add DV as variable to model() call & add bmm_formula

NAMESPACE

@@ -19,6 +19,7 @@ export("%>%")
 export(IMMabc)
 export(IMMbsc)
 export(IMMfull)
+export(bmf)
 export(bmmformula)
 export(c_bessel2sqrtexp)
 export(c_sqrtexp2bessel)

R/bmmformula.R

@@ -0,0 +1,102 @@
+#' @title Create formula for predicting parameters of a `bmmmodel`
+#'
+#' @description This function is used to specify the formulas predicting the
+#' different parameters of a `bmmmodel`.
+#'
+#' @aliases bmf
+#'
+#' @details # General formula structure
+#'
+#' The formula argument accepts formulas of the following syntax:
+#'
+#'   ``` r
+#'   parameter ~ fixed_effects + (random_effects | grouping_variable)
+#'   ```
+#'
+#' `bmm` formulas are built on `brms` formulas and function in nearly the same
+#' way, so you can use most of the `brms` formula syntax. The main differences
+#' is that in `bmm` formulas, the response variable is not specified in the
+#' formula. Instead, each parameter of the model is explicitly specified as the
+#' left-hand side of the formula. In `brms`, the response variable is always
+#' specified as the left-hand side of the first formula, which implicitly means
+#' that any predictors in the first formula are predictors of the `mu` parameter
+#' of the model. In general, measurement models do not all have a `mu`
+#' parameter, therefore it is more straigthforward to explicitely predict each
+#' parameter of the model.
+#'
+#' For example, in the following `brms` formula for the drift diffusion model,
+#' the first line corresponds to the drift rate parameter, but this is not
+#' explicitely stated.
+#'
+#'
+#'           brmsformula(rt | dec(response) ~ condition + (condition | id),
+#'                       bs ~ 1 + (1 | id),
+#'                       ndt ~ 1 + (1 | id),
+#'                       bias ~ 1 + (1 | id))
+#'
+#'
+#'
+#'
+#' In `bmm`, the same formula would be written as:
+#'
+#'           bmmformula(drift ~ condition + (condition | id),
+#'                      bs ~ 1 + (1 | id),
+#'                      ndt ~ 1 + (1 | id),
+#'                      bias ~ 1 + (1 | id))
+#'
+#' and the rt and response variables would be specified in the model argument of
+#' the `fit_model` function.
+#'
+#' Aside from that, the `bmm` formula syntax is the same as the `brms` formula
+#' syntax. For more information on the `brms` formula syntax, see
+#' [brms::brmsformula()].
+#'
+#' You can also use the `bmf()` function as a shorthand for `bmmformula()`.
+#'
+#'
+#' @param formula Formula for predicting a `bmmmodel` parameter.
+#' @param ... Additional formulas for more than a single model parameter.
+#'
+#' @return A list of formulas for each parameters being predicted
+#' @export
+#' @examples
+#' imm_formula <- bmmformula(
+#'   c ~ 0 + setsize + (0 + setsize | id),
+#'   a ~ 1,
+#'   kappa ~ 0 + setsize + (0 + setsize | id)
+#' )
+#'
+#' # or use the shorter alias 'bmf'
+#' imm_formula2 <- bmf(
+#'   c ~ 0 + setsize + (0 + setsize | id),
+#'   a ~ 1,
+#'   kappa ~ 0 + setsize + (0 + setsize | id)
+#' )
+#' identical(imm_formula, imm_formula2)
+#'
+#'
+bmmformula <- function(formula, ...){
+  # paste formulas into a single list
+  dots <- list(...)
+  formula <- list(formula)
+  formula <- c(formula, dots)
+
+  # extract parameter names
+  pform_names <- character()
+  for (i in 1:length(formula)) {
+    form_line <- formula[[i]]
+    parm <- formula.tools::lhs(form_line)
+    pform_names <- c(pform_names, as.character(parm))
+  }
+
+  # label the different formulas according to the parameter predicted
+  names(formula) <- pform_names
+  return(formula)
+}
+
+
+# alias of bmmformula
+#' @export
+bmf <- function(formula, ...) {
+  brmsformula(bmmformula(formula, ...))
+}

R/helpers-formula.R

@@ -41,89 +41,3 @@ get_response <- function(formula) {
   response <- attr(tt, "response") # index of response var
   vars[response]
 }
-
-
-#' @title Create formula for predicting parameters of a `bmmmodel`
-#'
-#' @description This function is used to specify the formulas predicting the
-#' different parameters of a `bmmmodel`.
-#'
-#' @details # General formula structure
-#'
-#' The formula argument accepts formulas of the following syntax:
-#'
-#'   ``` r
-#'   parameter ~ fixed_effects + (random_effects | grouping_variable)
-#'   ```
-#'
-#' `bmm` formulas are built on `brms` formulas and function in nearly the same
-#' way, so you can use most of the `brms` formula syntax. The main differences
-#' is that in `bmm` formulas, the response variable is not specified in the
-#' formula. Instead, each parameter of the model is explicitly specified as the
-#' left-hand side of the formula. In `brms`, the response variable is always
-#' specified as the left-hand side of the first formula, which implicitly means
-#' that any predictors in the first formula are predictors of the `mu` parameter
-#' of the model. In general, measurement models do not all have a `mu`
-#' parameter, therefore it is more straigthforward to explicitely predict each
-#' parameter of the model.
-#'
-#' For example, in the following `brms` formula for the drift diffusion model,
-#' the first line corresponds to the drift rate parameter, but this is not
-#' explicitely stated.
-#'
-#'
-#'           brmsformula(rt | dec(response) ~ condition + (condition | id),
-#'                       bs ~ 1 + (1 | id),
-#'                       ndt ~ 1 + (1 | id),
-#'                       bias ~ 1 + (1 | id))
-#'
-#'
-#'
-#'
-#' In `bmm`, the same formula would be written as:
-#'
-#'           bmmformula(drift ~ condition + (condition | id),
-#'                      bs ~ 1 + (1 | id),
-#'                      ndt ~ 1 + (1 | id),
-#'                      bias ~ 1 + (1 | id))
-#'
-#' and the rt and response variables would be specified in the model argument of
-#' the `fit_model` function.
-#'
-#' Aside from that, the `bmm` formula syntax is the same as the `brms` formula
-#' syntax. For more information on the `brms` formula syntax, see
-#' [brms::brmsformula()].
-#'
-#'
-#' @param formula Formula for predicting a `bmmmodel` parameter.
-#' @param ... Additional formulas for more than a single model parameter.
-#'
-#' @return A list of formulas for each parameters being predicted
-#' @export
-#' @examples
-#' imm_formula <- bmmformula(
-#'   c ~ 0 + setsize + (0 + setsize | id),
-#'   a ~ 1,
-#'   kappa ~ 0 + setsize + (0 + setsize | id)
-#' )
-#'
-#' imm_formula
-#'
-bmmformula <- function(formula, ...){
-  # paste formulas into a single list
-  dots <- list(...)
-  formula <- list(formula)
-  formula <- c(formula, dots)
-
-  # extract parameter names
-  pform_names <- character()
-  for (i in 1:length(formula)) {
-    form_line <- formula[[i]]
-    parm <- formula.tools::lhs(form_line)
-    pform_names <- c(pform_names, as.character(parm))
-  }
-
-  # label the different formulas according to the parameter predicted
-  names(formula) <- pform_names
-  return(formula)
-}