Merge pull request #342 from poissonconsulting/prop

- Soft-deprecated argument percent = 5 for proportion = 0.05

R/bcanz.R

@@ -70,7 +70,7 @@ ssd_fit_bcanz <- function(data, left = "Conc") {
 #' ssd_hc_bcanz(fits, nboot = 100)
 ssd_hc_bcanz <- function(x, nboot = 10000, delta = 10, min_pboot = 0.9) {
   ssd_hc(x,
-    percent = c(1, 5, 10, 20),
+    proportion = c(0.01, 0.05, 0.1, 0.2),
     ci = TRUE,
     level = 0.95,
     nboot = nboot,

R/data.R

@@ -17,7 +17,7 @@
 #' A data frame of the predictions based on 1,000 bootstrap iterations.
 #'
 #' \describe{
-#'   \item{percent}{The percent of species affected (int).}
+#'   \item{proportion}{The proportion of species affected (int).}
 #'   \item{est}{The estimated concentration (dbl).}
 #'   \item{se}{The standard error of the estimate (dbl).}
 #'   \item{lcl}{The lower confidence limit (dbl).}

R/exposure.R

@@ -12,7 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 
-#' Percent Exposure
+#' Proportion Exposure
 #'
 #' Calculates average proportion exposed based on log-normal distribution of concentrations.
 #'

R/ggplot.R

@@ -164,7 +164,7 @@ geom_hcintersect <- function(mapping = NULL,
 #' @export
 #' @examples
 #' gp <- ggplot2::ggplot(boron_pred) +
-#'   geom_xribbon(ggplot2::aes(xmin = lcl, xmax = ucl, y = percent))
+#'   geom_xribbon(ggplot2::aes(xmin = lcl, xmax = ucl, y = proportion))
 geom_xribbon <- function(mapping = NULL,
                          data = NULL,
                          stat = "identity",

R/hc-burrlioz.R

@@ -25,13 +25,25 @@
 #' ssd_hc_burrlioz(fit)
 #'
 #' @export
-ssd_hc_burrlioz <- function(x, percent = 5, ci = FALSE, level = 0.95, nboot = 1000,
+ssd_hc_burrlioz <- function(x, percent, proportion = 0.05, ci = FALSE, level = 0.95, nboot = 1000,
                             min_pboot = 0.99, parametric = FALSE) {
   lifecycle::deprecate_warn("0.3.5", "ssd_hc_burrlioz()", "ssd_hc()")
   chk_s3_class(x, "fitburrlioz")
+
+  if(lifecycle::is_present(percent)) {
+    lifecycle::deprecate_soft("1.0.6.9009", "ssd_hc(percent)", "ssd_hc(proportion)", id = "hc")
+    chk_vector(percent)
+    chk_numeric(percent)
+    chk_range(percent, c(0, 100))
+    proportion <- percent / 100
+  }
+
+  chk_vector(proportion)
+  chk_numeric(proportion)
+  chk_range(proportion)
 
   ssd_hc(x,
-    percent = percent, ci = ci, level = level,
+    proportion = proportion, ci = ci, level = level,
     nboot = nboot, min_pboot = min_pboot, parametric = parametric
   )
 }

R/hc.R

@@ -15,7 +15,7 @@
 #' Hazard Concentrations for Species Sensitivity Distributions
 #'
 #' Calculates concentration(s) with bootstrap confidence intervals 
-#' that protect specified percentage(s) of species for 
+#' that protect specified proportion(s) of species for 
 #' individual or model-averaged distributions
 #' using parametric or non-parametric bootstrapping.
 #' 
@@ -37,9 +37,8 @@
 #' calculating the weighted arithmetic means of the lower 
 #' and upper confidence limits based on `nboot` samples for each distribution.
 #' 
-#' Based on Burnham and Anderson (2002),
-#' distributions with an absolute AIC difference greater 
-#' than a delta of by default 7 have considerably less support (weight < 0.03)
+#' Distributions with an absolute AIC difference greater 
+#' than a delta of by default 7 have considerably less support (weight < 0.01)
 #' and are excluded
 #' prior to calculation of the hazard concentrations to reduce the run time.
 #' 
@@ -62,7 +61,7 @@ ssd_hc <- function(x, ...) {
   est <- do.call(fun, args)
   tibble(
     dist = dist,
-    percent = proportion * 100, est = est,
+    proportion = proportion, est = est,
     se = NA_real_, lcl = NA_real_, ucl = NA_real_,
     wt = 1,
     nboot = 0L, pboot = NA_real_
@@ -74,19 +73,35 @@ ssd_hc <- function(x, ...) {
 #' @examples
 #' 
 #' ssd_hc(ssd_match_moments())
-ssd_hc.list <- function(x, percent = 5, ...) {
+ssd_hc.list <- function(
+    x, 
+    percent, 
+    proportion = 0.05, 
+    ...) {
   chk_list(x)
   chk_named(x)
   chk_unique(names(x))
   chk_unused(...)
 
