changed argument precision to resolution to close #142

R/amend.R

@@ -27,7 +27,7 @@
 #'
 #' @export
 amend <- function(object, drop, add, loo_controls, x_range = NA,
-                  precision = 1000, sig_val = 0.01, priors) {
+                  resolution = 1000, sig_val = 0.01, priors) {
   UseMethod("amend")
 }
 
@@ -46,7 +46,7 @@ amend <- function(object, drop, add, loo_controls, x_range = NA,
 #'
 #' @export
 amend.bayesmanecfit <- function(object, drop, add, loo_controls, x_range = NA,
-                                precision = 1000, sig_val = 0.01, priors) {
+                                resolution = 1000, sig_val = 0.01, priors) {
   general_error <- paste(
     "Nothing to amend, please specify a proper model to either add or drop, or",
     "changes to loo_controls;\n Returning original model set."
@@ -60,7 +60,7 @@ amend.bayesmanecfit <- function(object, drop, add, loo_controls, x_range = NA,
   if (!missing(drop)) {chk_character(drop)}
   if (!missing(add)) {chk_character(add)}
   if (!is.na(x_range[1])) {chk_numeric(x_range)}
-  chk_numeric(precision)
+  chk_numeric(resolution)
   chk_numeric(sig_val)
   if(!inherits(object, "bayesmanecfit")){ 
     stop("object is not of class bayesmanecfit")
@@ -151,13 +151,13 @@ amend.bayesmanecfit <- function(object, drop, add, loo_controls, x_range = NA,
   }
   formulas <- lapply(mod_fits, extract_formula)
   mod_fits <- expand_manec(mod_fits, formula = formulas, x_range = x_range,
-                           precision = precision, sig_val = sig_val,
+                           resolution = resolution, sig_val = sig_val,
                            loo_controls = loo_controls)
   if (length(mod_fits) > 1) {
     allot_class(mod_fits, c("bayesmanecfit", "bnecfit"))
   } else {
     mod_fits <- expand_nec(mod_fits[[1]], formula = formula, x_range = x_range,
-                           precision = precision, sig_val = sig_val,
+                           resolution = resolution, sig_val = sig_val,
                            loo_controls = loo_controls, model = names(mod_fits))
     allot_class(mod_fits, c("bayesnecfit", "bnecfit"))
   }

R/average_estimates.R

@@ -44,7 +44,7 @@
 average_estimates <- function(x, estimate = "nec", ecx_val = 10,
                               posterior = FALSE, type = "absolute",
                               hormesis_def = "control", sig_val = 0.01,
-                              precision = 1000, x_range = NA, xform = identity,
+                              resolution = 1000, x_range = NA, xform = identity,
                               prob_vals = c(0.5, 0.025, 0.975)) {
   if (!is.list(x) | is.null(names(x))) {
     stop("Argument x must be a named list")
@@ -57,7 +57,7 @@ average_estimates <- function(x, estimate = "nec", ecx_val = 10,
   chk_character(hormesis_def)
   chk_numeric(ecx_val)
   chk_numeric(sig_val)
-  chk_numeric(precision)
+  chk_numeric(resolution)
   if (!inherits(xform, "function")) {
     stop("xform must be a function.")
   }
@@ -69,13 +69,13 @@ average_estimates <- function(x, estimate = "nec", ecx_val = 10,
     posterior_list <- lapply(x, return_nec_post, xform = xform)
   }
   if (estimate == "ecx") {
-    posterior_list <- lapply(x, ecx, ecx_val = ecx_val, precision = precision,
+    posterior_list <- lapply(x, ecx, ecx_val = ecx_val, resolution = resolution,
                              posterior = TRUE, type = type,
                              hormesis_def = hormesis_def, x_range = x_range,
                              xform = xform)
   }
   if (estimate == "nsec") {
-    posterior_list <- lapply(x, nsec, sig_val = sig_val, precision = precision,
+    posterior_list <- lapply(x, nsec, sig_val = sig_val, resolution = resolution,
                              posterior = TRUE, hormesis_def = hormesis_def,
                              x_range = x_range, xform = xform)
   }

R/bayesmanecfit-class.R

@@ -29,7 +29,7 @@
 #' @slot w_residuals Model-weighted residual values
 #' (i.e. observed - w_predicted_y).
 #' @slot w_pred_vals A \code{\link[base]{list}} containing model-weighted
-#' posterior predicted values based on the supplied \code{precision} and
+#' posterior predicted values based on the supplied \code{resolution} and
 #' \code{x_range}.
 #' @slot w_nec The summary stats (median and 95% credibility intervals) of
 #' w_nec_posterior.

