R/plotConvergence.R

Defines functions plotConvergence

Documented in plotConvergence

#' Plot convergence time series for parameter vectors from an \code{mjoint}
#' object
#'
#' @description Plot convergence time series for parameter vectors from an
#'   \code{mjoint} object.
#'
#' @inheritParams confint.mjoint
#' @param params a string indicating what parameters are to be shown. Options
#'   are \code{params='gamma'} for the time-to-event sub-model covariate
#'   coefficients, including the latent association parameters;
#'   \code{params='beta'} for the longitudinal sub-model fixed effects
#'   coefficients; \code{params='sigma2'} for the residual error variances from
#'   the longitudinal sub-model; \code{params='D'} for the lower triangular
#'   matrix of the variance-covariance matrix of random effects;
#'   \code{params='loglik'} for the log-likelihood.
#' @param discard logical; if \code{TRUE} then the 'burn-in' phase iterations of
#'   the MCEM algorithm are discarded. Default is \code{discard=FALSE}.
#'
#' @references
#'
#' Wei GC, Tanner MA. A Monte Carlo implementation of the EM algorithm and the
#' poor man's data augmentation algorithms. \emph{J Am Stat Assoc.} 1990;
#' \strong{85(411)}: 699-704.
#'
#' @author Graeme L. Hickey (\email{graemeleehickey@@gmail.com})
#' @keywords methods dplot
#' @seealso \code{\link{plot.mjoint}}, \code{\link[graphics]{plot.default}},
#'   \code{\link[graphics]{par}}, \code{\link[graphics]{abline}}.
#'
#' @importFrom graphics par plot
#' @export
plotConvergence <- function(object, params = "gamma", discard = FALSE) {

  if (class(object) != "mjoint") {
    stop("Use only with 'mjoint' model objects.\n")
  }

  his <- object$history
  n.iters <- ncol(his)

  dims <- object$dims
  p <- sum(dims[["p"]])
  q <- dims[["q"]]
  K <- dims[["K"]]
  r <- sum(dims[["r"]])

  # NB: for rows indices of 'his', note number of parameters =
  #     p (beta)
  #     q + K (gamma)
  #     K (sigma2)
  #     r * (r + 1) / 2 (upper D matrix)
  inds <- c(0, cumsum(c(p, q + K, K, r * (r + 1) / 2)))

  if (n.iters == 1) {
    stop("No convergence history found.\n")
  }

  old.par <- par(no.readonly = TRUE)
  nc <- 1

  if (discard) {
    his <- his[, (object$control$burnin + 1):ncol(his), drop = FALSE]
  }

  #--------------------------------------------------------

  # betas
  if (params == "beta") {
    if (p > 3) {
      nc <- ceiling(p / 3)
    }
    par(mfrow = c(nrow = min(p, 3), ncol = nc))
    for (i in 1:p) {
      plot(his[(inds[1] + 1):(inds[2]), , drop = FALSE][i, ], type = "l",
           xlab = "Iteration",
           ylab = rownames(his[(inds[1] + 1):(inds[2]), , drop = FALSE])[i])
    }
  }

  #--------------------------------------------------------

  # gammas
  if (params == "gamma") {
    n.par <- q + K # must always be >= 1
    if (n.par > 3) {
      nc <- ceiling(n.par / 3)
    }
    par(mfrow = c(nrow = min(n.par, 3), ncol = nc))
    for (i in 1:n.par) {
      plot(his[(inds[2] + 1):(inds[3]), , drop = FALSE][i, ], type = "l",
           xlab = "Iteration",
           ylab = rownames(his[(inds[2] + 1):(inds[3]), , drop = FALSE])[i])
    }
  }

  #--------------------------------------------------------

  # sigma2
  if (params == "sigma2") {
    if (K > 3) {
      nc <- ceiling(K / 3)
    }
    par(mfrow = c(nrow = min(K, 3), ncol = nc))
    for (i in 1:K) {
      plot(his[(inds[3] + 1):(inds[4]), , drop = FALSE][i, ], type = "l",
           xlab = "Iteration",
           ylab = rownames(his[(inds[3] + 1):(inds[4]), , drop = FALSE])[i])
    }
  }

  #--------------------------------------------------------

  # D
  if (params == "D") {
    n.par <- r * (r + 1) / 2 # upper triangle only
    if (n.par > 3) {
      nc <- ceiling(n.par / 3)
    }
    par(mfrow = c(nrow = min(n.par, 3), ncol = nc))
    for (i in 1:n.par) {
      plot(his[(inds[4] + 1):(inds[5]), , drop = FALSE][i, ], type = "l",
           xlab = "Iteration",
           ylab = rownames(his[(inds[4] + 1):(inds[5]), , drop = FALSE])[i])
    }
  }

  #--------------------------------------------------------

  # log-likelihood
  if (params == "loglik") {
    par(mfrow = c(1, 1))
    ll <- na.omit(object$ll.hx)
    if (discard) {
      ll <- ll[(object$control$burnin + 1):length(ll)]
    }
    plot(ll, type = "l",
         xlab = "Iteration",
         ylab = "Log-likelihood")
  }

  on.exit(par(old.par))

}

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joineRML documentation built on Jan. 5, 2021, 5:07 p.m.