Type: | Package |
Title: | Model Selection in Multivariate Longitudinal Data Analysis |
Version: | 1.0 |
Date: | 2020-03-13 |
Author: | Kuo-Jung Lee |
Maintainer: | Kuo-Jung Lee <kuojunglee@mail.ncku.edu.tw> |
Description: | An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection. |
URL: | https://github.com/kuojunglee/ |
Depends: | R(≥ 3.5.0) |
License: | GPL-2 |
Imports: | Rcpp (≥ 1.0.1), MASS |
Suggests: | testthat |
LinkingTo: | Rcpp, RcppArmadillo, RcppDist |
NeedsCompilation: | yes |
Packaged: | 2020-03-20 14:30:29 UTC; kjlee |
Repository: | CRAN |
Date/Publication: | 2020-03-20 15:10:08 UTC |
Model estimation for multivariate longitudinal models.
Description
Using MCMC procedure to generate posterior samples and provide AIC, BIC, DIC, MPL, MSPE, and predicted values.
Usage
MLModelSelectionMCMC(Num.of.iterations, list.Data, list.InitialValues, list.HyperPara,
list.UpdatePara, list.TuningPara)
Arguments
Num.of.iterations |
Number of iterations. |
list.Data |
List of data set containing response |
list.InitialValues |
List of initial values for parameters. |
list.HyperPara |
List of given hyperparameters in priors. |
list.UpdatePara |
Determine which parameter will be updated. |
list.TuningPara |
Provide turning parameters in proposal distributions. |
Details
We set the subject i
(i=1, \ldots, N
) has K
continuous responses at each time point t
(t=1, \ldots, n_i
). Assume that the measurement times are common across subjects, but not necessarily equally-spaced. Let {y}_{it} = (y_{it1}, \ldots, y_{itK})
denote the response vector containing K
continuous responses for i
th subject at time t
along with a p\times 1
vectof of covariates, {x}_{it} = (x_{it1}, \ldots, x_{itp})
. An efficient Gibbs sampling algorithm is developed for model estimation in the multivariate longitudinal model given by
y_{i1k} = {x}'_{it}{\beta}_k + e_{i1k}, t=1;
y_{itk} = {x}'_{it}{\beta}_k + \sum_{g=1}^K\sum_{j=1}^{t-1} \phi_{itj, kg} (y_{ijg}-x'_{ij}{\beta}_g)+ e_{itk}, t\geq 2,
where {\beta}_k = (\beta_{k1}, \ldots, \beta_{kp})'
is a vector of regression coefficients of length p
, \phi_{itj, kg}
is a generalized autoregressive parameter (GARP) to explain the serial dependence of responses across time. Moreover,
\phi_{itj, kg} = \alpha_{kg} \mathbf{1}\{|t-j|=1\} ,\; \log(\sigma_{itk}) = \lambda_{k0} + \lambda_{k1} h_{it}, \; \log\left(\frac{\omega_{ilm}}{\pi-\omega_{ilm}}\right) = \nu_l + \nu_m.
The priors for the parameters in the model given by
{\beta} \sim \mathcal{N}(0, \sigma_\beta^2 I);
{\lambda}_k \sim \mathcal{N}(0, \sigma_\lambda^2 I);
{\nu}_k \sim \mathcal{N}(0, \sigma_\nu^2 I), \quad k=1, \ldots, K,
where \sigma_\beta^2
, \sigma_\lambda^2
, and \sigma_\nu^2
are prespecified values. For k, g = 1, \ldots, K
and m=1, \ldots, a
, we further assume
\alpha_{kgm} \sim \delta_{kgm} \mathcal{N}(0, \sigma^2_\delta) + (1-\delta_{kgm})\eta_0,
where \sigma^2_\delta
is prespecified value and \eta_0
is the point mass at 0.
Value
Lists of posterior samples, parameters estimates, AIC, BIC, DIC, MPL, MSPE, and predicted values are returned
Note
We'll provide the reference for details of the model and the algorithm for performing model estimation whenever the manuscript is accepted.
Author(s)
Kuo-Jung Lee
References
Keunbaik Lee et al. (2015) Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions. Biometrics. 75-86, 2020. doi: 10.1111/biom.13113.
Examples
library(MASS)
library(MLModelSelection)
AR.Order = 6 #denote \phi_{itj, kg} = \alpha_{kg} \mathbf{1}{|t-j|=1}
ISD.Model = 1 #denote \log(\sigma_{itk}) = \lambda_{k0} + \lambda_{k1} h_{it}
data(SimulatedData)
N = dim(SimulatedData$Y)[1] # the number of subjects
T = dim(SimulatedData$Y)[2] # time points
K = dim(SimulatedData$Y)[3] # the number of attributes
P = dim(SimulatedData$X)[3] # the number of covariates
M = AR.Order # the demension of alpha
nlamb = ISD.Model + 1 # the dimension of lambda
Data = list(Y = SimulatedData$Y, X = SimulatedData$X,
TimePointsAvailable = SimulatedData$TimePointsAvailable,
AR.Order = AR.Order, ISD.Model = ISD.Model)
beta.ini = matrix(rnorm(P*K), P, K)
delta.ini = array(rbinom(K*K*M, 1, 0.1), c(K, K, M))
alpha.ini = array(runif(K*K*M, -1, 1), c(K, K, M))
lambda.ini = matrix(rnorm(nlamb*K), K, nlamb, byrow=T)
nu.ini = rnorm(K)
InitialValues = list(beta = beta.ini, delta = delta.ini, alpha = alpha.ini,
lambda = lambda.ini, nu = nu.ini)
# Hyperparameters in priors
sigma2.beta = 1
sigma2.alpha = 10
sigma2.lambda = 0.01
sigma2.nu = 0.01
# Whehter the parameter will be updated
UpdateBeta = TRUE
UpdateDelta = TRUE
UpdateAlpha = TRUE
UpdateLambda = TRUE
UpdateNu = TRUE
HyperPara = list(sigma2.beta = sigma2.beta, sigma2.alpha=sigma2.alpha,
sigma2.lambda=sigma2.lambda, sigma2.nu=sigma2.nu)
UpdatePara = list(UpdateBeta = UpdateBeta, UpdateAlpha = UpdateAlpha, UpdateDelta = UpdateDelta,
UpdateLambda = UpdateLambda, UpdateNu = UpdateNu)
# Tuning parameters in proposal distribution within MCMC
TuningPara = list(TuningAlpha = 0.01, TuningLambda = 0.005, TuningNu = 0.005)
num.of.iter = 100
start.time <- Sys.time()
PosteriorSamplesEstimation = MLModelSelectionMCMC(num.of.iter, Data, InitialValues,
HyperPara, UpdatePara, TuningPara)
end.time <- Sys.time()
cat("Estimate of beta\n")
print(PosteriorSamplesEstimation$PosteriorEstimates$beta.mean)
Simulated data
Description
A simulated multivariate longitudinal data for demonstration.
Usage
data("SimulatedData")
Format
A list
consists of Y
the observations 100 subjects in 3 attributes along 10 time points,
X
the design matrix with 6 covariate including the intercept, TimePointsAvailable
the avilable time points for each subject.
Y
The response variables.
X
The design matrix.
TimePointsAvailable
The available time points for each subject.
Examples
library(MLModelSelection)
data(SimulatedData)
SimulatedData = data(SimulatedData)