get_parameter_dims for cmdstanr, docs

R/as_data_frame.R

@@ -31,8 +31,8 @@
 #'     factors. Samples of the full correlation matrix can be extracted
 #'     manually as `rstan::extract(fit$stanfit, pars = "corr_matrix_psi")` if
 #'     necessary.
-#'  * `sigma`\cr Standard deviations of gaussian responses.
-#'  * `corr`\cr Pairwise correlations of multivariate gaussian responses.
+#'  * `sigma`\cr Standard deviations of Gaussian responses.
+#'  * `corr`\cr Pairwise correlations of multivariate Gaussian responses.
 #'  * `phi`\cr Describes various distributional parameters, such as:
 #'    - Dispersion parameter of the Negative Binomial distribution.
 #'    - Shape parameter of the Gamma distribution.
@@ -64,10 +64,10 @@
 #'   should be extracted. Possible options are elements of
 #'   `unique(x$priors$response)`, and the default is this entire vector.
 #'    Ignored if the argument `parameters` is supplied.
-#'   `omega_alpha`, and `omega_psi`. See also [dynamite::get_parameter_types()].
+#'   `omega_alpha`, and `omega_psi`. See also [get_parameter_types()].
 #' @param times \[`double()`]\cr Time point(s) to keep. If `NULL`
 #'   (the default), all time points are kept.
-#' @param groups \[`character()`] Group name(s) to keep. If `NULL`
+#' @param groups \[`character()`]\cr Group name(s) to keep. If `NULL`
 #'   (the default), all groups are kept.
 #' @param summary \[`logical(1)`]\cr If `TRUE`, returns posterior
 #'   mean, standard deviation, and posterior quantiles (as defined by the
@@ -82,7 +82,7 @@
 #' @param ... Ignored.
 #' @return A `tibble` containing either samples or summary statistics of the
 #'   model parameters in a long format. For a wide format, see
-#'   [dynamite::as_draws()].
+#'   [as_draws.dynamitefit()].
 #' @examples
 #' data.table::setDTthreads(1) # For CRAN
 #' as.data.frame(

R/as_data_table.R

@@ -1,7 +1,7 @@
 #' Extract Samples From a `dynamitefit` Object as a Data Table
 #'
 #' Provides a `data.table` representation of the posterior samples of the model
-#' parameters. See [dynamite::as.data.frame.dynamitefit()] for details.
+#' parameters. See [as.data.frame.dynamitefit()] for details.
 #'
 #' @export
 #' @export as.data.table

R/as_draws.R

@@ -1,19 +1,18 @@
 #' Convert `dynamite` Output to `draws_df` Format
 #'
-#' Converts the output from a [dynamite::dynamite()] call to a
+#' Converts the output from a [dynamite()] call to a
 #' `draws_df` format of the \pkg{posterior} package, enabling the use
 #' of diagnostics and plotting methods of \pkg{posterior} and \pkg{bayesplot}
 #' packages. Note that this function returns variables in a wide format,
-#' whereas [dynamite::as.data.frame()] uses the long format.
+#' whereas [as.data.frame.dynamitefit()] uses the long format.
 #'
 #' You can use the arguments `parameters`, `responses` and `types` to extract
 #' only a subset of the model parameters (i.e., only certain types of
 #' parameters related to a certain response variable).
 #'
-#' See potential values for the types argument in
-#' [dynamite::as.data.frame.dynamitefit()] and
-#' [dynamite::get_parameter_names()] for potential values for `parameters`
-#' argument.
+#' See potential values for the types argument in [as.data.frame.dynamitefit()]
+#' and [get_parameter_names()] for potential values for `parameters` argument.
+#'
 #' @export
 #' @family output
 #' @aliases as_draws_df

R/ci.R

@@ -1,4 +1,4 @@
-#' Credible Intervals for Dynamite Model Parameters
+#' Credible Intervals for \pkg{dynamite} Model Parameters
 #'
 #' Extracts credible intervals from `dynamitefit` object.
 #'