+  if(lifecycle::is_present(percent)) {
+    lifecycle::deprecate_soft("1.0.6.9009", "ssd_hc(percent)", "ssd_hc(proportion)", id = "hc")
+    chk_vector(percent)
+    chk_numeric(percent)
+    chk_range(percent, c(0, 100))
+    proportion <- percent / 100
+  }
+
+  chk_vector(proportion)
+  chk_numeric(proportion)
+  chk_range(proportion)
+
   if (!length(x)) {
     hc <- no_hcp()
-    hc <- dplyr::rename(hc, percent = "value")
+    hc <- dplyr::rename(hc, proportion = "value")
     return(hc)
   }
   hc <- mapply(.ssd_hc_dist, x, names(x),
-               MoreArgs = list(proportion = percent / 100),
+               MoreArgs = list(proportion = proportion),
                SIMPLIFY = FALSE
   )
   bind_rows(hc)
@@ -100,7 +115,8 @@ ssd_hc.list <- function(x, percent = 5, ...) {
 #' ssd_hc(fits)
 ssd_hc.fitdists <- function(
     x, 
-    percent = 5, 
+    percent, 
+    proportion = 0.05,
     average = TRUE,
     ci = FALSE, 
     level = 0.95, 
@@ -110,20 +126,27 @@ ssd_hc.fitdists <- function(
     multi_ci = TRUE,
     weighted = TRUE,
     parametric = TRUE, 
-    delta = 7, 
+    delta = 9.21, 
     samples = FALSE,
     save_to = NULL,
     control = NULL,
     ...
 ) {
 
-  chk_vector(percent)
-  chk_numeric(percent)
-  chk_range(percent, c(0, 100))
   chk_unused(...)
 
-  proportion <- percent / 100
-
+  if(lifecycle::is_present(percent)) {
+    lifecycle::deprecate_soft("1.0.6.9009", "ssd_hc(percent)", "ssd_hc(proportion)", id = "hc")
+    chk_vector(percent)
+    chk_numeric(percent)
+    chk_range(percent, c(0, 100))
+    proportion <- percent / 100
+  }
+
+  chk_vector(proportion)
+  chk_numeric(proportion)
+  chk_range(proportion)
+
   hcp <- ssd_hcp_fitdists(
     x = x, 
     value = proportion,
@@ -142,8 +165,7 @@ ssd_hc.fitdists <- function(
     save_to = save_to,
     hc = TRUE)
 
-  hcp <- dplyr::rename(hcp, percent = "value")
-  hcp <- dplyr::mutate(hcp, percent = .data$percent * 100)
+  hcp <- dplyr::rename(hcp, proportion = "value")
   hcp
 }
 
@@ -155,7 +177,8 @@ ssd_hc.fitdists <- function(
 #' ssd_hc(fit)
 ssd_hc.fitburrlioz <- function(
     x, 
-    percent = 5, 
+    percent, 
+    proportion = 0.05,
     ci = FALSE, 
     level = 0.95, 
     nboot = 1000,
@@ -167,15 +190,22 @@ ssd_hc.fitburrlioz <- function(
   chk_length(x, upper = 1L)
   chk_named(x)
   chk_subset(names(x), c("burrIII3", "invpareto", "llogis", "lgumbel"))
-  chk_vector(percent)
-  chk_numeric(percent)
-  chk_range(percent, c(0, 100))
   chk_unused(...)
+
+  if(lifecycle::is_present(percent)) {
+    lifecycle::deprecate_soft("1.0.6.9009", "ssd_hc(percent)", "ssd_hc(proportion)", id = "hc")
+    chk_vector(percent)
+    chk_numeric(percent)
+    chk_range(percent, c(0, 100))
+    proportion <- percent / 100
+  }
+
+  chk_vector(proportion)
+  chk_numeric(proportion)
+  chk_range(proportion)
 
   fun <- if(names(x) == "burrIII3") fit_burrlioz else fit_tmb
 
-  proportion <- percent / 100
-
   hcp <-   ssd_hcp_fitdists (
     x = x,
     value = proportion, 
@@ -195,7 +225,6 @@ ssd_hc.fitburrlioz <- function(
     fix_weights = FALSE,
     fun = fun)
 
-  hcp <- dplyr::rename(hcp, percent = "value")
-  hcp <- dplyr::mutate(hcp, percent = .data$percent * 100)
+  hcp <- dplyr::rename(hcp, proportion = "value")
   hcp
 }

