R/rancov.R

In joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

#' Function to extract the estimated covariance matrices for the random effects
#' specified in the model
#'
#' A function to allow the random effects covariance matrix for a particular
#' level of random effects specified in the sub-model to be extracted from the
#' \code{jointmeta1} model fit.
#'
#' @param fitted a \code{jointmeta1.object}
#' @param type a character string indicating what level the random effects
#'   covariance matrix should be returned for.  If the individual level random
#'   effects covariance matrix is required then \code{type = "individual"}. If
#'   the study level random effects covariance matrix is required then
#'   \code{type = "study"}.  Note that if study level random effects are not
#'   included in the model, then attempting to extract them will result in an
#'   error message.
#'
#' @return a matrix of dimensions equal to the number of random effects at the
#'   level specified by the \code{type} parameter.
#'
#' @export
#'
#' @seealso \code{\link{jointmeta1}}, \code{\link{jointmeta1.object}}
#'
#' @examples
#'    #change example data to jointdata object
#'    jointdat2<-tojointdata(longitudinal = simdat2$longitudinal,
#'    survival = simdat2$survival, id = 'id',longoutcome = 'Y',
#'    timevarying = c('time','ltime'),
#'    survtime = 'survtime', cens = 'cens',time = 'time')
#'
#'    #set variables to factors
#'    jointdat2$baseline$study <- as.factor(jointdat2$baseline$study)
#'    jointdat2$baseline$treat <- as.factor(jointdat2$baseline$treat)
#'
#'    #fit multi-study joint model
#'    #note: for demonstration purposes only - max.it restricted to 5
#'    #model would need more iterations to truely converge
#'    onestagefit<-jointmeta1(data = jointdat2, long.formula = Y ~ 1 + time +
#'                            + treat + study, long.rand.ind = c('int', 'time'),
#'                            long.rand.stud = c('treat'),
#'                            sharingstrct = 'randprop',
#'                            surv.formula = Surv(survtime, cens) ~ treat,
#'                            study.name = 'study', strat = TRUE, max.it=5)
#'
#'     #extract the individual level random effects covariance matrix
#'     rancov(onestagefit, type = "individual")
#'
#'     #extract the study level random effects covariance matrix
#'     rancov(onestagefit, type = "study")
#'
#'
rancov<-function(fitted, type=c("individual", "study")) {
  if(class(fitted) != "jointmeta1") {
    stop("Variable fitted should be of class jointmeta1")
  }
  if(missing(type) || !(type %in% c("individual", "study"))) {
    stop("type should be one of \"individual\", \"study\"")
  }
  if(type == "individual") {
    ranef.ind <- fitted$rand_cov$D
    return(ranef.ind)
  }else if(type == "study"){
    if(is.null(fitted$rand_cov$A)) {
      stop("No study level random effects specified in this model")
    }else {
      ranef.stud <- fitted$rand_cov$A
    }
    return(ranef.stud)
  }
}