fix some warnings/errors

Pretty much closes #191

Author: jgabry

R/bayesplot-colors.R

@@ -142,7 +142,6 @@ color_scheme_set <- function(scheme = "blue") {
 }
 
 #' @rdname bayesplot-colors
-#' @md
 #' @export
 #' @param i For `color_scheme_get()`, an optional subset of the integers from `1`
 #'   (lightest) to `6` (darkest) indicating which of the colors in the

R/bayesplot-helpers.R

@@ -46,18 +46,22 @@
 #'   the lower and upper bounds (`lb`, `ub`) of the `100*p`\% central interval
 #'   of `x`, as well as the median (if `med=TRUE`).
 #' }
+#'
 #' \subsection{Control appearance of facet strips}{
-#' * `facet_text()` and `facet_bg()` return ggplot2 theme objects that
-#'   can be added to an existing plot (ggplot object) to format the text and the
-#'   background for the facet strips.
+#' * `facet_text()` returns ggplot2 theme objects that can be added to an
+#'    existing plot (ggplot object) to format the text in facet strips.
+#'
+#' * `facet_bg()` can be added to a plot to change the background of the facet strips.
 #' }
+#'
 #' \subsection{Move legend, remove legend, or style the legend text}{
-#' * `legend_move()` and `legend_none()` return a ggplot2 theme object
-#'   that can be added to an existing plot (ggplot object) in order to change the
-#'   position of the legend (`legend_move()`) or remove the legend
-#'   (`legend_none()`). `legend_text()` works much like `facet_text()`,
-#'   except it controls the legend text.
+#' * `legend_move()` and `legend_none()` return a ggplot2 theme object that can
+#'   be added to an existing plot (ggplot object) in order to change the
+#'   position of the legend or remove it.
+#'
+#' * `legend_text()` works much like `facet_text()` but for the legend.
 #' }
+#'
 #' \subsection{Control appearance of \eqn{x}-axis and \eqn{y}-axis features}{
 #' * `xaxis_title()` and `yaxis_title()` return a ggplot2 theme object
 #'   that can be added to an existing plot (ggplot object) in order to toggle or
@@ -73,21 +77,24 @@
 #'   that can be added to an existing plot (ggplot object) to change the
 #'   appearance of the axis tick marks.
 #' }
+#'
 #' \subsection{Customize plot background}{
 #' * `plot_bg()` returns a ggplot2 theme object that can be added to an
-#' existing plot (ggplot object) to format the background of the *entire* plot.
+#'   existing plot (ggplot object) to format the background of the *entire* plot.
+#'
 #' * `panel_bg()` returns a ggplot2 theme object that can be added to an
-#' existing plot (ggplot object) to format the background of the just the
-#' plotting area.
+#'   existing plot (ggplot object) to format the background of the just the
+#'   plotting area.
+#'
 #' * `grid_lines()` returns a ggplot2 theme object that can be added to
-#' an existing plot (ggplot object) to add grid lines to the plot background.
+#'   an existing plot (ggplot object) to add grid lines to the plot background.
 #' }
+#'
 #' \subsection{Superimpose a function on an existing plot}{
-#' * `overlay_function()` is a simple wrapper for
-#'   [ggplot2::stat_function()] but with the `inherit.aes` argument
-#'   fixed to `FALSE`. Fixing `inherit.aes=FALSE` will avoid potential
-#'   errors due to the [ggplot2::aes()]thetic mapping used by certain
-#'   **bayesplot** plotting functions.
+#' * `overlay_function()` is a simple wrapper for [ggplot2::stat_function()] but
+#'   with the `inherit.aes` argument fixed to `FALSE`. Fixing `inherit.aes=FALSE`
+#'   will avoid potential errors due to the [ggplot2::aes()]thetic mapping used by
+#'   certain **bayesplot** plotting functions.
 #' }
 #'
 #' @seealso [theme_default()] for the default ggplot theme used by
@@ -284,7 +291,7 @@ abline_01 <- function(..., na.rm = TRUE) {
 # intervals ---------------------------------------------------------------
 #' @rdname bayesplot-helpers
 #' @export
-#' @param p The probability mass (in `[0,1]``) to include in the interval.
+#' @param p The probability mass (in `[0,1]`) to include in the interval.
 #' @param med Should the median also be included in addition to the lower
 #' and upper bounds of the interval?
 #'

