Reduce number of methods in docs

R/methods_FactoMineR.R

@@ -59,18 +59,16 @@ model_parameters.PCA <- function(model,
 
 
   # add class-attribute for printing
-  if ("PCA" %in% class(model)) {
+  if (inherits(model, "PCA")) {
     attr(loadings, "type") <- "pca"
     class(loadings) <- unique(c("parameters_pca", "see_parameters_pca", class(loadings)))
-  } else if ("FAMD" %in% class(model)) {
+  } else if (inherits(model, "FAMD")) {
     attr(loadings, "type") <- "fa"
     class(loadings) <- unique(c("parameters_efa", "see_parameters_efa", class(loadings)))
   }
 
   loadings
 }
 
-
-#' @rdname model_parameters.principal
 #' @export
 model_parameters.FAMD <- model_parameters.PCA

R/methods_PMCMRplus.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.PMCMR <- function(model, ...) {
   insight::check_if_installed("PMCMRplus")

R/methods_aov.R

@@ -143,7 +143,7 @@
   object_name <- insight::safe_deparse_symbol(substitute(model))
 
   if (inherits(model, "aov") && !is.null(type) && type > 1) {
     if (!requireNamespace("car", quietly = TRUE)) {
       insight::format_warning("Package {.pkg car} required for type-2 or type-3 Anova. Defaulting to type-1.")
     } else {
       model <- car::Anova(model, type = type)
@@ -255,7 +255,6 @@
 #' @export
 p_value.anova <- p_value.aov
 
-#' @rdname model_parameters.aov
 #' @export
 model_parameters.anova <- model_parameters.aov
 
@@ -269,7 +268,6 @@
 #' @export
 p_value.aovlist <- p_value.aov
 
-#' @rdname model_parameters.aov
 #' @export
 model_parameters.aovlist <- model_parameters.aov
 
@@ -333,15 +331,12 @@
 
 # others  ------
 
-#' @rdname model_parameters.aov
 #' @export
 model_parameters.anova.rms <- model_parameters.aov
 
-#' @rdname model_parameters.aov
 #' @export
 model_parameters.Anova.mlm <- model_parameters.aov
 
-#' @rdname model_parameters.aov
 #' @export
 model_parameters.maov <- model_parameters.aov
 
@@ -356,11 +351,11 @@
       }
       switch(type,
         `1` = ,
         `I` = 1,
         `2` = ,
         `II` = 2,
         `3` = ,
         `III` = 3,
         1
       )
     }
@@ -435,7 +430,7 @@
     predictors <- .safe(insight::get_predictors(model))
 
     # if data available, check contrasts and mean centering
     if (!is.null(predictors)) {
       treatment_contrasts_or_not_centered <- vapply(predictors, function(i) {
         if (is.factor(i)) {
           cn <- stats::contrasts(i)
@@ -456,7 +451,7 @@
     # successfully checked predictors, or if not possible, at least found interactions?
     if (!is.null(interaction_terms) && (any(treatment_contrasts_or_not_centered) || is.null(predictors))) {
       insight::format_alert(
         "Type 3 ANOVAs only give sensible and informative results when covariates are mean-centered and factors are coded with orthogonal contrasts (such as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but *not* by the default `contr.treatment`)."
       )
     }
   }

R/methods_bamlss.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.stanreg
 #' @inheritParams insight::get_parameters
 #' @export
 model_parameters.bamlss <- function(model,
@@ -69,7 +68,7 @@
 
   .data_frame(
     Parameter = colnames(params),
     SE = unname(sapply(params, stats::sd, na.rm = TRUE))
   )
 }
 

R/methods_bayesQR.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.stanreg
 #' @export
 model_parameters.bayesQR <- function(model,
                                      centrality = "median",

R/methods_bbmle.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.mle2 <- model_parameters.glm
 

R/methods_cplm.R

@@ -165,7 +165,6 @@
 
 ########## .bcpglm ---------------
 
-#' @rdname model_parameters.stanreg
 #' @export
 model_parameters.bcplm <- model_parameters.bayesQR
 
@@ -298,10 +297,10 @@
   if (!is.null(df_method)) {
     df_method <- tolower(df_method)
     if (df_method %in% c("satterthwaite", "kenward", "kr")) {
       insight::format_alert("Satterthwaite or Kenward-Rogers approximation of degrees of freedom is only available for linear mixed models.")
       df_method <- "wald"
     }
     df_method <- match.arg(df_method, choices = c("wald", "normal", "residual", "ml1", "betwithin", "profile", "boot", "uniroot"))
   }
   df_method
 }

