| Type: | Package |
| Title: | Censored Mixed-Effects Models with Different Correlation Structures |
| Version: | 2.4.1 |
| Date: | 2022-06-02 |
| Author: | Rommy C. Olivari, Kelin Zhong, Aldo M. Garay and Victor H. Lachos |
| Maintainer: | Rommy C. Olivari <rco1@de.ufpe.br> |
| Description: | Left, right or interval censored mixed-effects linear model with autoregressive errors of order p or DEC correlation structure using the type-EM algorithm. The error distribution can be Normal or t-Student. It provides the parameter estimates, the standard errors and prediction of future observations (available only for the normal case). Olivari et all (2021) <doi:10.1080/10543406.2020.1852246>. |
| Depends: | R (≥ 2.14) |
| Imports: | numDeriv, stats, MASS, mnormt, tcltk, expm, relliptical, TruncatedNormal, LaplacesDemon |
| NeedsCompilation: | no |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 7.2.0 |
| Encoding: | UTF-8 |
| Packaged: | 2022-06-02 19:09:43 UTC; Rommy |
| Repository: | CRAN |
| Date/Publication: | 2022-06-26 23:50:02 UTC |
Censored Mixed-Effects Models with Autoregressive Correlation Structure and DEC for Normal and t-Student Errors
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,
struc = "UNC",
order = 1,
initial = NULL,
nu.fixed = TRUE,
typeModel = "Normal",
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 |
x |
Design matrix of the fixed effects of order |
z |
Design matrix of the random effects of order |
tt |
Vector |
cc |
Vector of censoring indicators of length |
nj |
Vector |
struc |
|
order |
Order of the autoregressive process. Must be a positive integer value. |
initial |
List with the initial values in the next orden: betas,sigma2,alphas,phi and nu. If it is not indicated it will be provided automatically. Default is |
nu.fixed |
Logical. Should estimate the parameter "nu" for the t-student distribution?. If is False indicates the value in the list of initial values. Default is |
typeModel |
|
cens.type |
|
LI |
Vector censoring lower limit indicator of length |
LS |
Vector censoring upper limit indicator of length |
MaxIter |
The maximum number of iterations of the EM algorithm. Default is |
error |
The convergence maximum error. Default is |
Prev |
Indicator of the prediction process. Available at the moment only for the |
step |
Number of steps for prediction. Default is |
isubj |
Vector indicator of subject included in the prediction process. Default is |
xpre |
Design matrix of the fixed effects to be predicted. Default is |
zpre |
Design matrix of the random effects to be predicted. Default is |
Value
returns list of class “ARpMMEC”:
FixEffect |
Data frame with: estimate, standar errors and confidence intervals of the fixed effects. |
Sigma2 |
Data frame with: estimate, standar errors and confidence intervals of the variance of the white noise process. |
Phi |
Data frame with: estimate, standar errors and confidence intervals of the autoregressive parameters. |
RandEffect |
Data frame with: estimate, standar errors and confidence intervals of the random effects. |
nu |
the parameter "nu" for the t-student distribution |
Est |
Vector of parameters estimate (fixed Effects, sigma2, phi, random effects). |
SE |
Vector of the standard errors of (fixed Effects, sigma2, phi, random effects). |
Residual |
Vector of the marginal residuals. |
loglik |
Log-likelihood value. |
AIC |
Akaike information criterion. |
BIC |
Bayesian information criterion. |
AICc |
Corrected Akaike information criterion. |
iter |
Number of iterations until convergence. |
Yfit |
Vector "y" fitted |
MI |
Information matrix |
Prev |
Predicted values (if xpre and zpre is not |
time |
Processing time. |
others |
The first and second moments of the random effect and vector Y |
References
Olivari, R. C., Garay, A. M., Lachos, V. H., & Matos, L. A. (2021). Mixed-effects models for censored data with autoregressive errors. Journal of Biopharmaceutical Statistics, 31(3), 273-294. doi:10.1080/10543406.2020.1852246
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)
teste1=ARpMMEC.est(data$y_cc,x,z,tt,data$cc,nj,struc="ARp",order=1,typeModel="Normal",MaxIter = 2)
teste2=ARpMMEC.est(data$y_cc,x,z,tt,data$cc,nj,struc="ARp",order=1,typeModel="Student",MaxIter = 2)
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)
teste3=ARpMMEC.est(data$y_cc,x,z,tt,data$cc,nj,struc="ARp",order=1,typeModel="Normal",
MaxIter = 2,Prev=TRUE,step=2,isubj=isubj,xpre=xx,zpre=zz)
teste3$Prev
## End(Not run)
Generating Censored Autoregressive Dataset with Mixed Effects, for normal distribution.
Description
This function simulates a censored response variable with autoregressive errors of order p, with mixed effect and a established censoring rate. This function returns the censoring vector and censored response vector.
Usage
ARpMMEC.sim(
m,
x = NULL,
z = NULL,
tt = NULL,
nj,
beta,
sigmae,
D,
phi,
struc = "ARp",
order = 1,
typeModel = "Normal",
p.cens = NULL,
n.cens = NULL,
cens.type = "left",
nu = NULL
)
Arguments
m |
Number of individuals |
x |
Design matrix of the fixed effects of order |
z |
Design matrix of the random effects of order |
tt |
Vector |
nj |
Vector |
beta |
Vector of values fixed effects. |
sigmae |
It's the value for sigma. |
D |
Covariance Matrix for the random effects. |
phi |
Vector of length |
struc |
Correlation structure. This must be one of |
order |
Order of the autoregressive process. Must be a positive integer value. |
typeModel |
|
p.cens |
Censoring percentage for the process. Default is |
n.cens |
Censoring level for the process. Default is |
cens.type |
|
nu |
degrees of freedom for t-Student distibution (nu > 0, maybe non-integer). |
Value
returns list:
cc |
Vector of censoring indicators. |
y_cc |
Vector of responses censoring. |
Examples
## Not run:
p.cens = 0.1
m = 10
D = matrix(c(0.049,0.001,0.001,0.002),2,2)
sigma2 = 0.30
phi = 0.6
beta = c(1,2,1)
nj=rep(4,10)
tt=rep(1:4,length(nj))
x<-matrix(runif(sum(nj)*length(beta),-1,1),sum(nj),length(beta))
z<-matrix(runif(sum(nj)*dim(D)[1],-1,1),sum(nj),dim(D)[1])
data=ARpMMEC.sim(m,x,z,tt,nj,beta,sigma2,D,phi,struc="ARp",typeModel="Normal",p.cens=p.cens)
y<-data$y_cc
cc<-data$cc
## End(Not run)