SimulatedDataGenerator.CumulativeProbit: Simulating a longitudinal ordinal data with HSD correlation...

In BayesRGMM: Bayesian Robust Generalized Mixed Models for Longitudinal Data

Simulating a longitudinal ordinal data with HSD correlation structures.

Description

This function is used to simulate data for the cumulative probit mixed-effects model with HSD correlation structures.

Usage

SimulatedDataGenerator.CumulativeProbit(
  Num.of.Obs,
  Num.of.TimePoints,
  Num.of.Cats,
  Fixed.Effs,
  Random.Effs,
  DesignMat,
  Missing,
  HSD.DesignMat.para
)

Arguments

Num.of.Obs	the number of subjects will be simulated.
Num.of.TimePoints	the maximum number of time points among all subjects.
Num.of.Cats	the number of categories.
Fixed.Effs	a vector of regression coefficients.
Random.Effs	a list of covariance matrix and the degree of freedom, e.g., list(Sigma = 0.5*diag(1), df=3).
DesignMat	a design matrix.
Missing	a list of the missing mechanism of observations, 0: data is complete, 1: missing complete at random, 2: missing at random related to responses , and 3: 2: missing at random related to covariates and the corresponding regression coefficients (weights) on the previous observed values either responses or covariates, e.g., Missing = list(Missing.Mechanism = 3, RegCoefs = c(0.4, 1.2, -2.1)).
HSD.DesignMat.para	the list of parameters in HSD correlation structure, e.g., HSD.DesignMat.para = list(HSD.para = HSD.para, DesignMat = w).

Value

a list containing the following components:

sim.data

The simulated response variables y, covariates x's, and subject identifcation id.

beta.true

The given values of fixed effects.

classes

The intervals of classes.

HSD.para

The given values of parameters in HSD model.

Examples

## Not run: 
library(BayesRGMM)
rm(list=ls(all=TRUE))

Fixed.Effs = c(-0.1, 0.1, -0.1)  
P = length(Fixed.Effs) 
q = 1 #number of random effects
T = 7 #time points
N = 100 #number of subjects
Num.of.Cats = 3 #number of categories 
num.of.iter = 1000 #number of iterations

HSD.para = c(-0.9, -0.6) #the parameters in HSD model
a = length(HSD.para)
w = array(runif(T*T*a), c(T, T, a)) #design matrix in HSD model
 
for(time.diff in 1:a)
w[, , time.diff] = 1*(as.matrix(dist(1:T, 1:T, method="manhattan")) 
==time.diff)


x = array(0, c(T, P, N))
for(i in 1:N){
    x[, 1, i] = 1:T
    x[, 2, i] = rbinom(1, 1, 0.5)
    x[, 3, i] = x[, 1, i]*x[, 2, i]
}

DesignMat = x

#MAR
CPREM.sim.data = SimulatedDataGenerator.CumulativeProbit(
 Num.of.Obs = N, Num.of.TimePoints = T, Num.of.Cats = Num.of.Cats, 
 Fixed.Effs = Fixed.Effs, Random.Effs = list(Sigma = 0.5*diag(1), df=3), 
 DesignMat = DesignMat, Missing = list(Missing.Mechanism = 2, 
 MissingRegCoefs=c(-0.7, -0.2, -0.1)), 
 HSD.DesignMat.para = list(HSD.para = HSD.para, DesignMat = w))


print(table(CPREM.sim.data$sim.data$y))
print(CPREM.sim.data$classes)

## End(Not run)

BayesRGMM documentation built on May 10, 2022, 5:12 p.m.