Small cosmetic changes.

R/plot_choices.r

@@ -1,6 +1,6 @@
 #' Plot how analytical choices affect results
 #'
-#' This functions plots how analytical choices affect the obtained results (i.e., the rank within the curve). Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). Further customization using \pkg{ggplot} is possible. This functions creates the lower panel in \code{plot_specs()}.
+#' This functions plots how analytical choices affect the obtained results (i.e., the rank within the curve). Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the lower panel in \code{plot_specs()}.
 #'
 #' @param df a data frame resulting from \code{run_specs()}.
 #' @param choices a vector specifying which analytical choices should be plotted. By default, all choices are plotted.

R/plot_curve.r

@@ -1,6 +1,6 @@
 #' Plot ranked specification curve
 #'
-#' This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). Further customization using \pkg{ggplot} is possible. This functions creates the upper panel in \code{plot_specs()}.
+#' This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the upper panel in \code{plot_specs()}.
 #'
 #' @param df a data frame resulting from \code{run_specs()}.
 #' @param desc logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE.

R/plot_decisiontree.r

@@ -1,6 +1,6 @@
 #' Plot decision tree
 #'
-#' This function plots a simple decision tree that is meant to help understanding how few analytical choices may results in a large number of specifications. It is somewhat useless if the final number of specifications is very high. Further customization using \pkg{ggplot} is possible.
+#' This function plots a simple decision tree that is meant to help understanding how few analytical choices may results in a large number of specifications. It is somewhat useless if the final number of specifications is very high.
 #'
 #' @param df data frame resulting from [run_specs()].
 #' @param label Logical. Should labels be included? Defaults to FALSE. Produces only a reasonable plot if number of specifications is low.

R/plot_samplesizes.r

@@ -1,6 +1,6 @@
 #' Plot sample sizes
 #'
-#' This function plots a histogram of sample sizes per specification. It can be added to the overall specification curve plot (see vignettes). Further customization using \pkg{ggplot} is possible.
+#' This function plots a histogram of sample sizes per specification. It can be added to the overall specification curve plot (see vignettes).
 #'
 #' @param df a data frame resulting from \code{run_specs()}.
 #' @param desc logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE.

R/plot_specs.r

@@ -54,8 +54,7 @@
 #'
 #' plot_specs(plot_a = p1,    # arguments must be called directly!
 #'            plot_b = p2,
-#'            rel_height = c(2, 2)) %>% class
-#'
+#'            rel_height = c(2, 2))
 #'@seealso \itemize{
 #'  \item [plot_curve()] to plot only the specification curve.
 #'  \item [plot_choices()] to plot only the choices panel.

R/plot_summary.r

@@ -1,6 +1,6 @@
 #' Create box plots for given analytical choices
 #'
-#' This function provides a convenient way to visually investigate the effect of individual choices on the estimate of interest. It produces box-and-whisker plot(s) for each provided analytical choice. Further customization using \pkg{ggplot} is possible.
+#' This function provides a convenient way to visually investigate the effect of individual choices on the estimate of interest. It produces box-and-whisker plot(s) for each provided analytical choice.
 #'
 #' @param df a data frame resulting from \code{run_specs()}.
 #' @param choices a vector specifying which analytical choices should be plotted. By default, all choices are plotted.

R/plot_variance.r

@@ -1,6 +1,6 @@
 #' Plot variance decomposition
 #'
-#' This functions creates a simple barplot that visually displays how much variance in the outcome (e.g., the regression coeficient) different analytical choices or combinations therefor account for. To use this approach, one needs to estimate a multilevel model that includes all analytical choices as grouping variables (see examples and vignettes). This function uses [icc_specs()] to compute the intraclass correlation coefficients (ICCs), which provides the data basis for the plot (see examples). Further customization using \pkg{ggplot} is possible.
+#' This functions creates a simple barplot that visually displays how much variance in the outcome (e.g., the regression coeficient) different analytical choices or combinations therefor account for. To use this approach, one needs to estimate a multilevel model that includes all analytical choices as grouping variables (see examples and vignettes). This function uses [icc_specs()] to compute the intraclass correlation coefficients (ICCs), which provides the data basis for the plot (see examples).
 #'
 #' @param model a multilevel model that captures the variances of the specification curve (based on the data frame resulting from \code{run_specs}).
 #'