R/hp.R

@@ -12,16 +12,16 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 
-#' Hazard Percent
+#' Hazard Proportion
 #'
-#' Calculates percent of species affected at specified concentration(s)
+#' Calculates proportion of species affected at specified concentration(s)
 #' with quantile based bootstrap confidence intervals for 
 #' individual or model-averaged distributions
 #' using parametric or non-parametric bootstrapping.
 #' For more information see the inverse function [`ssd_hc()`].
 #'
 #' @inheritParams params
-#' @return A tibble of corresponding hazard percents.
+#' @return A tibble of corresponding hazard proportions.
 #' @seealso [`ssd_hc()`]
 #' @export
 #' @examples
@@ -31,7 +31,7 @@ ssd_hp <- function(x, ...) {
   UseMethod("ssd_hp")
 }
 
-#' @describeIn ssd_hp Hazard Percents for fitdists Object
+#' @describeIn ssd_hp Hazard Proportions for fitdists Object
 #' @export
 ssd_hp.fitdists <- function(
     x, 
@@ -45,7 +45,7 @@ ssd_hp.fitdists <- function(
     multi_ci = TRUE,
     weighted = TRUE,
     parametric = TRUE, 
-    delta = 7, 
+    delta = 9.21, 
     samples = FALSE,
     save_to = NULL,
     control = NULL,
@@ -79,7 +79,7 @@ ssd_hp.fitdists <- function(
 }
 
 
-#' @describeIn ssd_hp Hazard Percents for fitburrlioz Object
+#' @describeIn ssd_hp Hazard Proportions for fitburrlioz Object
 #' @export
 #' @examples
 #' 

R/params.R

@@ -33,7 +33,7 @@
 #' Distributions with an absolute AIC difference greater than delta are excluded from the calculations.
 #' @param digits A whole number specifying the number of significant figures.
 #' @param dists A character vector of the distribution names.
-#' @param hc A count between 1 and 99 indicating the percent hazard concentration (or NULL).
+#' @param hc A value between 0 and 1 indicating the proportion hazard concentration (or NULL).
 #' @param label A string of the column in data with the labels.
 #' @param left A string of the column in data with the concentrations.
 #' @param level A number between 0 and 1 of the confidence level of the interval.
@@ -70,8 +70,9 @@
 #' @param object The object.
 #' @param parametric A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.
 #' @param p vector of probabilities.
-#' @param percent A numeric vector of percent values to estimate hazard concentrations for.
+#' @param percent A numeric vector of percent values to estimate hazard concentrations for. Soft-deprecated for `proportion = 0.05`.
 #' @param pmix Proportion mixture parameter.
+#' @param proportion A numeric vector of proportion values to estimate hazard concentrations for.
 #' @param pvalue A flag specifying whether to return p-values or the statistics (default) for the various tests.
 #' @param pred A data frame of the predictions.
 #' @param q	vector of quantiles.

R/plot-cdf.R

@@ -30,14 +30,13 @@ ssd_plot_cdf <- function(x, ...) {
 #' @examples
 #' fits <- ssd_fit_dists(ssddata::ccme_boron)
 #' ssd_plot_cdf(fits)
-ssd_plot_cdf.fitdists <- function(x, average = FALSE, delta = 7, ...) {
-  pred <- ssd_hc(x, percent = 1:99, average = average, delta = delta)
+ssd_plot_cdf.fitdists <- function(x, average = FALSE, delta = 9.21, ...) {
+  pred <- ssd_hc(x, proportion = 1:99/100, average = average, delta = delta)
   data <- ssd_data(x)
   cols <- .cols_fitdists(x)
 
   linetype <- if (length(unique(pred$dist)) > 1) "dist" else NULL
   linecolor <- linetype
-  pred$percent <- round(pred$percent) # not sure why needed
 
   ssd_plot(
     data = data, pred = pred, left = cols$left, right = cols$right,
@@ -57,12 +56,11 @@ ssd_plot_cdf.fitdists <- function(x, average = FALSE, delta = 7, ...) {
 #'   lnorm = c(meanlog = 2, sdlog = 2)
 #' ))
 ssd_plot_cdf.list <- function(x, ...) {
-  pred <- ssd_hc(x, percent = 1:99)
+  pred <- ssd_hc(x, proportion = 1:99/100)
   data <- data.frame(Conc = numeric(0))
 
   linetype <- if (length(unique(pred$dist)) > 1) "dist" else NULL
   linecolor <- linetype
-  pred$percent <- round(pred$percent) # not sure why needed
 
   ssd_plot(
     data = data, pred = pred,