rename bmm_formula to bmmformula and replace all old brmsformula refe…

…rences in documentation

- there are still brms::bf cases in examples and vignettes, but let's change those at the end when everything is working

NAMESPACE

@@ -19,7 +19,7 @@ export("%>%")
 export(IMMabc)
 export(IMMbsc)
 export(IMMfull)
-export(bmm_formula)
+export(bmmformula)
 export(c_bessel2sqrtexp)
 export(c_sqrtexp2bessel)
 export(calc_error_relative_to_nontargets)

R/fit_model.R

@@ -6,7 +6,7 @@
 #'   This is a wrapper function for [brms::brm], which is used to estimate the
 #'   model.
 #'
-#' @param formula An object of class `brmsformula`. A symbolic description of
+#' @param formula An object of class `bmmformula`. A symbolic description of
 #'   the model to be fitted.
 #' @param data An object of class data.frame, containing data of all variables
 #'   used in the model. The names of the variables must match the variable names

R/helpers-formula.R

@@ -7,9 +7,9 @@ check_formula <- function(model, formula) {
   # Pre-Check: was a valid brms formula provided
   if (inherits(formula, 'brmsformula')) {
     stop("The provided formula is a brms formula.
-        Please specify formula with the bmm_formula() function instead of
+        Please specify formula with the bmmformula() function instead of
         the brmsformula() or bf() function.
-        E.g.: bmm_formula(kappa ~ 1, thetat ~ 1")
+        E.g.: bmmformula(kappa ~ 1, thetat ~ 1")
   }
 
   # Check: is the formula valid for the specified model type
@@ -101,15 +101,15 @@ get_response <- function(formula) {
 #' @return A list of formulas for each parameters being predicted
 #' @export
 #' @examples
-#' imm_formula <- bmm_formula(
+#' imm_formula <- bmmformula(
 #'   c ~ 0 + setsize + (0 + setsize | id),
 #'   a ~ 1,
 #'   kappa ~ 0 + setsize + (0 + setsize | id)
 #' )
 #'
 #' imm_formula
 #'
-bmm_formula <- function(formula, ...){
+bmmformula <- function(formula, ...){
   # paste formulas into a single list
   dots <- list(...)
   formula <- list(formula)

R/helpers-model.R

@@ -478,7 +478,7 @@ use_model_template <- function(model_name,
 #' @description A wrapper around `brms::make_stancode()` for models specified with
 #'   `bmm`. Given the `model`, the `data` and the `formula` for the model, this
 #'   function will return the combined stan code generated by `bmm` and `brms`
-#' @param formula An object of class `brmsformula`. A symbolic description of
+#' @param formula An object of class `bmmformula`. A symbolic description of
 #'   the model to be fitted.
 #' @param data An object of class data.frame, containing data of all variables
 #'   used in the model. The names of the variables must match the variable names
@@ -550,7 +550,7 @@ get_stancode <- function(formula, data, model, prior=NULL, ...) {
 #'   `bmm`. Given the `model`, the `data` and the `formula` for the model, this
 #'   function will return just the parameters block. Useful for figuring out
 #'   which paramters you can set initial values on
-#' @param formula An object of class `brmsformula`. A symbolic description of
+#' @param formula An object of class `bmmformula`. A symbolic description of
 #'   the model to be fitted.
 #' @param data An object of class data.frame, containing data of all variables
 #'   used in the model. The names of the variables must match the variable names

R/helpers-prior.R

@@ -19,7 +19,7 @@ combine_prior <- function(prior1, prior2) {
 #'   return all model parameters that have no prior specified (flat priors). This can help to
 #'   get an idea about which priors need to be specified and also know which priors were
 #'   used if no user-specified priors were passed to the [fit_model()] function.
-#' @param formula An object of class `brmsformula`. A symbolic description of
+#' @param formula An object of class `bmmformula`. A symbolic description of
 #'   the model to be fitted.
 #' @param data An object of class data.frame, containing data of all variables
 #'   used in the model. The names of the variables must match the variable names
@@ -53,7 +53,7 @@ combine_prior <- function(prior1, prior2) {
 #' dat <- data.frame(y = rsdm(n=2000))
 #'
 #' # define formula
-#' ff <- brms::bf(y ~ 1,
+#' ff <- b(y ~ 1,
 #'                c ~ 1,
 #'                kappa ~ 1)
 #'

tests/testthat/test-fit_model.R

@@ -9,14 +9,14 @@ test_that('Available mock models run without errors',{
   )
 
   # two-parameter model mock fit
-  f <- bmm_formula(kappa ~ 1, thetat ~ 1)
+  f <- bmmformula(kappa ~ 1, thetat ~ 1)
   mock_fit <- fit_model(f, dat, mixture2p(respErr =  "respErr"), backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
 
