ARpMMEC.est {ARpLMEC}R Documentation

Autoregressive Censored Mixed Effects Models

Description

This functino fits left, right or intervalar censored mixed-effects linear model, with autoregressive errors of order p, using the EM algorithm. It returns estimates, standard errors and prediction of future observations.

Usage

ARpMMEC.est(
  y,
  x,
  z,
  tt,
  cc,
  nj,
  Arp = 1,
  beta0 = NULL,
  sigma0 = NULL,
  D0 = NULL,
  pi0 = NULL,
  typeModel = "L",
  cens.type = "left",
  LI = NULL,
  LS = NULL,
  MaxIter = 200,
  error = 1e-04,
  Prev = FALSE,
  step = NULL,
  isubj = NULL,
  xpre = NULL,
  zpre = NULL
)

Arguments

y

Vector 1 x n of censored responses, where n is the sum of the number of observations of each individual

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.

cc

Vector of censoring indicators of length n, where n is the total of observations. For each observation: 0 if non-censored, 1 if censored.

nj

Vector 1 x m with the number of observations for each subject, where m is the total number of individuals.

Arp

Order of the autoregressive process. Must be a positive integer value. To consider a model uncorrelated use UNC.

beta0

Initial values for the vector of fixed effects. If it is not indicated it will be provided automatically. Default is NULL

sigma0

Initial values for sigma. If it is not indicated it will be provided automatically. Default is NULL

D0

Initial values for the covariance matrix for the random effects. If it is not indicated it will be provided automatically. Default is NULL

pi0

Initial values for the vector for autoregressive coefficients pi's. If it is not indicated it will be provided automatically. Default is NULL

typeModel

L for linear model and NL for nolinear model. Default is L

cens.type

left for left censoring, right for right censoring and interval for intervalar censoring. Default is left

LI

Vector censoring lower limit indicator of length n. For each observation: 0 if non-censored, -inf if censored. It is only indicated for when cens.type is both. Default is NULL

LS

Vector censoring upper limit indicator of length n. For each observation: 0 if non-censored, inf if censored.It is only indicated for when cens.type is both. Default is NULL

MaxIter

The maximum number of iterations of the EM algorithm. Default is 200

error

The convergence maximum error. Default is 0.0001

Prev

Indicator of the prediction process. Default is FALSE

step

Number of steps for prediction. Default is NULL

isubj

Vector indicator of subject included in the prediction process. Default is NULL

xpre

Design matrix of the fixed effects to be predicted. Default is NULL.

zpre

Design matrix of the random effects to be predicted. Default is NULL.

Value

returns list of class “ARpMMEC”:

FixEffect

Data frame with: estimate, standars erros and confidence intervals of the fixed effects.

Sigma2

Data frame with: estimate, standars erros and confidence intervals of the variance of the white noise process.

Phi

Data frame with: estimate, standars erros and confidence intervals of the autoregressive parameters.

RnEffect

Data frame with: estimate, standars erros and confidence intervals of the random effects.

Est

Vector of parameters estimate (fixed Effects, sigma2, phi, random effects).

SE

Vector of the standard errors of (fixed Effects, sigma2, phi, random effects).

loglik

Log-likelihood value.

AIC

Akaike information criterion.

BIC

Bayesian information criterion.

AICc

Corrected Akaike information criterion.

iter

Number of iterations until convergence.

MI

Information matrix

Prev

Predicted values (if xpre and zpre is not NULL).

time

Processing time.

Examples

## Not run: 
 p.cens   = 0.1
 m           = 50
 D = matrix(c(0.049,0.001,0.001,0.002),2,2)
 sigma2 = 0.30
 phi    = c(0.48,-0.2)
 beta   = c(1,2,1)
 nj=rep(6,50)
 tt=rep(seq(1:6),50)
 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,p.cens)
 attach(data, warn.conflicts = F)
 Arp    = 2

 teste1=ARpMMEC.est(y_cc,x,z,tt,cc,nj,Arp,MaxIter = 10)

 xx=matrix(runif(6*length(beta),-1,1),6,length(beta))
 zz=matrix(runif(6*dim(D)[1],-1,1),6,dim(D)[1])
 isubj=c(1,4,5)
 teste2=ARpMMEC.est(y_cc,x,z,tt,cc,nj,Arp,MaxIter=10,Prev=TRUE,step=2,isubj=isubj,xpre=xx,zpre=zz)
 teste2$Prev

## End(Not run)

[Package ARpLMEC version 1.1 Index]