R/coef.R

@@ -1,4 +1,4 @@
-#' Extract Regression Coefficients of a Dynamite Model
+#' Extract Regression Coefficients of a \pkg{dynamite} Model
 #'
 #' Extracts either time-varying or time-invariant parameters of the model.
 #'

R/deprecated.R

@@ -1,4 +1,4 @@
-#' Deprecated Functions in the dynamite Package
+#' Deprecated Functions in the \pkg{dynamite} Package
 #'
 #' These functions are provided for compatibility with older versions of the
 #' package. They will eventually be completely removed.
@@ -11,7 +11,7 @@
 #' plot_lambdas(x,  ...)
 #' plot_psis(x, ...)
 #' @return A `ggplot` object.
-#' @seealso See [dynamite::plot.dynamitefit()] for documentation of the
+#' @seealso See [plot.dynamitefit()] for documentation of the
 #'   parameters of these functions
 #' @export  plot_betas plot_deltas plot_nus plot_lambdas plot_psis
 #' @aliases plot_betas plot_deltas plot_nus plot_lambdas plot_psis

R/deterministic.R

@@ -195,7 +195,7 @@ assign_lags_init <- function(data, idx, ro, lhs, rhs, offset = 1L) {
 #' Evaluate Definitions of Deterministic Channels
 #'
 #' @inheritParams parse_data
-#' @param dformulas \[list()]\cr The return object of [dynamite::parse_lags()].
+#' @param dformulas \[list()]\cr The return object of [parse_lags()].
 #' @noRd
 evaluate_deterministic <- function(dformulas, data, group_var, time_var) {
   fixed <- as.integer(attr(dformulas$all, "max_lag"))

R/dynamice.R

@@ -2,7 +2,7 @@
 #'
 #' Applies multiple imputation using [mice::mice()] to the supplied `data`
 #' and fits a dynamic multivariate panel model to each imputed data set using
-#' [dynamite::dynamite()]. Posterior samples from each imputation run are
+#' [dynamite()]. Posterior samples from each imputation run are
 #' combined. When using wide format imputation, the long format `data` is
 #' automatically converted to a wide format before imputation to preserve the
 #' longitudinal structure, and then converted back to long format for
@@ -19,11 +19,12 @@
 #'   kept in the return object? The default is `FALSE`. If `TRUE`, the
 #'   imputations will be included in the `imputed` field in the return object
 #'   that is otherwise `NULL`.
-#' @param stan_csv_dir \[`character(1)`] A directory path to output the
+#' @param stan_csv_dir \[`character(1)`]\cr A directory path to output the
 #'   Stan .csv files when `backend` is `"cmdstanr"`. The files are saved here
 #'   via `$save_output_files()` to avoid garbage collection between sampling
 #'   runs with different imputed datasets.
 #' @export
+#'
 dynamice <- function(dformula, data, time, group = NULL,
                      priors = NULL, backend = "rstan",
                      verbose = TRUE, verbose_stan = FALSE,
@@ -150,9 +151,7 @@ dynamice <- function(dformula, data, time, group = NULL,
     stanfit <- rstan::sflist2stanfit(sf)
   } else {
     stanfit <- cmdstanr::as_cmdstan_fit(filenames, check_diagnostics = FALSE)
-    #stanfit@stanmodel <- methods::new("stanmodel", model_code = tmp$model_code)
   }
-  # TODO does this work in this case?
   n_draws <- ifelse_(is.null(stanfit), 0L, get_ndraws(stanfit))
   # TODO return object? How is this going to work with update?
   structure(