R/bayesnecfit-class.R

@@ -23,7 +23,7 @@
 #' \code{\link[stats]{formula}}.
 #' @slot pred_vals A \code{\link[base]{list}} containing a
 #' \code{\link[base]{data.frame}} of summary posterior predicted values
-#' and a vector containing based on the supplied \code{precision} and
+#' and a vector containing based on the supplied \code{resolution} and
 #' \code{x_range}.
 #' @slot top The estimate for parameter "top" in the fitted model.
 #' @slot beta The estimate for parameter "beta" in the fitted model.

R/bnec.R

@@ -10,7 +10,7 @@
 #' the \code{formula}.
 #' @param x_range A range of predictor values over which to consider extracting
 #' ECx.
-#' @param precision The length of the predictor vector used for posterior
+#' @param resolution The length of the predictor vector used for posterior
 #' predictions, and over which to extract ECx values. Large values will be
 #' slower but more precise.
 #' @param sig_val Probability value to use as the lower quantile to test
@@ -191,10 +191,10 @@
 #' @importFrom chk chk_number
 #'
 #' @export
-bnec <- function(formula, data, x_range = NA, precision = 1000, sig_val = 0.01,
+bnec <- function(formula, data, x_range = NA, resolution = 1000, sig_val = 0.01,
                  loo_controls, x_var = NULL, y_var = NULL, trials_var = NULL,
                  model = NULL, random = NULL, random_vars = NULL, ...) {
-  chk_number(precision)
+  chk_number(resolution)
   chk_number(sig_val)
 
   mf <- match.call(expand.dots = FALSE)
@@ -232,13 +232,13 @@ bnec <- function(formula, data, x_range = NA, precision = 1000, sig_val = 0.01,
     }
     formulas <- lapply(mod_fits, extract_formula)
     mod_fits <- expand_manec(mod_fits, formula = formulas, x_range = x_range,
-                             precision = precision, sig_val = sig_val,
+                             resolution = resolution, sig_val = sig_val,
                              loo_controls = loo_controls)
     if (length(mod_fits) > 1) {
       allot_class(mod_fits, c("bayesmanecfit", "bnecfit"))
     } else {
       mod_fits <- expand_nec(mod_fits[[1]], formula = formula,
-                             x_range = x_range, precision = precision,
+                             x_range = x_range, resolution = resolution,
                              sig_val = sig_val, loo_controls = loo_controls,
                              model = names(mod_fits))
       allot_class(mod_fits, c("bayesnecfit", "bnecfit"))
@@ -247,7 +247,7 @@ bnec <- function(formula, data, x_range = NA, precision = 1000, sig_val = 0.01,
     mod_fit <- fit_bayesnec(formula = formula, data = data, model = model,
                             brm_args = brm_args)
     mod_fit <- expand_nec(mod_fit, formula = formula, x_range = x_range,
-                          precision = precision, sig_val = sig_val,
+                          resolution = resolution, sig_val = sig_val,
                           loo_controls = loo_controls, model = model)
     allot_class(mod_fit, c("bayesnecfit", "bnecfit"))
   }

R/bnec_newdata.R

@@ -4,7 +4,7 @@
 #'
 #' @param x An object of class \code{\link{bayesnecfit}} or
 #' \code{\link{bayesmanecfit}} as returned by \code{\link{bnec}}.
-#' @param precision A \code{\link[base]{numeric}} vector of length 1 indicating
+#' @param resolution A \code{\link[base]{numeric}} vector of length 1 indicating
 #' the number of x values over which to predict values.
 #' @param x_range A \code{\link[base]{numeric}} vector of length 2 indicating
 #' the range of x values over which to make predictions.
@@ -15,16 +15,16 @@
 #' \dontrun{
 #' library(bayesnec)
 #' nec4param <- pull_out(manec_example, model = "nec4param")
-#' # Make fine precision, predict out of range
-#' newdata <- bnec_newdata(nec4param, precision = 200, x_range = c(0, 4))
+#' # Make fine resolution, predict out of range
+#' newdata <- bnec_newdata(nec4param, resolution = 200, x_range = c(0, 4))
 #' nrow(newdata) == 200
 #' all(range(newdata$x) == c(0, 4))
 #' newdata2 <- bnec_newdata(manec_example) # default size
 #' nrow(newdata2) == 100
 #' }
 #' 
 #' @export
-bnec_newdata <- function(x, precision = 100, x_range = NA) {
+bnec_newdata <- function(x, resolution = 100, x_range = NA) {
   UseMethod("bnec_newdata")
 }
 
