| Type: | Package |
| Title: | Adjust Longitudinal Regression Models Using Bayesian Methodology |
| Version: | 0.1.0 |
| Date: | 2017-07-18 |
| Author: | Edwin Javier Castillo Carreño, Edilberto Cepeda Cuervo |
| Maintainer: | Edwin Javier Castillo Carreño <edjcastilloca@unal.edu.co> |
| Description: | Adjusts longitudinal regression models using Bayesian methodology for covariance structures of composite symmetry (SC), autoregressive ones of order 1 AR (1) and autoregressive moving average of order (1,1) ARMA (1,1). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Depends: | R(≥ 3.1.0), LearnBayes, mvtnorm, MASS |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-07-18 21:21:17 UTC; edwin |
| Repository: | CRAN |
| Date/Publication: | 2017-07-25 21:13:13 UTC |
Dental distance
Description
It reports the distance in millimeters from the center of the pituitary to the pteromaxillary fissure. The subjects were 16 children and 11 girls. Data were taken every two years from 8 years and ended at age 14.
Usage
Dental
Format
A data Frame with 98 rows and 5 variables:
- gencode
1 for girls, 0 for boy
- id
Number of the individual
- distance
Distance from the center of the pituitary gland to the pterygomaxillary fissure
- age
Child's age at which measurement was taken
- gender
Gender of the child
Source
https://faculty.biostat.ucla.edu/robweiss/filedepot_download/87/524
bloques ar 1
Description
Build a block diagonal matrix with structure AR(1)
Usage
bloques(s, r, t, n)
Arguments
s |
Numerical value indicating global standard deviation of the matrix |
r |
Numerical value indicating correlation of individuals |
t |
Numerical value indicating number of times when observations are repeated |
n |
Numerical value indicating number of individuals |
Value
A diagonal block matrix with structure AR(1)
References
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
bloques(2,.5,10,2)
bloques arma (1,1)
Description
Build a block diagonal matrix with structure ARMA(1,1)
Usage
bloques2(s, r, g, t, n)
Arguments
s |
Numerical value indicating global standard deviation of the matrix |
r |
Numerical value indicating the first parameter rho correlation of individuals |
g |
Numerical value indicating the second parameter phi correlation of individuals |
t |
Numerical value indicating number of times when observations are repeated |
n |
Numerical value indicating number of individuals |
Value
A diagonal block matrix with structure ARMA(1,1)
References
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
bloques2(2,.5,.8,10,2)
bloques3 compound symmetry
Description
Build a block diagonal matrix with compound symmetry structure
Usage
bloques3(s, r, t, n)
Arguments
s |
Numerical value indicating global standard deviation of the matrix |
r |
Numerical value indicating correlation of individuals |
t |
Numerical value indicating number of times when observations are repeated |
n |
Numerical value indicating number of individuals |
Value
A diagonal block matrix with compound symmetry structure
References
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
bloques3(2,.5,10,2)
mhar1
Description
Run Bayesian estimation of a balanced longitudinal model with AR(1) structure
Usage
mhar1(Data, Matriz, individuos, tiempos, betai, rhoi, beta1i, beta2i,
iteraciones, burn)
Arguments
Data |
A vector with the observations of the response variable |
Matriz |
The model design matrix |
individuos |
A numerical value indicating the number of individuals in the study |
tiempos |
A numerical value indicating the number of times observations were repeated |
betai |
A vector with the initial values of the vector of regressors |
rhoi |
A numerical value with the initial value of the correlation |
beta1i |
A numerical value with the shape parameter of a beta apriori distribution of rho |
beta2i |
A numerical value with the scaling parameter of a beta apriori distribution of rho |
iteraciones |
A numerical value with the number of iterations that will be applied the algorithm MCMC |
burn |
Number of iterations that are discarded from the chain |
Value
A dataframe with the mean, median and standard deviation of each parameter, A graph with the histograms and chains for the parameters that make up the variance matrix, as well as the selection criteria AIC, BIC and DIC
References
Gamerman, D. 1997. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing, 7, 57-68
Cepeda, C and Gamerman, D. 2004. Bayesian modeling of joint regressions for the mean and covariance matrix. Biometrical journal, 46, 430-440.