R/methods_emmeans.R

@@ -3,7 +3,6 @@
 # model_parameters ----------------
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.emmGrid <- function(model,
                                      ci = 0.95,
@@ -29,7 +28,7 @@
   params <- as.data.frame(s)
 
   # we assume frequentist here...
   if (!.is_bayesian_emmeans(model)) {
     # get statistic, se and p
     statistic <- insight::get_statistic(model, ci = ci, adjust = "none")
     SE <- standard_error(model)

R/methods_epi2x2.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.epi.2by2 <- function(model, verbose = TRUE, ...) {
   # get parameter estimates

R/methods_fitdistr.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.fitdistr <- function(model,
                                       exponentiate = FALSE,

R/methods_gam.R

@@ -4,7 +4,6 @@
 #################### .gam ------
 
 
-#' @rdname model_parameters.cgam
 #' @export
 model_parameters.gam <- model_parameters.cgam
 

R/methods_gamlss.R

@@ -1,14 +1,16 @@
 #################### .gamlss ------
 
-#' @rdname model_parameters.cgam
+
 #' @export
 model_parameters.gamlss <- model_parameters.gam
 
 
 #' @export
 standard_error.gamlss <- function(model, ...) {
   parms <- insight::get_parameters(model)
-  utils::capture.output(cs <- summary(model))
+  utils::capture.output({
+    cs <- summary(model)
+  })
 
   .data_frame(
     Parameter = parms$Parameter,
@@ -21,7 +23,9 @@ standard_error.gamlss <- function(model, ...) {
 #' @export
 p_value.gamlss <- function(model, ...) {
   parms <- insight::get_parameters(model)
-  utils::capture.output(cs <- summary(model))
+  utils::capture.output({
+    cs <- summary(model)
+  })
   .data_frame(
     Parameter = parms$Parameter,
     p = as.vector(cs[, 4]),

R/methods_gjrm.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.SemiParBIV <- function(model,
                                         ci = 0.95,

R/methods_ivfixed.R

@@ -33,7 +33,6 @@ p_value.ivFixed <- function(model, method = "wald", ...) {
 }
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.ivFixed <- function(model,
                                      ci = 0.95,

R/methods_ivprobit.R

@@ -20,6 +20,5 @@ standard_error.ivprobit <- function(model, ...) {
 p_value.ivprobit <- p_value.default
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.ivprobit <- model_parameters.ivFixed

R/methods_lmodel2.R

@@ -1,7 +1,6 @@
 # lmodel2
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.lmodel2 <- function(model,
                                      ci = 0.95,

R/methods_logistf.R

@@ -1,6 +1,5 @@
 # model_parameters --------------------
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.logistf <- model_parameters.glm
 

R/methods_lrm.R

@@ -4,7 +4,6 @@
 # model parameters -------------
 
 
-#' @rdname model_parameters.stanreg
 #' @export
 model_parameters.blrm <- model_parameters.bayesQR
 

R/methods_marginaleffects.R

@@ -46,27 +46,22 @@ model_parameters.marginaleffects <- function(model,
   out
 }
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.comparisons <- model_parameters.marginaleffects
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.marginalmeans <- model_parameters.marginaleffects
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.hypotheses <- model_parameters.marginaleffects
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.slopes <- model_parameters.marginaleffects
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.predictions <- function(model,
                                          ci = 0.95,

R/methods_margins.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.margins <- function(model, ci = 0.95, exponentiate = FALSE, p_adjust = NULL, verbose = TRUE, ...) {
   # Parameters, Estimate and CI
@@ -21,7 +20,12 @@ model_parameters.margins <- function(model, ci = 0.95, exponentiate = FALSE, p_a
   # ==== Renaming
 
   if ("Statistic" %in% names(params)) {
-    names(params) <- gsub("Statistic", gsub("(-|\\s)statistic", "", attr(statistic, "statistic", exact = TRUE)), names(params))
+    names(params) <- gsub(
+      "Statistic",
+      gsub("(-|\\s)statistic", "", attr(statistic, "statistic", exact = TRUE)),
+      names(params),
+      fixed = TRUE
+    )
     names(params) <- gsub("chi-squared", "Chi2", names(params), fixed = TRUE)
   }
   names(params) <- gsub("(c|C)hisq", "Chi2", names(params))