R/dynamite-package.R

@@ -1,16 +1,17 @@
-#' The `dynamite` package.
+#' The \pkg{dynamite} Package
 #'
 #' @description Easy-to-use and efficient interface for Bayesian inference of
 #' complex panel data consisting of multiple individuals with multiple
-#' measurements over time. Supports several observational distributions,
-#' time-varying effects and realistic counterfactual predictions which take into
-#' account the dynamic structure of the model.
+#' measurements over time using dynamic multivariate panel models.
+#' Supports several observational distributions, time-varying effects and
+#' realistic counterfactual predictions which take into account the dynamic
+#' structure of the model.
 #'
 #' # See Also
 #'
 #' * The package vignettes
-#' * [dynamite::dynamiteformula()] for information on defining models.
-#' * [dynamite::dynamite()] for information on fitting models.
+#' * [dynamiteformula()] for information on defining models.
+#' * [dynamite()] for information on fitting models.
 #' * <https://github.com/ropensci/dynamite/issues/> to submit a bug report
 #'   or a feature request.
 #'
@@ -47,12 +48,12 @@
 #'   recovered.
 "_PACKAGE"
 
-#' Simulated Data of Gaussian Responses
+#' Simulated Data of a Gaussian Response
 #'
-#' Simulated data containing gaussian response variables with two covariates.
-#' The dataset was generated from a model with time-varying effects of
-#' covariate x and the lagged value of the response variable, time-varying
-#' intercept, and time-invariant effect of covariate z. The time-varying
+#' Simulated data containing a Gaussian response variable `y` with two
+#' covariates. The dataset was generated from a model with time-varying effects
+#' of covariate `x` and the lagged value of the response variable, time-varying
+#' intercept, and time-invariant effect of covariate `z`. The time-varying
 #' coefficients vary according to a spline with 20 degrees of freedom.
 #'
 #' @family examples
@@ -68,7 +69,7 @@
 #' }
 "gaussian_example"
 
-#' Model Fit for the Simulated Data of Gaussian Responses
+#' Model Fit for the Simulated Data of a Gaussian Response
 #'
 #' A `dynamitefit` object obtained by running `dynamite` on the
 #' `gaussian_example` dataset as

R/dynamite.R

@@ -98,6 +98,7 @@
 #'   * `priors`\cr Data frame containing the used priors.
 #'   * `backend`\cr Either `"rstan"` or `"cmdstanr"` indicating which
 #'     package was used in sampling.
+#'   * `permutation`\cr Randomized permutation of the posterior draws.
 #'   * `call`\cr Original function call as an object of class `call`.
 #'
 #' @srrstats {G2.9} Potential loss of information is reported by `dynamite`.
@@ -132,8 +133,8 @@
 #'   based on Stan, the  scalability of the package depends directly on the
 #'   scalability of Stan.
 #' @references
-#' Santtu Tikka and Jouni Helske (2023). `dynamite`: An \R Package for Dynamic
-#' Multivariate Panel Models. arXiv preprint,
+#' Santtu Tikka and Jouni Helske (2023). \pkg{dynamite}: An \R Package for
+#' Dynamic Multivariate Panel Models. arXiv preprint,
 #' <https://arxiv.org/abs/2302.01607>.
 #'
 #' Jouni Helske and Santtu Tikka (2022). Estimating Causal Effects
@@ -496,8 +497,6 @@ dynamite_sampling <- function(sampling, backend, model_code, model,
       out <- with(e, {
         do.call(model$sample, args)
       })
-      #out <- rstan::read_stan_csv(sampling_out$output_files())
-      #out@stanmodel <- methods::new("stanmodel", model_code = model_code)
     }
   }
   out
@@ -700,7 +699,7 @@ remove_redundant_parameters <- function(stan_input, backend,
   dots
 }
 
-#' Access the Model Formula of a Dynamite Model
+#' Access the Model Formula of a \pkg{dynamite} Model
 #'
 #' The `formula` method returns the model definition as a quoted expression.
 #'

