Merge pull request #326 from fweber144/mbox_mathrm

Docs: Replace `\mbox{}` by `\mathrm{}`

R/methods.R

@@ -42,16 +42,16 @@
 #' @param ... Arguments passed to [project()] if `object` is not already an
 #'   object returned by [project()].
 #'
-#' @return Let \eqn{S_{\mbox{prj}}}{S_prj} denote the number of (possibly
+#' @return Let \eqn{S_{\mathrm{prj}}}{S_prj} denote the number of (possibly
 #'   clustered) projected posterior draws (short: the number of projected draws)
 #'   and \eqn{N} the number of observations. Then, if the prediction is done for
 #'   one submodel only (i.e., `length(nterms) == 1 || !is.null(solution_terms)`
 #'   in the call to [project()]):
 #'   * [proj_linpred()] returns a `list` with elements `pred` (predictions) and
-#'   `lpd` (log predictive densities). Both elements are \eqn{S_{\mbox{prj}}
+#'   `lpd` (log predictive densities). Both elements are \eqn{S_{\mathrm{prj}}
 #'   \times N}{S_prj x N} matrices.
-#'   * [proj_predict()] returns an \eqn{S_{\mbox{prj}} \times N}{S_prj x N}
-#'   matrix of predictions where \eqn{S_{\mbox{prj}}}{S_prj} denotes
+#'   * [proj_predict()] returns an \eqn{S_{\mathrm{prj}} \times N}{S_prj x N}
+#'   matrix of predictions where \eqn{S_{\mathrm{prj}}}{S_prj} denotes
 #'   `nresample_clusters` in case of clustered projection.
 #'
 #'   If the prediction is done for more than one submodel, the output from above
@@ -689,18 +689,18 @@ print.vsel <- function(x, ...) {
 #'   the lower or upper bound (depending on argument `type`) of the
 #'   normal-approximation confidence interval (with nominal coverage `1 -
 #'   alpha`; see argument `alpha` of [summary.vsel()]) for \eqn{U_k -
-#'   U_{\mbox{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
-#'   submodel's true utility and \eqn{U_{\mbox{base}}}{U_base} denoting the
+#'   U_{\mathrm{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
+#'   submodel's true utility and \eqn{U_{\mathrm{base}}}{U_base} denoting the
 #'   baseline model's true utility) falls above (or is equal to)
-#'   \deqn{\texttt{pct} \cdot (u_0 - u_{\mbox{base}})}{pct * (u_0 - u_base)}
+#'   \deqn{\texttt{pct} \cdot (u_0 - u_{\mathrm{base}})}{pct * (u_0 - u_base)}
 #'   where \eqn{u_0} denotes the null model's estimated utility and
-#'   \eqn{u_{\mbox{base}}}{u_base} the baseline model's estimated utility. The
+#'   \eqn{u_{\mathrm{base}}}{u_base} the baseline model's estimated utility. The
 #'   baseline is either the reference model or the best submodel found (see
 #'   argument `baseline` of [summary.vsel()]).
 #'
 #'   For example, `alpha = 0.32`, `pct = 0`, and `type = "upper"` means that we
 #'   select the smallest model size for which the upper bound of the 68%
-#'   confidence interval for \eqn{U_k - U_{\mbox{base}}}{U_k - U_base} exceeds
+#'   confidence interval for \eqn{U_k - U_{\mathrm{base}}}{U_k - U_base} exceeds
 #'   (or is equal to) zero, that is, for which the submodel's utility estimate
 #'   is at most one standard error smaller than the baseline model's utility
 #'   estimate.
@@ -1056,8 +1056,8 @@ get_subparams.gamm4 <- function(x, ...) {
 #'   uses `"rstanarm"` if the reference model fit is of an unknown class).
 #' @param ... Currently ignored.
 #'
-#' @return An \eqn{S_{\mbox{prj}} \times Q}{S_prj x Q} matrix of projected
-#'   draws, with \eqn{S_{\mbox{prj}}}{S_prj} denoting the number of projected
+#' @return An \eqn{S_{\mathrm{prj}} \times Q}{S_prj x Q} matrix of projected
+#'   draws, with \eqn{S_{\mathrm{prj}}}{S_prj} denoting the number of projected
 #'   draws and \eqn{Q} the number of parameters.
 #'
 #' @examples