@@ -38,16 +38,16 @@ bnec_newdata <- function(x, precision = 100, x_range = NA) {
 #' @importFrom stats model.frame
 #' @noRd
 #' @export
-bnec_newdata.bayesnecfit <- function(x, precision = 100, x_range = NA) {
-  check_args_newdata(precision, x_range)
+bnec_newdata.bayesnecfit <- function(x, resolution = 100, x_range = NA) {
+  check_args_newdata(resolution, x_range)
   data <- model.frame(x$bayesnecformula, data = x$fit$data)
   x_var <- attr(data, "bnec_pop")[["x_var"]]
   fit <- x$fit
   x_vec <- fit$data[[x_var]]
   if (any(is.na(x_range))) {
-    x_seq <- seq(min(x_vec), max(x_vec), length = precision)
+    x_seq <- seq(min(x_vec), max(x_vec), length = resolution)
   } else {
-    x_seq <- seq(min(x_range), max(x_range), length = precision)
+    x_seq <- seq(min(x_range), max(x_range), length = resolution)
   }
   newdata <- data.frame(x_seq)
   names(newdata) <- x_var
@@ -68,9 +68,9 @@ bnec_newdata.bayesnecfit <- function(x, precision = 100, x_range = NA) {
 #' @inherit bnec_newdata description return examples
 #' @noRd
 #' @export
-bnec_newdata.bayesmanecfit <- function(x, precision = 100, x_range = NA) {
+bnec_newdata.bayesmanecfit <- function(x, resolution = 100, x_range = NA) {
   model_set <- names(x$mod_fits)
   bayesnecfit_x <- pull_out(x, model = model_set[1]) |>
     suppressMessages()
-  bnec_newdata(bayesnecfit_x, precision, x_range)
+  bnec_newdata(bayesnecfit_x, resolution, x_range)
 }

R/bnecfit-methods.R

@@ -127,7 +127,7 @@ c.bnecfit <- function(x, ...) {
 #'
 #' @export
 update.bnecfit <- function(object, newdata = NULL, recompile = NULL,
-                           x_range = NA, precision = 1000, sig_val = 0.01,
+                           x_range = NA, resolution = 1000, sig_val = 0.01,
                            loo_controls, force_fit = FALSE, ...) {
   original_class <- grep("bayes", class(object), value = TRUE)
   if (!original_class %in% c("bayesnecfit", "bayesmanecfit")) {
@@ -178,7 +178,7 @@ update.bnecfit <- function(object, newdata = NULL, recompile = NULL,
   formulas <- lapply(object, extract_formula)
   if (length(object) > 1) {
     object <- expand_manec(object, formula = formulas, x_range = x_range,
-                           precision = precision, sig_val = sig_val,
+                           resolution = resolution, sig_val = sig_val,
                            loo_controls = loo_controls)
     allot_class(object, c("bayesmanecfit", "bnecfit"))
   } else if (length(object) == 1) {
@@ -187,7 +187,7 @@ update.bnecfit <- function(object, newdata = NULL, recompile = NULL,
            " specified incorrect arguments? See ?update.bnecfit")
     }
     mod_fits <- expand_nec(object[[1]], formula = formulas[[1]],
-                           x_range = x_range, precision = precision,
+                           x_range = x_range, resolution = resolution,
                            sig_val = sig_val, loo_controls = loo_controls,
                            model = names(object))
     allot_class(mod_fits, c("bayesnecfit", "bnecfit"))

R/compare_estimates.R

@@ -33,7 +33,7 @@
 #' @export
 compare_estimates <- function(x, comparison = "nec", ecx_val = 10,
                               type = "absolute", hormesis_def = "control",
-                              sig_val = 0.01, precision = 100, x_range = NA) {
+                              sig_val = 0.01, resolution = 100, x_range = NA) {
   if ((comparison %in% c("nec", "ecx", "nsec")) == FALSE) {
     stop("comparison must be one of nec, ecx or nsec.")
   }
@@ -47,7 +47,7 @@ compare_estimates <- function(x, comparison = "nec", ecx_val = 10,
          Please see ?ecx for more details.")
   }
   chk_numeric(sig_val)
-  chk_numeric(precision)
+  chk_numeric(resolution)
   if (is.na(x_range[1])) {
     x_range <- return_x_range(x)
   } else {
@@ -57,12 +57,12 @@ compare_estimates <- function(x, comparison = "nec", ecx_val = 10,
     posterior_list <- lapply(x, return_nec_post, xform = identity)
   }
   if (comparison == "ecx") {
-    posterior_list <- lapply(x, ecx, ecx_val = ecx_val, precision = precision,
+    posterior_list <- lapply(x, ecx, ecx_val = ecx_val, resolution = resolution,
                              posterior = TRUE, type = type,
                              hormesis_def = hormesis_def, x_range = x_range)
   }
   if (comparison == "nsec") {
-    posterior_list <- lapply(x, nsec, sig_val = sig_val, precision = precision,
+    posterior_list <- lapply(x, nsec, sig_val = sig_val, resolution = resolution,
                              posterior = TRUE, hormesis_def = hormesis_def,
                              x_range = x_range)
   }