R/dynamiteformula.R

@@ -1,4 +1,4 @@
-#' Model formula for \pkg{dynamite}
+#' Model Formula for \pkg{dynamite}
 #'
 #' Defines a new observational or a new auxiliary channel for the model using
 #' standard \R formula syntax. Formulas of individual response variables can be
@@ -12,7 +12,7 @@
 #'
 #' * Categorical: `categorical` (with a softmax link using the first category
 #'   as reference). See the documentation of the `categorical_logit_glm` in the
-#'   Stan function reference manual (https://mc-stan.org/users/documentation/).
+#'   Stan function reference manual <https://mc-stan.org/users/documentation/>.
 #' * Multinomial: `multinomial` (softmax link, first category is reference).
 #' * Gaussian: `gaussian` (identity link, parameterized using mean and standard
 #'   deviation).
@@ -37,7 +37,7 @@
 #' combined with `+`. For example a formula
 #' `obs(y ~ lag(x), family = "gaussian") + obs(x ~ z, family = "poisson")`
 #' defines a model with two channels;
-#' first we declare that `y` is a gaussian variable depending on a previous
+#' first we declare that `y` is a Gaussian variable depending on a previous
 #' value of `x` (`lag(x)`), and then we add a second channel declaring `x` as
 #' Poisson distributed depending on some exogenous variable `z`
 #' (for which we do not define any distribution).
@@ -106,7 +106,7 @@
 #' `random(~1)` leads to a model where in addition to the common intercept,
 #' each individual/group has their own intercept with zero-mean normal prior and
 #' unknown standard deviation analogously with the typical mixed models. An
-#' additional model component [dynamite::random_spec()] can be used to define
+#' additional model component [random_spec()] can be used to define
 #' whether the random effects are allowed to correlate within and across
 #' channels and whether to use centered or noncentered parameterization for
 #' the random effects.

R/fitted.R

@@ -1,9 +1,9 @@
-#' Extract Fitted Values of a Dynamite Model
+#' Extract Fitted Values of a \pkg{dynamite} Model
 #'
 #' Fitted values for a `dynamitefit` object, i.e.,
 #' \eqn{E(y_t | newdata, \theta)} where \eqn{\theta} contains all the
-#' model parameters. See also
-#' [dynamite::predict.dynamitefit()] for multi-step predictions.
+#' model parameters. See also [predict.dynamitefit()] for multi-step
+#' predictions.
 #'
 #' @export
 #' @family prediction
@@ -55,7 +55,7 @@
 #'       ggplot2::geom_line(ggplot2::aes(y = LakeHuron), colour = "tomato") +
 #'       ggplot2::theme_bw()
 #'   }
-#'  }
+#' }
 #' }
 #'
 fitted.dynamitefit <- function(object, newdata = NULL, n_draws = NULL, thin = 1,

R/getters.R

@@ -1,12 +1,12 @@
-#' Get Prior Definitions of a Dynamite Model
+#' Get Prior Definitions of a \pkg{dynamite} Model
 #'
-#' Extracts the priors used in the dynamite model as a data frame. You
+#' Extracts the priors used in the `dynamite` model as a data frame. You
 #' can then alter the priors by changing the contents of the `prior` column and
 #' supplying this data frame to `dynamite` function using the argument
 #' `priors`. See vignettes for details.
 #'
 #' @note Only the `prior` column of the output should be altered when defining
-#' the user-defined priors for the `dynamite`.
+#' the user-defined priors for `dynamite`.
 #'
 #' @export
 #' @family fitting
@@ -48,7 +48,7 @@ get_priors.dynamitefit <- function(x, ...) {
   x$priors
 }
 
-#' Extract the Stan Code of the Dynamite Model
+#' Extract the Stan Code of the \pkg{dynamite} Model
 #'
 #' Returns the Stan code of the model. Mostly useful for debugging or for
 #' building a customized version of the model.
@@ -156,7 +156,7 @@ get_code_ <- function(x, blocks = NULL) {
   paste_rows(out, .parse = FALSE)
 }
 
