#' Results from independently fit cyclical model demonstration for package vignette
#'
#' an object of type "ihmm"
#'
#' @docType data
#'
#' @usage data(fit_indep_cyclical)
#'
#' @format A list, where each element is a list for a single time series with components
#' \describe{
#' \item {z.save} {list of estimated hidden states for each time series at each iteration}
#' \item {K.save} {list of estimated number of hidden states for each time series at each iteration}
#' \item {ymar} {matrix with len.imp rows of imputed values for MAR data}
#' \item {ylod} {matrix with len.imp rows of imputed values for data below LOD}
#' \item {beta.save} {list of posterior estimates of beta_k, state-specific regression coefficients in PSBP}
#' \item {gamma.save} {list of posterior estimates of gamma_ik, state-specific subject-specific regression coefficients in PSBP}
#' \item {mu.save} {list of posterior estimates of mu_k, state-specific means}
#' \item {hamming} {posterior hamming distance between true and estimated states, if z.true is given}
#' \item {mu.mse} {mean squared error for estimated state-specific means, if mu.true is given}
#' \item {mu.sse} {sum of squared errors for estimated state-specific means, if mu.true is given}
#' \item {mar.mse} {mean squared error of MAR imputations, if ycomplete is given}
#' \item {lod.mse} {mean squared error of imputations below LOD, if ycomplete is given}
#' \item {mar.sse} {sum of squared errors of MAR imputations, if ycomplete is given}
#' \item {lod.sse} {sum of squared errors of imputations below LOD, if ycomplete is given}
#' \item {mismat} {list, each element is a matrix indicating types of missing data for each time series, 0 = complete, 1 = MAR, 2 = below LOD}
#' \item {ycomplete} {list of complete data}
#' \item {MH.arate} {MH acceptance rate for lower triangular elements}
#' \item {MH.lamrate} {MH acceptance rate for diagonal elements}
#' }
#'
#' @examples
#' data(fit_indep_cyclical)
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