Merge pull request #80 from Gilead-BioStats/documentation

Update mathematical notations

R/adjust_weights.R

@@ -53,8 +53,8 @@
 #'   dimensions as `matrix_weights`, whose hypothesis weights have been
 #'   adjusted according to Simes tests.
 #' * `graphicalMCP:::c_value_function()` returns the difference between
-#'   \eqn{\alpha} and the Type I error of the parametric test with the $c$ value
-#'   of `x`, adjusted for the correlation between test statistics using
+#'   \eqn{\alpha} and the Type I error of the parametric test with the \eqn{c}
+#'   value of `x`, adjusted for the correlation between test statistics using
 #'   parametric tests based on equation (6) of Xi et al. (2017).
 #' * `graphicalMCP:::solve_c_parametric()` returns the c value adjusted for the
 #'   correlation between test statistics using parametric tests based on

R/graph_generate_weights.R

@@ -10,27 +10,27 @@
 #'
 #' The closure based on a graph consists of all updated graphs (corresponding
 #' to intersection hypotheses) after all combinations of hypotheses are deleted.
-#' For a graphical multiple comparison procedure with $m$ hypotheses, there are
-#' $2^{m}-1$ updated graphs (intersection hypotheses), including the initial
-#' graph (the overall intersection hypothesis). The weighting strategy of this
-#' graph consists of hypothesis weights from all $2^{m}-1$ updated graphs
-#' (intersection hypotheses). The algorithm to derive the weighting strategy is
-#' based on Algorithm 1 in Bretz et al. (2011).
+#' For a graphical multiple comparison procedure with \eqn{m} hypotheses, there
+#' are \eqn{2^{m}-1} updated graphs (intersection hypotheses), including the
+#' initial graph (the overall intersection hypothesis). The weighting strategy
+#' of this graph consists of hypothesis weights from all \eqn{2^{m}-1} updated
+#' graphs (intersection hypotheses). The algorithm to derive the weighting
+#' strategy is based on Algorithm 1 in Bretz et al. (2011).
 #'
 #' @inheritParams graph_update
 #'
 #' @return A numeric matrix of all intersection hypotheses and their hypothesis
-#' weights. For a graphical multiple comparison procedure with $m$ hypotheses,
-#' the number of rows is $2^{m}-1$, each of which corresponds to an intersection
-#' hypothesis. The number of columns is \eqn{2\cdot m}$. The first $m$ columns
+#' weights. For a graphical multiple comparison procedure with \eqn{m} hypotheses,
+#' the number of rows is \eqn{2^{m}-1}, each of which corresponds to an intersection
+#' hypothesis. The number of columns is \eqn{2\cdot m}. The first \eqn{m} columns
 #' indicate which individual hypotheses are included in a given intersection
 #' hypothesis and the second half of columns provide hypothesis weights for each
 #' individual hypothesis for a given intersection hypothesis.
 #'
 #' @section Performance:
 #' Generation of intersection hypotheses is closely related to the power set
 #' of a given set of indices. As the number of hypotheses increases, the memory
-#' and time usage can grow quickly (e.g., at a rate of $O(2^n)$). There are also
+#' and time usage can grow quickly (e.g., at a rate of \eqn{O(2^n)}). There are also
 #' multiple ways to implement Algorithm 1 in Bretz et al. (2011). See
 #' `vignette("generate-closure")` for more information about generating
 #' intersection hypotheses and comparisons of different approaches to calculate

R/graph_test_closure.R

@@ -10,7 +10,7 @@
 #' true simultaneously.
 #'
 #' For a graphical multiple comparison procedure with $m$ hypotheses, there are
-#' $2^{m}-1$ intersection hypotheses. For each intersection hypothesis, a test
+#' \eqn{2^m-1} intersection hypotheses. For each intersection hypothesis, a test
 #' type could be chosen to determine how to reject the intersection hypothesis.
 #' Current choices of test types include Bonferroni, Simes and parametric. This
 #' implementation offers a more general framework covering Bretz et al. (2011),

R/plot.initial_graph.R

@@ -8,7 +8,7 @@
 #' of commonly used multiple comparison procedure using graphs.
 #'
 #' @param x An object of class `initial_graph` to plot.
-#' @param ... Other arguments passed on to [igraph::plot.igraph()].
+#' @param ... Other arguments passed on to `igraph::plot.igraph()`.
 #' @param v_palette A character vector of length two specifying the colors for
 #'   retained and deleted hypotheses. More extensive color customization must be
 #'   done with `vertex.color`.

_pkgdown.yml

@@ -5,7 +5,7 @@ template:
 
 reference:
 - title: Creating an initial graph
-- desc: Functions for creating a multiple comparison procedure graph
+- desc: Functions for creating a graphical multiple comparison procedure
 - contents:
   - graph_create
   - print.initial_graph
@@ -14,29 +14,29 @@ reference:
   - as_graphMCP
   - as_igraph
 - title: Updating a graph
-- desc: Functions for removing hypotheses from a graph
+- desc: Functions for updating a graphical multiple comparison procedure after deleting hypotheses
 - contents:
   - graph_update
   - print.updated_graph
   - plot.updated_graph
-- title: Calculating sub-graph weights
-- desc: Functions for generating the weights of the closure
+- title: Calculating hypothesis weights in a closure
+- desc: Function for generating the weighting strategy
 - contents:
   - graph_generate_weights
-- title: Test a graph
-- desc: Functions for running Bonferroni, parametric, and Simes tests
+- title: Testing a graphical multiple comparison procedure
+- desc: Functions for performing a graphical multiple comparison procedure
 - contents:
   - graph_test_closure
   - graph_test_shortcut
   - print.graph_report
   - graph_rejection_orderings
-- title: Run a power simulation
-- desc: Functions for running power simulations
+- title: Power simulation
+- desc: Functions for performing power simulations
 - contents:
   - graph_calculate_power
   - print.power_report
 - title: Example graphs
-- desc: Functions for creating example graphs from a variety of papers
+- desc: Functions for creating example graphical multiple comparison procedures
 - contents:
   - bonferroni
   - bonferroni_holm