R: Add model fit criteria to model

add_model_criterion.bgmfit {bsitar}

R Documentation

Add model fit criteria to model

Description

The add_model_criterion() is a wrapper around the brms::add_criterion(). Note that arguments compare and pointwise are relevant only for brms::add_loo whereas arguments summary, robust, and probs ignored except for the brms::bayes_R2().

Usage

## S3 method for class 'bgmfit'
add_model_criterion(
  model,
  criterion = c("loo", "waic"),
  ndraws = NULL,
  draw_ids = NULL,
  compare = TRUE,
  pointwise = FALSE,
  model_names = NULL,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  newdata = NULL,
  resp = NULL,
  cores = 1,
  deriv_model = NULL,
  verbose = FALSE,
  expose_function = FALSE,
  usesavedfuns = NULL,
  clearenvfuns = NULL,
  envir = NULL,
  ...
)

add_model_criterion(model, ...)

Arguments

`model`	An object of class `bgmfit`.
`criterion`	Names of model fit criteria to compute. Currently supported are `"loo"`, `"waic"`, `"kfold"`, `"loo_subsample"`, `"bayes_R2"` (Bayesian R-squared), `"loo_R2"` (LOO-adjusted R-squared), and `"marglik"` (log marginal likelihood).
`ndraws`	A positive integer indicating the number of posterior draws to be used in estimation. If `NULL` (default), all draws are used.
`draw_ids`	An integer indicating the specific posterior draw(s) to be used in estimation (default `NULL`).
`compare`	A flag indicating if the information criteria of the models should be compared to each other via `loo_compare`.
`pointwise`	A flag indicating whether to compute the full log-likelihood matrix at once or separately for each observation. The latter approach is usually considerably slower but requires much less working memory. Accordingly, if one runs into memory issues, `pointwise = TRUE` is the way to go.
`model_names`	If `NULL` (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.
`summary`	A logical indicating whether only the estimate should be computed (`TRUE`, default), or estimate along with SE and CI should be returned (`FALSE`). Setting `summary` as `FALSE` will increase the computation time.
`robust`	A logical to specify the summarize options. If `FALSE` (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If `TRUE`, the median and the median absolute deviation (MAD) are applied instead. Ignored if `summary` is `FALSE`.
`probs`	The percentiles to be computed by the `quantile` function. Only used if `summary` is `TRUE`.
`newdata`	An optional data frame to be used in estimation. If `NULL` (default), the `newdata` is retrieved from the `model`.
`resp`	A character string (default `NULL`) to specify response variable when processing posterior draws for the `univariate_by` and `multivariate` models. See `bsitar()` for details on `univariate_by` and `multivariate` models
`cores`	Number of cores to be used when running the parallel computations (if `future = TRUE`). On non-Windows systems this argument can be set globally via the mc.cores option. For the default `NULL` option, the number of cores are set automatically by calling the `future::availableCores()`. The number of cores used are the maximum number of cores avaialble minus one, i.e., `future::availableCores() - 1`.
`deriv_model`	A logical to specify whether to estimate velocity curve from the derivative function, or the differentiation of the distance curve. The argument `deriv_model` is set to `TRUE` for those functions which need velocity curve such as `growthparameters()` and `plot_curves()`, and `NULL` for functions which explicitly use the distance curve (i.e., fitted values) such as `loo_validation()` and `plot_ppc()`.
`verbose`	An optional argument (logical, default `FALSE`) to indicate whether to print information collected during setting up the object(s).
`expose_function`	An optional logical argument to indicate whether to expose Stan functions (default `FALSE`). Note that if user has already exposed Stan functions during model fit by setting `expose_function = TRUE` in the `bsitar()`, then those exposed functions are saved and can be used during post processing of the posterior draws and therefore `expose_function` is by default set as `FALSE` in all post processing functions except `optimize_model()`. For `optimize_model()`, the default setting is `expose_function = NULL`. The reason is that each optimized model has different Stan function and therefore it need to be re exposed and saved. The `expose_function = NULL` implies that the setting for `expose_function` is taken from the original `model` fit. Note that `expose_function` must be set to `TRUE` when adding `fit criteria` and/or `bayes_R2` during model optimization.
`usesavedfuns`	A logical (default `NULL`) to indicate whether to use the already exposed and saved `Stan` functions. Depending on whether the user have exposed Stan functions within the `bsitar()` call via `expose_functions` argument in the `bsitar()`, the `usesavedfuns` is automatically set to `TRUE` (if `expose_functions = TRUE`) or `FALSE` (if `expose_functions = FALSE`). Therefore, manual setting of `usesavedfuns` as `TRUE`/`FALSE` is rarely needed. This is for internal purposes only and mainly used during the testing of the functions and therefore should not be used by users as it might lead to unreliable estimates.
`clearenvfuns`	A logical to indicate whether to clear the exposed function from the environment (`TRUE`) or not (`FALSE`). If `NULL` (default), then `clearenvfuns` is set as `TRUE` when `usesavedfuns` is `TRUE`, and `FALSE` if `usesavedfuns` is `FALSE`.
`envir`	Environment used for function evaluation. The default is `NULL` which will set `parent.frame()` as default environment. Note that since most of post processing functions are based on brms, the functions needed for evaluation should be in the `.GlobalEnv`. Therefore, it is strongly recommended to set `envir = globalenv()` (or `envir = .GlobalEnv`). This is particularly true for the derivatives such as velocity curve.
`...`	Further arguments passed to `brms::fitted.brmsfit()` and `brms::predict()` functions.

Value

An object of class class bgmfit with fit criteria added.

Author(s)

Satpal Sandhu satpal.sandhu@bristol.ac.uk

Examples



# Fit Bayesian SITAR model 

# To avoid mode estimation which takes time, the Bayesian SITAR model fit to 
# the 'berkeley_exdata' has been saved as an example fit ('berkeley_exfit').
# See 'bsitar' function for details on 'berkeley_exdata' and 'berkeley_exfit'.

model <- berkeley_exfit

model <- add_model_criterion(model, criterion = c("waic"))

[Package bsitar version 0.2.1 Index]