   # three-parameter model mock fit
-  f <- bmm_formula(kappa ~ 1, thetat ~ 1, thetant ~ 1)
+  f <- bmmformula(kappa ~ 1, thetat ~ 1, thetant ~ 1)
   mock_fit <- fit_model(f, dat, mixture3p(respErr = "respErr", setsize = 3,
                                           non_targets = paste0('Item',2:3,'_rel')),
                         backend="mock", mock_fit=1, rename=FALSE)
@@ -25,7 +25,7 @@ test_that('Available mock models run without errors',{
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
 
   # IMMabc model mock fit
-  f <- bmm_formula(kappa ~ 1, c ~ 1, a ~ 1)
+  f <- bmmformula(kappa ~ 1, c ~ 1, a ~ 1)
   mock_fit <- fit_model(f, dat, IMMabc(respErr = "respErr", setsize =3,
                                        non_targets = paste0('Item',2:3,'_rel')),
                         backend="mock", mock_fit=1, rename=FALSE)
@@ -34,15 +34,15 @@ test_that('Available mock models run without errors',{
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
 
   # IMMbsc model mock fit
-  f <- bmm_formula(kappa ~ 1, c ~ 1, s ~ 1)
+  f <- bmmformula(kappa ~ 1, c ~ 1, s ~ 1)
   mock_fit <- fit_model(f, dat, IMMbsc(respErr = "respErr", setsize=3, non_targets = paste0('Item',2:3,'_rel'), spaPos=paste0('spaD',2:3)),
                         backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
 
   # IMMbsc model mock fit
-  f <- bmm_formula(kappa ~ 1, c ~ 1, a ~ 1, s ~ 1)
+  f <- bmmformula(kappa ~ 1, c ~ 1, a ~ 1, s ~ 1)
   mock_fit <- fit_model(f, dat, IMMfull(respErr = "respErr", setsize=3, non_targets = paste0('Item',2:3,'_rel'), spaPos=paste0('spaD',2:3)), backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
@@ -66,19 +66,19 @@ test_that('Available models produce expected errors', {
     args_list <- formals(model)
     test_args <- lapply(args_list, function(x) {NULL})
     model <- brms::do_call(model, test_args)
-    expect_error(fit_model(bmm_formula(kappa~1), model=model, backend="mock", mock_fit=1, rename=FALSE),
+    expect_error(fit_model(bmmformula(kappa~1), model=model, backend="mock", mock_fit=1, rename=FALSE),
                  "Data must be specified using the 'data' argument.")
   }
 
 
   okmodels <- c('mixture3p','IMMabc','IMMbsc','IMMfull')
   for (model in okmodels) {
     model1 <- get_model2(model)(respErr = "respErr", non_targets='Item2_rel', setsize=5, spaPos='spaD2')
-    expect_error(fit_model(bmm_formula(kappa~1), data=dat, model=model1, backend="mock",
+    expect_error(fit_model(bmmformula(kappa~1), data=dat, model=model1, backend="mock",
                            mock_fit=1, rename=FALSE),
                  "'non_targets' is less than max\\(setsize\\)-1")
     model2 <- get_model2(model)(respErr = "respErr", non_targets='Item2_rel', setsize=TRUE, spaPos='spaD2')
-    expect_error(fit_model(bmm_formula(kappa~1), data=dat, model=model2, backend="mock",
+    expect_error(fit_model(bmmformula(kappa~1), data=dat, model=model2, backend="mock",
                            mock_fit=1, rename=FALSE),
                  "'setsize' must be either a single numeric value or a character string")
   }

tests/testthat/test-helpers-data.R

@@ -1,46 +1,46 @@
 test_that("check_data() produces expected errors and warnings", {
   expect_error(check_data(.model_mixture2p(respErr = "y")),
                "Data must be specified using the 'data' argument.")
-  expect_error(check_data(.model_mixture2p(respErr = "y"), data.frame(), bmm_formula(kappa ~ 1)),
+  expect_error(check_data(.model_mixture2p(respErr = "y"), data.frame(), bmmformula(kappa ~ 1)),
                "Argument 'data' does not contain observations.")
-  expect_error(check_data(.model_mixture2p(respErr = "y"), data.frame(x = 1), bmm_formula(kappa ~ 1)),
+  expect_error(check_data(.model_mixture2p(respErr = "y"), data.frame(x = 1), bmmformula(kappa ~ 1)),
                "The response variable 'y' is not present in the data.")
 