R/bayesplot-package.R

@@ -49,7 +49,6 @@
 #' * __General questions and help__:
 #'   To ask a question about **bayesplot** on the Stan Forums forum please visit
 #'   <https://discourse.mc-stan.org>.
-#' }
 #'
 #' @template seealso-theme
 #' @template seealso-colors

R/bayesplot_grid.R

@@ -23,8 +23,8 @@
 #'   Setting this to `FALSE` will make the returned object smaller but
 #'   these individual plot objects will not be available.
 #'
-#' @return An object of class "bayesplot_grid" (essentially a gtable object from
-#'   [gridExtra::arrangeGrob()]), which has a `plot` method.
+#' @return An object of class `"bayesplot_grid"` (essentially a gtable object
+#'   from [gridExtra::arrangeGrob()]), which has a `plot` method.
 #'
 #' @examples
 #' y <- example_y_data()

R/mcmc-diagnostics-nuts.R

@@ -51,22 +51,26 @@
 #'     lines indicating the mean (solid line) and median (dashed line).
 #'   * Scatterplot of `accept_stat__` vs `lp__`.
 #'   }
+#'
 #'   \item{`mcmc_nuts_divergence()`}{
 #'   Two plots:
 #'   * Violin plots of `lp__|divergent__=1` and `lp__|divergent__=0`.
 #'   * Violin plots of `accept_stat__|divergent__=1` and `accept_stat__|divergent__=0`.
 #'   }
+#'
 #'   \item{`mcmc_nuts_stepsize()`}{
 #'   Two plots:
 #'   * Violin plots of `lp__` by chain ordered by `stepsize__` value.
 #'   * Violin plots of `accept_stat__` by chain ordered by `stepsize__` value.
 #'   }
+#'
 #'   \item{`mcmc_nuts_treedepth()`}{
 #'   Three plots:
 #'   * Violin plots of `lp__` by value of `treedepth__`.
 #'   * Violin plots of `accept_stat__` by value of `treedepth__`.
 #'   * Histogram of `treedepth__`.
 #'   }
+#'
 #'   \item{`mcmc_nuts_energy()`}{
 #'   Overlaid histograms showing `energy__` vs the change in
 #'   `energy__`. See Betancourt (2016) for details.

R/mcmc-diagnostics.R

@@ -19,28 +19,29 @@
 #' @section Plot Descriptions:
 #' \describe{
 #' \item{`mcmc_rhat()`, `mcmc_rhat_hist()`}{
-#' Rhat values as either points or a histogram. Values are colored using
-#' different shades (lighter is better). The chosen thresholds are somewhat
-#' arbitrary, but can be useful guidelines in practice.
-#' * _light_: below 1.05 (good)
-#' * _mid_: between 1.05 and 1.1 (ok)
-#' * _dark_: above 1.1 (too high)
+#'   Rhat values as either points or a histogram. Values are colored using
+#'   different shades (lighter is better). The chosen thresholds are somewhat
+#'   arbitrary, but can be useful guidelines in practice.
+#'   * _light_: below 1.05 (good)
+#'   * _mid_: between 1.05 and 1.1 (ok)
+#'   * _dark_: above 1.1 (too high)
 #' }
+#'
 #' \item{`mcmc_neff()`, `mcmc_neff_hist()`}{
-#' Ratios of effective sample size to total sample size as either points or a
-#' histogram. Values are colored using different shades (lighter is better). The
-#' chosen thresholds are somewhat arbitrary, but can be useful guidelines in
-#' practice.
-#' * _light_: between 0.5 and 1 (high)
-#' * _mid_: between 0.1 and 0.5 (good)
-#' * _dark_: below 0.1 (low)
-#'  }
+#'   Ratios of effective sample size to total sample size as either points or a
+#'   histogram. Values are colored using different shades (lighter is better).
+#'   The chosen thresholds are somewhat arbitrary, but can be useful guidelines
+#'   in practice.
+#'   * _light_: between 0.5 and 1 (high)
+#'   * _mid_: between 0.1 and 0.5 (good)
+#'   * _dark_: below 0.1 (low)
 #' }
+#'
 #' \item{`mcmc_acf()`, `mcmc_acf_bar()`}{
-#' Grid of autocorrelation plots by chain and parameter. The `lags`
-#' argument gives the maximum number of lags at which to calculate the
-#' autocorrelation function. `mcmc_acf()` is a line plot whereas
-#' `mcmc_acf_bar()` is a barplot.
+#'   Grid of autocorrelation plots by chain and parameter. The `lags` argument
+#'   gives the maximum number of lags at which to calculate the autocorrelation
+#'   function. `mcmc_acf()` is a line plot whereas `mcmc_acf_bar()` is a
+#'   barplot.
 #' }
 #'}
 #'

