ARpMMEC.sim: Generating Censored Autoregressive Dataset with Mixed...

In ARpLMEC: Censored Mixed-Effects Models with Different Correlation Structures

Generating Censored Autoregressive Dataset with Mixed Effects, for normal distribution.

Description

This function simulates a censored response variable with autoregressive errors of order p, with mixed effect and a established censoring rate. This function returns the censoring vector and censored response vector.

Usage

ARpMMEC.sim(
  m,
  x = NULL,
  z = NULL,
  tt = NULL,
  nj,
  beta,
  sigmae,
  D,
  phi,
  struc = "ARp",
  order = 1,
  typeModel = "Normal",
  p.cens = NULL,
  n.cens = NULL,
  cens.type = "left",
  nu = NULL
)

Arguments

m	Number of individuals
x	Design matrix of the fixed effects of order n x s, corresponding to vector of fixed effects.
z	Design matrix of the random effects of ordern x b, corresponding to vector of random effects.
tt	Vector 1 x n with the time the measurements were made, where n is the total number of measurements for all individuals.
nj	Vector 1 x m with the number of observations for each subject, where m is the total number of individuals.
beta	Vector of values fixed effects.
sigmae	It's the value for sigma.
D	Covariance Matrix for the random effects.
phi	Vector of length Arp, of values for autoregressive parameters.
struc	Correlation structure. This must be one of UNC,ARp,DEC,SYM or DEC(AR).
order	Order of the autoregressive process. Must be a positive integer value.
typeModel	Normal for Normal distribution and Student for t-Student distribution. Default is Normal
p.cens	Censoring percentage for the process. Default is NULL
n.cens	Censoring level for the process. Default is NULL
cens.type	left for left censoring, right for right censoring and interval for intervalar censoring. Default is left
nu	degrees of freedom for t-Student distibution (nu > 0, maybe non-integer).

Value

returns list:

cc	Vector of censoring indicators.
y_cc	Vector of responses censoring.

Examples

## Not run: 
p.cens   = 0.1
m           = 10
D = matrix(c(0.049,0.001,0.001,0.002),2,2)
sigma2 = 0.30
phi    = 0.6
beta   = c(1,2,1)
nj=rep(4,10)
tt=rep(1:4,length(nj))
x<-matrix(runif(sum(nj)*length(beta),-1,1),sum(nj),length(beta))
z<-matrix(runif(sum(nj)*dim(D)[1],-1,1),sum(nj),dim(D)[1])
data=ARpMMEC.sim(m,x,z,tt,nj,beta,sigma2,D,phi,struc="ARp",typeModel="Normal",p.cens=p.cens)
 y<-data$y_cc
 cc<-data$cc

## End(Not run)

ARpLMEC documentation built on June 27, 2022, 1:06 a.m.