemp$FISIT == "1" | unemp$FISIT == "2")
eta <- eta + rnorm(50)[unemp$PID] + rlogis(200)
unemp$GHQL <- cut(-eta, c(-Inf, -1, 0, 1, Inf), 1:4,
                  ordered_result = TRUE)

vcrpart documentation built on May 17, 2021, 3:01 a.m.