R/mcmc-traces.R

@@ -13,30 +13,29 @@
 #' @template args-facet_args
 #' @param ... Currently ignored.
 #' @param size An optional value to override the default line size
-#'   (`mcmc_trace`) or the default point size
-#'   (`mcmc_trace_highlight`).
-#' @param alpha For `mcmc_trace_highlight`, passed to
+#'   (`mcmc_trace()`) or the default point size
+#'   (`mcmc_trace_highlight()`).
+#' @param alpha For `mcmc_trace_highlight()`, passed to
 #'   [ggplot2::geom_point()] to control the transparency of the points
 #'   for the chains not highlighted.
 #' @param n_warmup An integer; the number of warmup iterations included in
 #'   `x`. The default is `n_warmup = 0`, i.e. to assume no warmup
 #'   iterations are included. If `n_warmup > 0` then the background for
 #'   iterations `1:n_warmup` is shaded gray.
 #' @param iter1 An integer; the iteration number of the first included draw
-#'   (default 0). This can be used to make it more obvious that the warmup
+#'   (default is `0`). This can be used to make it more obvious that the warmup
 #'   iterations have been discarded from the traceplot. It cannot be specified
 #'   if `n_warmup` is also set to a positive value.
 #' @param window An integer vector of length two specifying the limits of a
 #'   range of iterations to display.
-#' @param np For models fit using [NUTS()] (more generally, any
-#'   \href{https://en.wikipedia.org/wiki/Symplectic_integrator}{symplectic
-#'   integrator}), an optional data frame providing NUTS diagnostic
-#'   information. The data frame should be the object returned by
-#'   [nuts_params()] or one with the same structure. If `np` is
-#'   specified then tick marks are added to the bottom of the trace plot
-#'   indicating within which iterations there was a divergence (if there were any).
-#'   See the end of the **Examples** section, below.
-#' @param np_style A call to the `trace_style_np` helper function to
+#' @param np For models fit using [NUTS] (more generally, any
+#'   (https://en.wikipedia.org/wiki/Symplectic_integrator)[symplectic integrator]),
+#'   an optional data frame providing NUTS diagnostic information. The data
+#'   frame should be the object returned by [nuts_params()] or one with the same
+#'   structure. If `np` is specified then tick marks are added to the bottom of
+#'   the trace plot indicating within which iterations there was a divergence
+#'   (if there were any). See the end of the **Examples** section, below.
+#' @param np_style A call to the `trace_style_np()` helper function to
 #'   specify arguments controlling the appearance of tick marks representing
 #'   divergences (if the `np` argument is specified).
 #' @param divergences Deprecated. Use the `np` argument instead.
@@ -45,11 +44,11 @@
 #'
 #' @section Plot Descriptions:
 #' \describe{
-#'   \item{`mcmc_trace`}{
-#'    Standard trace plots of MCMC draws. For models fit using [NUTS()],
+#'   \item{`mcmc_trace()`}{
+#'    Standard trace plots of MCMC draws. For models fit using [NUTS],
 #'    the `np` argument can be used to also show divergences on the trace plot.
 #'   }
-#'   \item{`mcmc_trace_highlight`}{
+#'   \item{`mcmc_trace_highlight()`}{
 #'    Traces are plotted using points rather than lines and the opacity of all
 #'    chains but one (specified by the `highlight` argument) is reduced.
 #'   }
@@ -192,7 +191,7 @@ mcmc_trace <-
 
