ARpMMEC.sim {ARpLMEC} | R 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",
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 |
z |
Design matrix of the random effects of order |
tt |
Vector |
nj |
Vector |
beta |
Vector of values fixed effects. |
sigmae |
It's the value for sigma. |
D |
Covariance Matrix for the random effects. |
phi |
Vector of length |
struc |
Correlation structure. This must be one of |
order |
Order of the autoregressive process. Must be a positive integer value. |
typeModel |
|
p.cens |
Censoring percentage for the process. Default is |
n.cens |
Censoring level for the process. Default is |
cens.type |
|
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)