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

View source: R/ARpMMEC.sim.R

ARpMMEC.simR Documentation

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",
  typeModel = "Normal",
  p.cens = 0,
  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).

typeModel

Normal for Normal distribution and Student for t-Student distribution. Default is Normal

p.cens

Censoring level for the process. Default is 0

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)
 y<-data$y_cc
 cc<-data$cc

## End(Not run)

ARpLMEC documentation built on April 27, 2022, 9:06 a.m.

Related to ARpMMEC.sim in ARpLMEC...