updating "inits"-->"init" argument to allow for brms and cmdstanr compatibility

Author: dbarneche

R/amend.R

@@ -95,7 +95,7 @@ amend.default <- function(object, drop, add, loo_controls, x_range = NA,
     if (!inherits(mod_m, "prebayesnecfit")) {
       brm_args <- list(
         family = family, iter = simdat$iter, thin = simdat$thin,
-        warmup = simdat$warmup, inits = simdat$inits, chains = simdat$chains,
+        warmup = simdat$warmup, init = simdat$init, chains = simdat$chains,
         sample_prior = simdat$sample_prior
       )
       if (missing(priors)) {

R/bayesnecfit-class.R

@@ -17,7 +17,7 @@
 #' @slot fit The fitted Bayesian model of class \code{\link[brms]{brmsfit}}.
 #' @slot model A \code{\link[base]{character}} string indicating the name of
 #' the fitted model.
-#' @slot inits A \code{\link[base]{list}} containing the initialisation values
+#' @slot init A \code{\link[base]{list}} containing the initialisation values
 #' for to fit the model.
 #' @slot bayesnecformula An object of class \code{\link{bayesnecformula}} and
 #' \code{\link[stats]{formula}}.

R/extract_simdat.R

@@ -6,14 +6,14 @@
 #' by \code{\link{fit_bayesnec}}.
 #'
 #' @return A \code{\link[base]{list}} of model simulation statistics including
-#' iter, thin, warmup, chains, inits and nsamples.
+#' iter, thin, warmup, chains, init and nsamples.
 #'
 #' @noRd
 extract_simdat <- function(modfit) {
   x <- modfit$fit$fit@sim
   sample_prior <- attr(modfit$fit$prior, "sample_prior")
   list(iter = x$iter, thin = x$thin, warmup = x$warmup, chains = x$chains,
-       inits = modfit$fit$fit@inits, sample_prior = sample_prior,
+       init = modfit$fit$fit@inits, sample_prior = sample_prior,
        n_samples = ceiling((x$iter - x$warmup) /
                              x$thin * x$chains))
 }

R/fit_bayesnec.R

@@ -69,7 +69,7 @@ fit_bayesnec <- function(formula, data, model = NA, brm_args,
   msg_tag <- ifelse(family$family == "custom", custom_name, family$family)
   message(paste0("Response variable modelled as a ", model, " model using a ",
                  msg_tag, " distribution."))
-  out <- list(fit = fit, model = model, inits = all_args$inits,
+  out <- list(fit = fit, model = model, init = all_args$init,
               bayesnecformula = formula)
   allot_class(out, "prebayesnecfit")
 }

R/helpers.R

@@ -478,7 +478,7 @@ add_brm_defaults <- function(brm_args, model, family, predictor, response,
   } else {
     brm_args$prior <- priors
   }
-  if (!("inits" %in% names(brm_args)) || skip_check) {
+  if (!("init" %in% names(brm_args)) || skip_check) {
     msg_tag <- ifelse(family$family == "custom", custom_name, family$family)
     message(paste0("Finding initial values which allow the response to be",
                    " fitted using a ", model, " model and a ", msg_tag,
@@ -489,7 +489,7 @@ add_brm_defaults <- function(brm_args, model, family, predictor, response,
     if (length(inits) == 1 && "random" %in% names(inits)) {
       inits <- inits$random
     }
-    brm_args$inits <- inits
+    brm_args$init <- inits
   }
   brm_args
 }

R/prebayesnecfit-class.R

@@ -17,7 +17,7 @@
 #' @slot fit The fitted Bayesian model of class \code{\link[brms]{brmsfit}}.
 #' @slot model A \code{\link[base]{character}} string indicating the name of
 #' the fitted model.
-#' @slot inits A \code{\link[base]{list}} containing the initialisation values
+#' @slot init A \code{\link[base]{list}} containing the initialisation values
 #' for to fit the model.
 #' @slot bayesnecformula An object of class \code{\link{bayesnecformula}} and
 #' \code{\link[stats]{formula}}.
@@ -50,7 +50,7 @@ is_prebayesnecfit <- function(x) {
 #' @noRd
 recover_prebayesnecfit <- function(x) {
   if (is_bayesnecfit(x)) {
-    out <- allot_class(x[c("fit", "model", "inits", "bayesnecformula")],
+    out <- allot_class(x[c("fit", "model", "init", "bayesnecformula")],
                        "prebayesnecfit")
     out <- list(out = out)
     names(out) <- out$out$model

data/manec_example.rda

@@ -7,7 +7,7 @@ fit2 <- manec_example$mod_fits[["ecx4param"]]
 test_that("expand_nec defaults work for nec model", {
   nec_fit <- expand_nec(fit1, fit1$bayesnecformula, model = "nec4param") %>%
     suppressWarnings
-  expect_equal(names(nec_fit), c("fit", "model", "inits", "bayesnecformula",
+  expect_equal(names(nec_fit), c("fit", "model", "init", "bayesnecformula",
                                  "pred_vals", "top",
                                  "beta", "nec", "f", "bot", "d",
                                  "slope", "ec50", "dispersion", "predicted_y",
@@ -23,7 +23,7 @@ test_that("expand_nec arguments work for nec model", {
   nec_fit <- expand_nec(fit1, fit1$bayesnecformula, model = "nec4param",
                         x_range = c(0.01, 4), precision = 20) %>%
     suppressWarnings
-  expect_equal(names(nec_fit), c("fit", "model", "inits", "bayesnecformula",
+  expect_equal(names(nec_fit), c("fit", "model", "init", "bayesnecformula",
                                  "pred_vals", "top",
                                  "beta", "nec", "f", "bot", "d",
                                  "slope", "ec50", "dispersion", "predicted_y",
@@ -38,7 +38,7 @@ test_that("expand_nec arguments work for nec model", {
 test_that("expand_ecx defaults work for ecx model", {
   ecx_fit <- expand_nec(fit2, fit2$bayesnecformula, model = "ecx4param") %>%
     suppressWarnings
-  expect_equal(names(ecx_fit), c("fit", "model", "inits", "bayesnecformula",
+  expect_equal(names(ecx_fit), c("fit", "model", "init", "bayesnecformula",
                                  "pred_vals", "top",
                                  "beta", "nec", "f", "bot", "d",
                                  "slope", "ec50", "dispersion", "predicted_y",
@@ -54,7 +54,7 @@ test_that("expand_ecx arguments work for ecx model", {
   ecx_fit <- expand_nec(fit2, fit2$bayesnecformula, model = "ecx4param",
                         x_range = c(0.01, 4), precision = 20) %>%
     suppressWarnings
-  expect_equal(names(ecx_fit), c("fit", "model", "inits", "bayesnecformula",
+  expect_equal(names(ecx_fit), c("fit", "model", "init", "bayesnecformula",
                                  "pred_vals", "top",
                                  "beta", "nec", "f", "bot", "d", "slope",
                                  "ec50", "dispersion", "predicted_y",