change class vwm to circular (#216)

NAMESPACE

@@ -7,12 +7,12 @@ S3method(add_links,bmmfit)
 S3method(add_links,bmmodel)
 S3method(bmf2bf,bmmodel)
 S3method(check_data,bmmodel)
+S3method(check_data,circular)
 S3method(check_data,default)
 S3method(check_data,imm_bsc)
 S3method(check_data,imm_full)
 S3method(check_data,non_targets)
 S3method(check_data,sdm)
-S3method(check_data,vwm)
 S3method(check_formula,bmmodel)
 S3method(check_formula,default)
 S3method(check_formula,non_targets)

R/helpers-data.R

@@ -12,7 +12,7 @@
 #'   should be defined in the appropriate check_data.* function, where \*
 #'   corresponds to the shared class. For example, for the .model_imm_abc model,
 #'   this corresponds to the following order of check_data.* functions:
-#'   check_data() -> check_data.vwm(), check_data.non_targets() the output of the
+#'   check_data() -> check_data.circular(), check_data.non_targets() the output of the
 #'   final function is returned to bmm().
 #' @param model A model list object returned from check_model()
 #' @param data The user supplied data.frame containing the data to be checked
@@ -48,7 +48,7 @@ check_data.bmmodel <- function(model, data, formula) {
 
 
 #' @export
-check_data.vwm <- function(model, data, formula) {
+check_data.circular <- function(model, data, formula) {
   resp_name <- model$resp_vars[[1]]
   stopif(not_in(resp_name, colnames(data)),
          "The response variable '{resp_name}' is not present in the data.")

R/model_imm.R

@@ -55,7 +55,7 @@
       # attributes
       regex = regex,
       regex_vars = c('nt_features', 'nt_distances'),
-      class = c("bmmodel", "vwm", "non_targets", "imm", paste0('imm_',version)),
+      class = c("bmmodel", "circular", "non_targets", "imm", paste0('imm_',version)),
       call = call
     )
 

R/model_mixture2p.R

@@ -40,7 +40,7 @@
       ),
       void_mu = FALSE
     ),
-    class = c("bmmodel", "vwm", "mixture2p"),
+    class = c("bmmodel", "circular", "mixture2p"),
     call = call
   )
   out$links[names(links)] <- links

R/model_mixture3p.R

@@ -51,7 +51,7 @@
     # attributes
     regex = regex,
     regex_vars = c('nt_features'),
-    class =  c("bmmodel", "vwm", "non_targets", "mixture3p"),
+    class =  c("bmmodel", "circular", "non_targets", "mixture3p"),
     call = call
   )
   out$links[names(links)] <- links

R/model_sdm.R

@@ -38,7 +38,7 @@
       ),
       void_mu = FALSE
     ),
-    class = c('bmmodel', 'vwm', 'sdm', paste0("sdm_", version)),
+    class = c('bmmodel', 'circular', 'sdm', paste0("sdm_", version)),
     call = call
   )
   out$links[names(links)] <- links

tests/internal/generate_ref_model_fits_v0.2.1.R

@@ -15,7 +15,7 @@ install_and_load_bmm_version <- function(version) {
   library(bmm, lib.loc=path)
 }
 
-# Load data for vwm models (Participant 1-10, SetSize 1-4, from oberauer_lin_2017)
+# Load data for circular models (Participant 1-10, SetSize 1-4, from oberauer_lin_2017)
 # TODO: generalize in the future to allow for different datasets for different models
 ref_data <- function() {
   withr::local_package('dplyr')

vignettes/articles/bmm_bmmformula.Rmd

@@ -129,7 +129,7 @@ my_model <- sdm(resp_error = "error")
 my_model$fixed_parameters
 ```
 
-Fixed parameters that can be freely estimated are also listed in the `parameters` of a model, that you can access using `my_model$parameters`. Parameters that are only listed in the `fixed_parameters` but not part of the `parameters` need to be fixed for identification purposes and thus cannot be estimated freely. For example in the `mixture_vwm` models, the location `mu2` and precision `kappa2` of the guessing distributions needs to be fixed for model identification. These parameters are listed in the `fixed_parameters` but not in the `parameters` and therefore cannot be estimated. Only, the internally fixed location of the target responses `mu1` can be estimated freely if needed.
+Fixed parameters that can be freely estimated are also listed in the `parameters` of a model, that you can access using `my_model$parameters`. Parameters that are only listed in the `fixed_parameters` but not part of the `parameters` need to be fixed for identification purposes and thus cannot be estimated freely. For example in the `mixture` models for visual working memory, the location `mu2` and precision `kappa2` of the guessing distributions needs to be fixed for model identification. These parameters are listed in the `fixed_parameters` but not in the `parameters` and therefore cannot be estimated. Only, the internally fixed location of the target responses `mu1` can be estimated freely if needed.
 
 ```{r}
 my_model <- mixture2p(resp_error = "error")