   mls <- lapply(c('mixture2p','mixture3p','IMMabc','IMMbsc','IMMfull'), get_model2)
   for (ml in mls) {
     expect_warning(check_data(ml(respErr = "y", non_targets = 'x', setsize=2, spaPos = 'z'),
                               data.frame(y = 12, x = 1, z = 2),
-                              bmm_formula(kappa ~ 1)),
+                              bmmformula(kappa ~ 1)),
                    "It appears your response variable is in degrees.\n")
     expect_silent(check_data(ml(respErr = "y", non_targets = 'x', setsize=2, spaPos = 'z'),
                              data.frame(y = 1, x = 1, z = 2), brms::bf(y ~ 1)))
   }
 
   mls <- lapply(c('mixture3p','IMMabc','IMMbsc','IMMfull'), get_model2)
   for (ml in mls) {
-    expect_error(check_data(ml(respErr = "y", non_targets = 'x', spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y", non_targets = 'x', spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)),
                  'argument "setsize" is missing, with no default')
-    expect_error(check_data(ml(respErr = "y",setsize = 'x', spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y",setsize = 'x', spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)),
                  'argument "non_targets" is missing, with no default')
-    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = TRUE, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = TRUE, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)),
                  "Argument 'setsize' must be either a single numeric value or a character string.")
-    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = c(1,2,3), spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = c(1,2,3), spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)),
                  "Argument 'setsize' must be either a single numeric value or a character string.")
-    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = 5, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y",non_targets='x', setsize = 5, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)),
                  "'non_targets' is less than max\\(setsize\\)-1")
   }
 
   mls <- lapply(c('IMMbsc','IMMfull'), get_model2)
   for (ml in mls) {
-    expect_error(check_data(ml(respErr = "y",non_targets=paste0('x',1:4), setsize = 5, spaPos = 'z'), data.frame(y = 1, x1 = 1, x2=2,x3=3,x4=4, z = 2), bmm_formula(kappa ~ 1)),
+    expect_error(check_data(ml(respErr = "y",non_targets=paste0('x',1:4), setsize = 5, spaPos = 'z'), data.frame(y = 1, x1 = 1, x2=2,x3=3,x4=4, z = 2), bmmformula(kappa ~ 1)),
                  "'spaPos' is less than max\\(setsize\\)-1")
   }
 })
 
 test_that("check_data() returns a data.frame()", {
   mls <- lapply(supported_models(print_call=FALSE), get_model)
   for (ml in mls) {
-    expect_s3_class(check_data(ml(respErr = "y",non_targets = 'x', setsize=2, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmm_formula(kappa ~ 1)), "data.frame")
+    expect_s3_class(check_data(ml(respErr = "y",non_targets = 'x', setsize=2, spaPos = 'z'), data.frame(y = 1, x = 1, z = 2), bmmformula(kappa ~ 1)), "data.frame")
   }
 })
 
@@ -86,7 +86,7 @@ test_that("rad2deg returns the correct values for 0, pi/2, 2*pi", {
 
 test_that("get_standata() returns a string", {
   # define formula
-  ff <- bmm_formula(kappa ~ 1,
+  ff <- bmmformula(kappa ~ 1,
                     thetat ~ 1,
                     thetant ~ 1)
 

tests/testthat/test-helpers-model.R

@@ -39,7 +39,7 @@ test_that("use_model_template() prevents duplicate models", {
 
 test_that("get_stancode() returns a string", {
   # define formula
-  ff <- bmm_formula(kappa ~ 1,
+  ff <- bmmformula(kappa ~ 1,
                     thetat ~ 1,
                     thetant ~ 1)
 

tests/testthat/test-helpers-postprocess.R

@@ -1,7 +1,7 @@
 test_that("bmm version is added to mock model", {
   dat <- data.frame(y = rsdm(n=10))
 
-  ff <- bmm_formula(c ~ 1,
+  ff <- bmmformula(c ~ 1,
                  kappa ~ 1)
 
   fit <- fit_model(formula = ff,

tests/testthat/test-helpers-prior.R

@@ -1,6 +1,6 @@
 test_that("get_model_prior() returns a brmsprior object", {
   # define formula
-  ff <- bmm_formula(kappa ~ 1,
+  ff <- bmmformula(kappa ~ 1,
                     thetat ~ 1,
                     thetant ~ 1)
 

vignettes/mixture_models.Rmd

@@ -160,7 +160,7 @@ The model formula has three components:
 [^fn-1]: `brms` does not directly estimate the probabilities that each response comes from each distribution (e.g. $p_{mem}$ and $p_{guess}$). Instead, brms estimates mixing proportions that are weights applied to each of the mixture distributions and they are transformed into probabilities (e.g. $p_{mem}$ and $p_{guess}$) using a softmax normalization. To get $p_{mem}$ we can use the softmax function, that is: $p_{mem} = \frac{exp(\theta_{target})}{1+exp(\theta_{target})}$
 
 ```{r}
-ff <- bmm_formula(thetat ~ 0 + set_size + (0 + set_size | id),
+ff <- bmmformula(thetat ~ 0 + set_size + (0 + set_size | id),
                   kappa ~ 0 + set_size + (0 + set_size | id))
 ```