R/methods_mass.R

@@ -133,12 +133,8 @@ model_parameters.ridgelm <- function(model, verbose = TRUE, ...) {
   parameters
 }
 
-
-#' @rdname model_parameters.default
 #' @export
 model_parameters.polr <- model_parameters.glm
 
-
-#' @rdname model_parameters.default
 #' @export
 model_parameters.negbin <- model_parameters.glm

R/methods_maxLik.R

@@ -1,12 +1,10 @@
 # .maxLik, .maxim
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.maxLik <- model_parameters.default
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.maxim <- model_parameters.default
 

R/methods_mediate.R

@@ -1,4 +1,3 @@
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.mediate <- function(model, ci = 0.95, exponentiate = FALSE, verbose = TRUE, ...) {
   # Parameters, Estimate and CI
@@ -13,7 +12,7 @@ model_parameters.mediate <- function(model, ci = 0.95, exponentiate = FALSE, ver
 
   # ==== Renaming
 
-  if (any(grepl("\\(control\\)$", params$Parameter))) {
+  if (any(endsWith(params$Parameter, "(control)"))) {
     params$Component <- gsub("(.*)\\((.*)\\)$", "\\2", params$Parameter)
   }
 

R/methods_metaplus.R

@@ -290,7 +290,6 @@ ci.meta_random <- function(x, method = "eti", ...) {
 
 ###### .meta_fixed -------------------
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.meta_fixed <- model_parameters.meta_random
 

R/methods_mfx.R

@@ -1,7 +1,6 @@
 # model parameters ---------------------
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.logitor <- function(model,
                                      ci = 0.95,
@@ -25,17 +24,14 @@ model_parameters.logitor <- function(model,
 }
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.poissonirr <- model_parameters.logitor
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.negbinirr <- model_parameters.logitor
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.poissonmfx <- function(model,
                                         ci = 0.95,
@@ -71,17 +67,14 @@ model_parameters.poissonmfx <- function(model,
 }
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.logitmfx <- model_parameters.poissonmfx
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.probitmfx <- model_parameters.poissonmfx
 
 
-#' @rdname model_parameters.averaging
 #' @export
 model_parameters.negbinmfx <- model_parameters.poissonmfx
 

R/methods_multcomp.R

@@ -2,7 +2,6 @@
 #'
 #' Parameters from Hypothesis Testing.
 #'
-#' @rdname model_parameters.averaging
 #' @param model Object of class [multcomp::glht()] (**multcomp**)
 #'   or of class `PMCMR`, `trendPMCMR` or `osrt` (**PMCMRplus**).
 #' @inheritParams model_parameters.default

R/methods_pscl.R

@@ -2,16 +2,12 @@
 
 # model parameters -----------------
 
-#' @rdname model_parameters.zcpglm
 #' @export
 model_parameters.zeroinfl <- model_parameters.zcpglm
 
-#' @rdname model_parameters.zcpglm
 #' @export
 model_parameters.hurdle <- model_parameters.zcpglm
 
-
-#' @rdname model_parameters.zcpglm
 #' @export
 model_parameters.zerocount <- model_parameters.zcpglm
 
@@ -40,11 +36,9 @@ ci.zeroinfl <- function(x,
   .ci_generic(model = x, ci = ci, dof = dof, method = method, component = component, ...)
 }
 
-
 #' @export
 ci.hurdle <- ci.zeroinfl
 
-
 #' @export
 ci.zerocount <- ci.zeroinfl
 
@@ -100,11 +94,9 @@ standard_error.zeroinfl <- function(model,
   .filter_component(se, component)
 }
 
-
 #' @export
 standard_error.hurdle <- standard_error.zeroinfl
 
-
 #' @export
 standard_error.zerocount <- standard_error.zeroinfl
 
@@ -160,11 +152,9 @@ p_value.zeroinfl <- function(model,
   .filter_component(p, component)
 }
 
-
 #' @export
 p_value.hurdle <- p_value.zeroinfl
 
-
 #' @export
 p_value.zerocount <- p_value.zeroinfl
 
@@ -230,10 +220,8 @@ simulate_parameters.zeroinfl <- function(model,
   out
 }
 
-
 #' @export
 simulate_parameters.hurdle <- simulate_parameters.zeroinfl
 
-
 #' @export
 simulate_parameters.zerocount <- simulate_parameters.zeroinfl