R/compare_fitted.R

@@ -7,12 +7,12 @@
 #' @inheritParams compare_posterior
 #' @param make_newdata Should the
 #' user allow the package to create \code{newdata} for predictions?
-#' If so, arguments \code{precision} and \code{x_range} will be used. Defaults
+#' If so, arguments \code{resolution} and \code{x_range} will be used. Defaults
 #' to TRUE. See details.
 #'
 #' @details The argument \code{make_newdata} is relevant to those who want the
 #' package to create a data.frame from which to make predictions. This is done
-#' via \code{\link{bnec_newdata}} and uses arguments \code{precision} and
+#' via \code{\link{bnec_newdata}} and uses arguments \code{resolution} and
 #' \code{x_range}. If \code{make_newdata = FALSE} and no additional
 #' \code{newdata} argument is provided (via \code{...}), then the predictions
 #' are made for the raw data. Else, to generate predictions for a specific
@@ -42,7 +42,7 @@
 #' }
 #'
 #' @export
-compare_fitted <- function(x, precision = 50, x_range = NA,
+compare_fitted <- function(x, resolution = 50, x_range = NA,
                            make_newdata = TRUE, ...) {
   if (is.na(x_range[1])) {
     x_range <- return_x_range(x)
@@ -52,11 +52,11 @@ compare_fitted <- function(x, precision = 50, x_range = NA,
   names(posterior_list) <- names(x)
   for (i in seq_along(posterior_list)) {
     newdata_list <- newdata_eval_fitted(
-      x[[i]], precision = precision, x_range = x_range,
+      x[[i]], resolution = resolution, x_range = x_range,
       make_newdata = make_newdata, fct_eval = "compare_fitted", ...
     )
     x_vec <- newdata_list$x_vec
-    precision <- newdata_list$precision
+    resolution <- newdata_list$resolution
     dot_list$newdata <- newdata_list$newdata
     dot_list$re_formula <- newdata_list$re_formula
     dot_list$object <- x[[i]]

R/compare_posterior.R

@@ -11,7 +11,7 @@
 #' "nec", "nsec", "ecx" or "fitted".
 #' @param make_newdata Only used if \code{comparison = "fitted"}. Should the
 #' user allow the package to create \code{newdata} for predictions?
-#' If so, arguments \code{precision} and \code{x_range} will be used. Defaults
+#' If so, arguments \code{resolution} and \code{x_range} will be used. Defaults
 #' to TRUE. See details.
 #' @param ... Further arguments that control posterior predictions via
 #' \code{\link[brms]{posterior_epred}}.
@@ -37,7 +37,7 @@
 #' The argument \code{make_newdata} is only used if
 #' \code{comparison = "fitted"}. It is relevant to those who want the package
 #' to create a data.frame from which to make predictions. This is done via
-#' \code{\link{bnec_newdata}} and uses arguments \code{precision} and
+#' \code{\link{bnec_newdata}} and uses arguments \code{resolution} and
 #' \code{x_range}. If \code{make_newdata = FALSE} and no additional
 #' \code{newdata} argument is provided (via \code{...}), then the predictions
 #' are made for the raw data. Else, to generate predictions for a specific
@@ -65,7 +65,7 @@
 #' @export
 compare_posterior <- function(x, comparison = "nec", ecx_val = 10,
                               type = "absolute", hormesis_def = "control",
-                              sig_val = 0.01, precision, x_range = NA,
+                              sig_val = 0.01, resolution, x_range = NA,
                               make_newdata = TRUE, ...) {
   if (!is.list(x) | is.null(names(x))) {
     stop("Argument x must be a named list.")
@@ -74,18 +74,18 @@ compare_posterior <- function(x, comparison = "nec", ecx_val = 10,
     stop("Argument comparison must be a character vector.")
   }
   if (comparison != "fitted") {
-    if (missing(precision)) {
-      precision <- 500
+    if (missing(resolution)) {
+      resolution <- 500
     }
     out <- compare_estimates(x = x, comparison = comparison, ecx_val = ecx_val,
                              type = type, hormesis_def = hormesis_def,
-                             sig_val = sig_val, precision = precision,
+                             sig_val = sig_val, resolution = resolution,
                              x_range = x_range)
   } else {
-    if (missing(precision)) {
-      precision <- 50
+    if (missing(resolution)) {
+      resolution <- 50
     }
-    out <- compare_fitted(x = x, precision = precision, x_range = x_range,
+    out <- compare_fitted(x = x, resolution = resolution, x_range = x_range,
                           make_newdata = make_newdata, ...)
   }
   out