 #' @rdname MCMC-traces
 #' @export
-#' @param highlight For `mcmc_trace_highlight`, an integer specifying one
+#' @param highlight For `mcmc_trace_highlight()`, an integer specifying one
 #'   of the chains that will be more visible than the others in the plot.
 mcmc_trace_highlight <-
   function(x,
@@ -227,11 +226,10 @@ mcmc_trace_highlight <-
 #' @rdname MCMC-traces
 #' @export
 #' @param div_color,div_size,div_alpha Optional arguments to the
-#'   `trace_style_np` helper function that are eventually passed to
-#'   [ggplot2::geom_rug()] if the `np` argument is also
-#'   specified. They control the color, size, and transparency specifications
-#'   for showing divergences in the plot. The default values are displayed in
-#'   the \strong{Usage} section above.
+#'   `trace_style_np()` helper function that are eventually passed to
+#'   [ggplot2::geom_rug()] if the `np` argument is also specified. They control
+#'   the color, size, and transparency specifications for showing divergences in
+#'   the plot. The default values are displayed in the **Usage** section above.
 trace_style_np <-
   function(div_color = "red",
            div_size = 0.25,
@@ -391,16 +389,17 @@ chain_colors <- function(n) {
 }
 
 
-# Add divergences to trace plot using geom_rug
-#
-# @param np User's 'np' argument, if specified.
-# @param np_style User's 'np_style' argument, if specified.
-# @param n_iter Number of iterations in the trace plot (to check against number
-#   of iterations provided in 'np').
-# @param n_chain Number of chains in the trace plot (to check against number
-#   of chains provided in 'np').
-# @return Object returned by geom_rug.
-#
+#' Add divergences to trace plot using geom_rug
+#'
+#' @noRd
+#' @param np User's `np` argument, if specified.
+#' @param np_style User's `np_style` argument, if specified.
+#' @param n_iter Number of iterations in the trace plot (to check against number
+#'   of iterations provided in `np`).
+#' @param n_chain Number of chains in the trace plot (to check against number
+#'   of chains provided in `np`).
+#' @return Object returned by `ggplot2::geom_rug()`.
+#'
 #' @importFrom dplyr summarise group_by select
 divergence_rug <- function(np, np_style, n_iter, n_chain) {
   if (is.data.frame(np)) {

R/ppc-overview.R

@@ -51,10 +51,9 @@
 #' Chapter 6 of Gelman et. al. (2013).
 #' }
 #'
-#' @section PPC plotting functions:
-#'
-#' The plotting functions for posterior predictive checking in this package are
-#' organized into several categories, each with its own documentation:
+#' @section PPC plotting functions: The plotting functions for posterior
+#'   predictive checking in this package are organized into several categories,
+#'   each with its own documentation:
 #' * [__Distributions__][PPC-distributions]: Histograms, kernel density
 #'   estimates, boxplots, and other plots comparing the empirical distribution
 #'   of the observed data `y` to the distributions of individual replicated
@@ -78,7 +77,6 @@
 #'   multinomial outcomes.
 #' * [__LOO predictive checks__][PPC-loo]: PPC functions for predictive checks
 #'   based on (approximate) leave-one-out (LOO) cross-validation.
-#' }
 #'
 #' @section Providing an interface for posterior predictive checking from another package:
 #'