R/misc.R

@@ -181,24 +181,24 @@ bootstrap <- function(x, fun = mean, B = 2000,
 #   subsampled (without replacement).
 #
 # @return Let \eqn{y} denote the response (vector), \eqn{N} the number of
-#   observations, and \eqn{S_{\mbox{prj}}}{S_prj} the number of projected draws
-#   (= either `nclusters` or `ndraws`, depending on which one is used). Then the
-#   return value is a list with elements:
+#   observations, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of projected
+#   draws (= either `nclusters` or `ndraws`, depending on which one is used).
+#   Then the return value is a list with elements:
 #
-#   * `mu`: An \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix of expected
+#   * `mu`: An \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix of expected
 #   values for \eqn{y} for each draw/cluster.
-#   * `var`: An \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix of predictive
+#   * `var`: An \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix of predictive
 #   variances for \eqn{y} for each draw/cluster which are needed for projecting
 #   the dispersion parameter (the predictive variances are NA for those families
 #   that do not have a dispersion parameter).
-#   * `dis`: A vector of length \eqn{S_{\mbox{prj}}}{S_prj} containing the
+#   * `dis`: A vector of length \eqn{S_{\mathrm{prj}}}{S_prj} containing the
 #   reference model's dispersion parameter value for each draw/cluster (NA for
 #   those families that do not have a dispersion parameter).
-#   * `weights`: A vector of length \eqn{S_{\mbox{prj}}}{S_prj} containing the
+#   * `weights`: A vector of length \eqn{S_{\mathrm{prj}}}{S_prj} containing the
 #   weights for the draws/clusters.
 #   * `cl`: Cluster assignment for each posterior draw, that is, a vector that
 #   has length equal to the number of posterior draws and each value is an
-#   integer between 1 and \eqn{S_{\mbox{prj}}}{S_prj}.
+#   integer between 1 and \eqn{S_{\mathrm{prj}}}{S_prj}.
 .get_refdist <- function(refmodel, ndraws = NULL, nclusters = NULL,
                          thinning = TRUE) {
   # Number of draws in the reference model:

R/refmodel.R

@@ -101,8 +101,8 @@
 #' Arguments `ref_predfun`, `proj_predfun`, and `div_minimizer` may be `NULL`
 #' for using an internal default. Otherwise, let \eqn{N} denote the number of
 #' observations (in case of CV, these may be reduced to each fold),
-#' \eqn{S_{\mbox{ref}}}{S_ref} the number of posterior draws for the reference
-#' model's parameters, and \eqn{S_{\mbox{prj}}}{S_prj} the number of (possibly
+#' \eqn{S_{\mathrm{ref}}}{S_ref} the number of posterior draws for the reference
+#' model's parameters, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of (possibly
 #' clustered) parameter draws for projection (short: the number of projected
 #' draws). Then the functions supplied to these arguments need to have the
 #' following prototypes:
@@ -114,7 +114,7 @@
 #'     typically stored in `fit`) or data for new observations (at least in the
 #'     form of a `data.frame`).
 #' * `proj_predfun`: `proj_predfun(fits, newdata)` where:
-#'     + `fits` accepts a `list` of length \eqn{S_{\mbox{prj}}}{S_prj}
+#'     + `fits` accepts a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj}
 #'     containing this number of submodel fits. This `list` is the same as that
 #'     returned by [project()] in its output element `submodl` (which in turn is
 #'     the same as the return value of `div_minimizer`, except if [project()]
@@ -125,26 +125,26 @@
 #' * `div_minimizer` does not need to have a specific prototype, but it needs to
 #' be able to be called with the following arguments:
 #'     + `formula` accepts either a standard [`formula`] with a single response
-#'     (if \eqn{S_{\mbox{prj}} = 1}{S_prj = 1}) or a [`formula`] with
-#'     \eqn{S_{\mbox{prj}} > 1}{S_prj > 1} response variables [cbind()]-ed on
+#'     (if \eqn{S_{\mathrm{prj}} = 1}{S_prj = 1}) or a [`formula`] with
+#'     \eqn{S_{\mathrm{prj}} > 1}{S_prj > 1} response variables [cbind()]-ed on
 #'     the left-hand side in which case the projection has to be performed for
 #'     each of the response variables separately.
 #'     + `data` accepts a `data.frame` to be used for the projection.
 #'     + `family` accepts a [`family`] object.
 #'     + `weights` accepts either observation weights (at least in the form of a
 #'     numeric vector) or `NULL` (for using a vector of ones as weights).
-#'     + `projpred_var` accepts an \eqn{N \times S_{\mbox{prj}}}{N x S_prj}
+#'     + `projpred_var` accepts an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj}
 #'     matrix of predictive variances (necessary for \pkg{projpred}'s internal
 #'     GLM fitter).
 #'     + `projpred_regul` accepts a single numeric value as supplied to argument
 #'     `regul` of [project()], for example.
 #'     + `...` accepts further arguments specified by the user.
 #'
 #' The return value of these functions needs to be:
-#' * `ref_predfun`: an \eqn{N \times S_{\mbox{ref}}}{N x S_ref} matrix.
-#' * `proj_predfun`: an \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix.
-#' * `div_minimizer`: a `list` of length \eqn{S_{\mbox{prj}}}{S_prj} containing
-#' this number of submodel fits.
+#' * `ref_predfun`: an \eqn{N \times S_{\mathrm{ref}}}{N x S_ref} matrix.
+#' * `proj_predfun`: an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix.
+#' * `div_minimizer`: a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj}
+#' containing this number of submodel fits.
 #'
 #' # Argument `extract_model_data`
 #'