Don't use two attributes for same purpose

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: modelbased
 Title: Estimation of Model-Based Predictions, Contrasts and Means
-Version: 0.17.0.18
+Version: 0.17.0.19
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",

R/estimate_predicted.R

@@ -564,7 +564,9 @@ estimate_relation <- function(
     out$Residuals <- response - out$Predicted
   }
 
-  # transform reponse?
+  # transform response? we keep the argument as passed by the user for the
+  # table footer
+  transform_arg <- transform
   if (isTRUE(transform)) {
     trans_fun <- insight::get_transformation(model, verbose = FALSE)$inverse
   } else {
@@ -582,8 +584,7 @@ estimate_relation <- function(
   attr(out, "iterations") <- iterations
   attr(out, "keep_iterations") <- keep_iterations
   attr(out, "response") <- model_response
-  attr(out, "transform") <- !is.null(transform)
-  attr(out, "transform_arg") <- transform
+  attr(out, "transform") <- transform_arg
   attr(out, "model") <- model
   attr(out, "datagrid") <- data
   attr(out, "focal_terms") <- grid_specs$at_specs$varname
@@ -603,8 +604,7 @@ estimate_relation <- function(
   # attributes in `grid_specs`, so we replace them with those of this call
   footer_info <- grid_specs
   footer_info$predict <- predict
-  footer_info$transform <- !is.null(transform)
-  footer_info$transform_arg <- transform
+  footer_info$transform <- transform_arg
   footer_info$predict_used <- footer_predict
   attr(out, "table_footer") <- .table_footer(
     out,
@@ -616,7 +616,7 @@ estimate_relation <- function(
 
   attributes(out) <- c(
     attributes(out),
-    grid_specs[!names(grid_specs) %in% c(names(attributes(out)), "transform_arg")]
+    grid_specs[!names(grid_specs) %in% names(attributes(out))]
   )
 
   # Class

R/get_marginalcontrasts.R

@@ -184,7 +184,6 @@ get_marginalcontrasts <- function(
       contrast = my_args$contrast,
       predict = predict,
       transform = transform,
-      transform_arg = transform,
       comparison = my_args$comparison,
       estimate = estimate,
       p_adjust = p_adjust,

R/get_marginalmeans.R

@@ -319,8 +319,7 @@ get_marginalmeans <- function(
         predict = predict_args$predict,
         estimate = estimate,
         datagrid = datagrid,
-        transform = !is.null(transform),
-        transform_arg = transform_arg,
+        transform = transform_arg,
         iterations = iterations,
         keep_iterations = keep_iterations,
         joint_test = joint_test,
@@ -668,7 +667,7 @@ get_marginalmeans <- function(
 # - slope: the type of slope, e.g. "dx/dy". equals the "slope" argument when
 #   calling avg_slopes()
 # - model_info: object from insight::model_info()
-# - transform_arg: the `transform` argument as passed by the user (`transform`
+# - transform: the `transform` argument as passed by the user (`transform`
 #   is often only a logical), used for the scale line of the table footer
 # - renamed_focal: focal terms with the name of an output column, named by
 #   their old names, with the new names "<name>.1" as values
@@ -701,8 +700,8 @@ get_marginalmeans <- function(
   # fmt: skip
   c(
     "at", "by", "focal_terms", "adjusted_for", "predict", "trend", "comparison",
-    "contrast", "estimate", "p_adjust", "transform", "transform_arg", "datagrid",
-    "preserve_range", "coef_name", "slope", "ci", "model_info", "contrast_filter", "null",
+    "contrast", "estimate", "p_adjust", "transform", "datagrid", "preserve_range",
+    "coef_name", "slope", "ci", "model_info", "contrast_filter", "null",
     "iterations", "keep_iterations", "joint_test", "omnibus_test", "vcov",
     "equivalence", "context_effects", "renamed_focal"
   )

R/table_footer.R

@@ -134,7 +134,7 @@
     # here. `info$transform` is often only a logical (and always `TRUE` for
     # emmeans), so we use the `transform` argument as passed by the user
     if (predict == "link") {
-      predict <- .link_transform_label(info$transform_arg, model)
+      predict <- .link_transform_label(info$transform, model)
     }
     # if we have contrasts of slope for GLMs, the default is to calculate
     # ORs or IRRs - in this case, we don't add information about the scale