#' ---
#' title: "`r title`"
#' author: "`r author`"
#' date: "`r Sys.Date()`"
#' ---
#+ setup, include=FALSE
require("knitr")
require("pander")
opts_chunk$set(fig.path = figure.dir, comment = NA)
# options(width = 200)
panderOptions("table.split.table", Inf)
#'
#' Total number of unique independent units in the input data:
{{prettyNum(nuniqueIDs, big.mark = ",", scientific = FALSE)}}
#'
#' Total number of person-time observations in the input data:
{{prettyNum(nobs, big.mark = ",", scientific = FALSE)}}
#'
#' Total number of unique time-points in the input data:
{{prettyNum(nuniquets, big.mark = ",", scientific = FALSE)}}
#'
#' ## Model Performance Based on MSE for Holdout / Validation Data
#'
#+ warning=FALSE, message=FALSE
plotMSEs(modelfit, K = K, interactive = TRUE)
#'
#'
#'
#'
#+ warning=FALSE, message=FALSE, results='asis'
tabMSEonly <- modelfit$get_best_MSEs(K = K)
pander::set.caption("Top MSEs (for Holdout / Validation Data).")
pander::pander(data.frame(model = names(tabMSEonly), MSEs = tabMSEonly, row.names = NULL))
#'
#'
#'
#'
#+ warning=FALSE, message=FALSE, results='asis'
tab <- modelfit$get_best_MSE_table(K = K)
pander::set.caption("Best Performing Models (Based on MSE for Holdout / Validation Data).")
pander::pander(tab)
#'
#'
#'
#'
#'
#' ## Summary of Model Grids
#'
#+ warning=FALSE, message=FALSE
grids <- modelfit$get_modelfits_grid()
for (grid in grids) {
print(grid)
cat("\n\n\n");
}
#'
#'
#'
#'
#+ results='asis'
panderOptions('knitr.auto.asis', FALSE)
grids <- modelfit$get_modelfits_grid()
for (grid in grids) {
if (is.data.frame(grid) || is.data.table(grid))
grid <- grid[ , names(grid)[!(names(grid) %in% c("glob_params", "xgb_fit", "fit", "params"))], with = FALSE]
pander::pander(grid) # , caption = "Grid Details"
}
panderOptions('knitr.auto.asis', TRUE)
#'
#'
#'
#'
#'
#' ## Top Performing Models
#'
#+ echo=FALSE, warning=FALSE, message=FALSE, results='asis'
panderOptions('knitr.auto.asis', FALSE)
if (!skip.modelfits) {
models.object <- modelfit$get_best_models(K = K)
for (model_idx in seq_along(models.object)) {
cat("\n\n");
cat("###")
cat('<a name=',paste0("jump",model_idx),'>', "Summaries for Model " %+% names(models.object)[model_idx], '</a> ')
cat("\n\n");
if (!is.null(models.object[[model_idx]])) {
print_tables(models.object[[model_idx]])
} else {
cat("*no modeling objects found*")
}
cat("\n\n"); cat(" ")
}
}
panderOptions('knitr.auto.asis', TRUE)
#'
#'
#'
#'
#'
#' ## Model Stack Summaries
#'
#+ echo=FALSE
if (!skip.modelfits) {
print(modelfit, model_stats = TRUE, all_fits = print_all_fits)
# print(modelfit, model_stats = TRUE)
# models <- modelfit$getfit
# single_models <- models$modelfits_all[[1]]
# for (single_model in models$modelfits_all) {
# # print(models, only.coefs = only.coefs)
# print(single_model)
# # res <- utils::capture.output(single_models)
# # pander(print(paste(res, collapse = '\n')))
# }
}
# panderOptions('knitr.auto.asis', TRUE)
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