-#' Extract the Model Data of the Dynamite Model
+#' Extract the Model Data of the \pkg{dynamite} Model
 #'
 #' Returns the input data to the Stan model. Mostly useful for debugging.
 #'
@@ -206,11 +206,11 @@ get_data.dynamitefit <- function(x, ...) {
   out$stan_input$sampling_vars
 }
 
-#' Get Parameter Dimensions of the Dynamite Model
+#' Get Parameter Dimensions of the \pkg{dynamite} Model
 #'
 #' Extracts the names and dimensions of all parameters used in the
-#' `dynamite` model. See also [dynamite::get_parameter_types()] and
-#' [dynamite::get_parameter_names()]. The returned dimensions match those of
+#' `dynamite` model. See also [get_parameter_types()] and
+#' [get_parameter_names()]. The returned dimensions match those of
 #' the `stanfit` element of the `dynamitefit` object. When applied to
 #' `dynamiteformula` objects, the model is compiled and sampled for 1 iteration
 #' to get the parameter dimensions.
@@ -267,6 +267,17 @@ get_parameter_dims.dynamitefit <- function(x, ...) {
     !is.null(x$stanfit),
     "No Stan model fit is available."
   )
+  if (x$backend == "cmdstanr") {
+    return(
+      get_parameter_dims.dynamiteformula(
+        x = eval(formula(x)),
+        data = x$data,
+        time = x$time_var,
+        group = x$group_var,
+        ...
+      )
+    )
+  }
   pars_text <- get_code(x, blocks = "parameters")
   pars <- get_parameters(pars_text)
   # TODO no inits
@@ -298,10 +309,10 @@ get_parameters <- function(x) {
   vapply(par_matches, "[[", character(1L), 2L)
 }
 
-#' Get Parameter Types of the Dynamite Model
+#' Get Parameter Types of the \pkg{dynamite} Model
 #'
 #' Extracts all parameter types of used in the `dynamitefit` object. See
-#' [dynamite::as.data.frame.dynamitefit()] for explanations of different types.
+#' [as.data.frame.dynamitefit()] for explanations of different types.
 #'
 #' @param x \[`dynamitefit`]\cr The model fit object.
 #' @param ... Ignored.
@@ -323,7 +334,7 @@ get_parameter_types.dynamitefit <- function(x, ...) {
   unique(d$type)
 }
 
-#' Get Parameter Names of the Dynamite Model
+#' Get Parameter Names of the \pkg{dynamite} Model
 #'
 #' Extracts all parameter names of used in the `dynamitefit` object.
 #'
@@ -336,7 +347,7 @@ get_parameter_types.dynamitefit <- function(x, ...) {
 #'
 #' @param x \[`dynamitefit`]\cr The model fit object.
 #' @param types \[`character()`]\cr Extract only names of parameter of a
-#'   certain type. See [dynamite::get_parameter_types()].
+#'   certain type. See [get_parameter_types()].
 #' @param ... Ignored.
 #' @return A `character` vector with parameter names of the input model.
 #' @export

R/lags.R

@@ -1,4 +1,4 @@
-#' Add Lagged Responses as Predictors to Each Channel of a Dynamite Model
+#' Add Lagged Responses as Predictors to Each Channel of a \pkg{dynamite} Model
 #'
 #' Adds the lagged value of the response of each channel specified via
 #' [dynamiteformula()] as a predictor to each channel. The added predictors

R/latent_factor.R

@@ -1,6 +1,6 @@
-#' Define a Common Latent Factor for the Dynamite Model.
+#' Define a Common Latent Factor for the \pkg{dynamite} Model.
 #'
-#' This function can be used as part of [dynamite::dynamiteformula()] to define
+#' This function can be used as part of a [dynamiteformula()] to define
 #' a common latent factor component. The latent factor is modeled as a spline
 #' similarly as a time-varying intercept, but instead of having equal effect on
 #' each group, there is an additional loading variable for each group so that