Updated tests documentation

R/measure_diffusion.R

@@ -1,6 +1,6 @@
 # net_diffusion ####
 
-#' Diffusion metrics for networks
+#' Measures of network diffusion
 #' @description
 #'   These functions allow measurement of various features of
 #'   a diffusion process:
@@ -17,6 +17,7 @@
 #'   to be protected through vaccination, isolation, or recovery for herd immunity to be reached.
 #'   - `network_hazard()` measures the hazard rate or instantaneous probability that
 #'   nodes will adopt/become infected at that time
+#'   
 #' @param diff_model A valid network diffusion model,
 #'   as created by `as_diffusion()` or `play_diffusion()`.
 #' @family measures
@@ -199,7 +200,7 @@ network_hazard <- function(diff_model){
 
 # node_diffusion ####
 
-#' Diffusion metrics for nodes
+#' Measures of nodes in a diffusion
 #' @description
 #'   These functions allow measurement of various features of
 #'   a diffusion process:
@@ -215,6 +216,7 @@ network_hazard <- function(diff_model){
 #'   a given mark
 #'   - `node_is_exposed()`: Marks the nodes that are susceptible,
 #'   i.e. are in the immediate neighbourhood of given mark vector
+#'   
 #' @inheritParams cohesion
 #' @inheritParams net_diffusion
 #' @family measures

R/model_regression.R

@@ -1,5 +1,6 @@
 #' Linear and logistic regression for network data
 #' 
+#' @description
 #' This function provides an implementation of
 #' the multiple regression quadratic assignment procedure (MRQAP)
 #' for both one-mode and two-mode network linear models.

R/model_tests.R

@@ -1,7 +1,17 @@
 #' Conditional uniform graph and permutation tests
 #' 
-#' These functions conduct conditional uniform graph (CUG) 
-#' or permutation (QAP) tests of any graph-level statistic.
+#' @description
+#'   These functions conduct tests of any network-level statistic:
+#'   
+#'   - `test_random()` performs a conditional uniform graph (CUG) test
+#'   of a measure against a distribution of measures on random networks 
+#'   of the same dimensions.
+#'   - `test_permutation()` performs a quadratic assignment procedure (QAP) test 
+#'   of a measure against a distribution of measures on permutations 
+#'   of the original network.
+#'   - `test_gof()` performs a chi-squared test on the squared Mahalanobis distance 
+#'   between a diff_model and diff_models objects.
+#'   
 #' @name tests
 #' @inheritParams regression
 #' @family models
@@ -10,8 +20,7 @@
 #'   e.g. the name of the attribute.
 NULL
 
-#' @describeIn tests Returns test results for some measure on an object
-#'   against a distribution of measures on random networks of the same dimensions
+#' @rdname tests 
 #' @examples 
 #' marvel_friends <- to_unsigned(ison_marvel_relationships)
 #' marvel_friends <- to_giant(marvel_friends) %>% 
@@ -62,8 +71,7 @@ test_random <- function(.data, FUN, ...,
   class(out) <- "network_test"
   out
 }
-#' @describeIn tests Returns test results for some measure on an object
-#'   against a distribution of measures on permutations of the original network
+#' @rdname tests 
 #' @examples 
 #' (qaptest <- test_permutation(marvel_friends, 
 #'                 network_heterophily, attribute = "Attractive",
@@ -161,8 +169,7 @@ plot.network_test <- function(x, ...,
                         color="red", linewidth=1.2) + ggplot2::ylab("Density")
 }
 
-#' @describeIn tests Returns the squared Mahalanobis distance 
-#'   and chi-squared results for diff_model and diff_models objects
+#' @rdname tests 
 #' @param diff_model A diff_model object is returned by
 #'   `play_diffusion()` or `as_diffusion()` and contains
 #'   a single empirical or simulated diffusion.