getJMCM: Extract or Get Generalized Components from a Fitted Joint...

In jmcm: Joint Mean-Covariance Models using 'Armadillo' and S4

Description

Extract (or "get") "components" - in a generalized sense - from a fitted joint mean covariance model from an object of class "jmcmMod".

Usage

getJMCM(object, name, sub.num)

## S3 method for class 'jmcmMod'
getJMCM(object, name = c("m", "Y", "X", "Z", "W", "D", "T",
  "Sigma", "mu", "n2loglik", "grad", "hess", "theta", "beta", "lambda", "gamma",
  "loglik", "BIC", "iter", "triple"), sub.num = 0)

Arguments

object	a fitted joint mean covariance model of class "jmcmMod", i.e., typically the result of jmcm().
name	a character vector specifying the name(s) of the "component". When sub.num is not specified or equal to 0, possible values are: "m" a vector of number of measurement for each subject "Y" response vector "X" model matrix for mean structure "Z" model matrix for covariance structure (the diagonal matrix) "W" model matrix for covariance structure (the lower triangular matrix) "theta" parameter estimates of joint mean covariance model "beta" parameter estimates for mean structure model "lambda" parameter estimates for covariace structure (the diagonal matrix) "gamma" parameter estimates for covariance structure (the lower triangular matrix) "loglik" log-likelihood, except for a constant "BIC" Bayesian information criterion "iter" number of iterations until convergence "triple" (p, d, q) When sub.num is specified, possible values are: "m" number of measurements for subject i "Y" response vector for subject i "X" model matrix of subject i for mean structure "Z" model matrix of subject i for covariance structure (the diagonal matrix) "W" model matrix of subject i for covariance structure (the lower triangular matrix) "D" the estimated diagonal matrix for subject i "T" the estimated lower triangular matrix for subject i "Sigma" the estimated covariance matrix for subject i "mu" the estimated mean for subject i "n2loglik" the estimated -2l(theta) "grad" the estimated gradient "hess" the estimated Hessian matrix
sub.num	refer to i's subject

Methods (by class)

• jmcmMod: Extract or Get Generalized Components from a Fitted Joint Mean Covariance Model

Examples

fit.mcd <- jmcm(I(sqrt(cd4)) | id | time ~ 1 | 1, data = aids,
  triple = c(8, 1, 3), cov.method = 'mcd')

beta <- getJMCM(fit.mcd, "beta")
BIC  <- getJMCM(fit.mcd, "BIC")
Di   <- getJMCM(fit.mcd, "D", 10)

jmcm documentation built on Jan. 16, 2021, 5:32 p.m.