Cepeda, C and Nuñez, A. 2007. Bayesian joint modelling of the mean and covariance structures for normal longitudinal data. SORT. 31, 181-200.
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
attach(Dental)
Y=as.vector(distance)
X=as.matrix(cbind(1,age))
mhar1(Y,X,27,4,c(1,1),0.5,1,1,500,50)
mharma11
Description
Run Bayesian estimation of a balanced longitudinal model with ARMA(1) structure
Usage
mharma11(Data, Matriz, individuos, tiempos, betai, rhoi, gammai, beta1i, beta2i,
beta1j, beta2j, iteraciones, burn)
Arguments
Data |
A vector with the observations of the response variable |
Matriz |
The model design matrix |
individuos |
A numerical value indicating the number of individuals in the study |
tiempos |
A numerical value indicating the number of times observations were repeated |
betai |
A vector with the initial values of the vector of regressors |
rhoi |
A numerical value with the initial value of the correlation for rho |
gammai |
A numerical value with the initial value of the correlation for phi |
beta1i |
A numerical value with the shape parameter of a beta apriori distribution of rho |
beta2i |
A numerical value with the scaling parameter of a beta apriori distribution of rho |
beta1j |
A numerical value with the shape parameter of a beta apriori distribution of phi |
beta2j |
A numerical value with the scaling parameter of a beta apriori distribution of phi |
iteraciones |
A numerical value with the number of iterations that will be applied the algorithm MCMC |
burn |
Number of iterations that are discarded from the chain |
Value
A dataframe with the mean, median and standard deviation of each parameter, A graph with the histograms and chains for the parameters that make up the variance matrix, as well as the selection criteria AIC, BIC and DIC
References
Gamerman, D. 1997. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing, 7, 57-68
Cepeda, C and Gamerman, D. 2004. Bayesian modeling of joint regressions for the mean and covariance matrix. Biometrical journal, 46, 430-440.
Cepeda, C and Nuñez, A. 2007. Bayesian joint modelling of the mean and covariance structures for normal longitudinal data. SORT. 31, 181-200.
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
attach(Dental)
Y=as.vector(distance)
X=as.matrix(cbind(1,age))
mharma11(Y,X,27,4,c(1,1),0.5,0.5,1,1,1,1,500,50)
mhsc
Description
Run Bayesian estimation of a balanced longitudinal model with compound symmetry structure
Usage
mhsc(Data, Matriz, individuos, tiempos, betai, rhoi, beta1i, beta2i,
iteraciones, burn)
Arguments
Data |
A vector with the observations of the response variable |
Matriz |
The model design matrix |
individuos |
A numerical value indicating the number of individuals in the study |
tiempos |
A numerical value indicating the number of times observations were repeated |
betai |
A vector with the initial values of the vector of regressors |
rhoi |
A numerical value with the initial value of the correlation |
beta1i |
A numerical value with the shape parameter of a beta apriori distribution of rho |
beta2i |
A numerical value with the scaling parameter of a beta apriori distribution of rho |
iteraciones |
numerical value with the number of iterations that will be applied the algorithm MCMC |
burn |
Number of iterations that are discarded from the chain |
Value
A dataframe with the mean, median and standard deviation of each parameter, A graph with the histograms and chains for the parameters that make up the variance matrix, as well as the selection criteria AIC, BIC and DIC
References
Gamerman, D. 1997. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing, 7, 57-68
Cepeda, C and Gamerman, D. 2004. Bayesian modeling of joint regressions for the mean and covariance matrix. Biometrical journal, 46, 430-440.
Cepeda, C and Nuñez, A. 2007. Bayesian joint modelling of the mean and covariance structures for normal longitudinal data. SORT. 31, 181-200.
Nuñez A. and Zimmerman D. 2001. Modelación de datos longitudinales con estructuras de covarianza no estacionarias: Modelo de coeficientes aleatorios frente a modelos alternativos. Questio. 2001. 25.
Examples
attach(Dental)
Y=as.vector(distance)
X=as.matrix(cbind(1,age))
mhsc(Y,X,27,4,c(1,1),0.5,1,1,500,50)