Canned median quantile

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.7
+Version: 0.1.8
 Authors@R: c(
     person("Daniel J.", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NEWS.md

@@ -25,6 +25,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 - Quantiles produced by `grf` were sometimes out of order.
 - dist_quantiles can have all `NA` values without causing unrelated errors
+- adjust default quantiles throughout so that they match.
+- force `layer_residual_quantiles()` to always include `0.5`.
 
 # epipredict 0.1
 

R/arx_forecaster.R

@@ -200,7 +200,8 @@ arx_fcast_epi_workflow <- function(
     } else {
       quantile_levels <- sort(compare_quantile_args(
         args_list$quantile_levels,
-        rlang::eval_tidy(trainer$eng_args$quantiles) %||% c(.1, .5, .9),
+        rlang::eval_tidy(trainer$eng_args$quantiles) %||%
+          c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         "grf"
       ))
       trainer$eng_args$quantiles <- rlang::enquo(quantile_levels)
@@ -253,8 +254,8 @@ arx_fcast_epi_workflow <- function(
 #'   the last day of data. For example, if the last day of data was 3 days ago,
 #'   the ahead becomes `ahead+3`.
 #'   - `"extend_lags"`: increase the lags so they're relative to the actual
-#'   forecast date. For example, if the lags are `c(0,7,14)` and the last day of
-#'   data was 3 days ago, the lags become `c(3,10,17)`.
+#'   forecast date. For example, if the lags are `c(0, 7, 14)` and the last day of
+#'   data was 3 days ago, the lags become `c(3, 10, 17)`.
 #' @param warn_latency by default, `step_adjust_latency` warns the user if the
 #'   latency is large. If this is `FALSE`, that warning is turned off.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
@@ -295,7 +296,7 @@ arx_args_list <- function(
     target_date = NULL,
     adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
     warn_latency = TRUE,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
@@ -362,7 +363,7 @@ compare_quantile_args <- function(alist, tlist, train_method = c("qr", "grf")) {
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
   default_tlist <- switch(train_method,
     "qr" = eval(formals(quantile_reg)$quantile_levels),
-    "grf" = c(.1, .5, .9)
+    "grf" = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
   )
   if (setequal(alist, default_alist)) {
     if (setequal(tlist, default_tlist)) {

R/autoplot.R

@@ -39,9 +39,7 @@ ggplot2::autoplot
 #'   step_epi_naomit()
 #'
 #' f <- frosting() %>%
-#'   layer_residual_quantiles(
-#'     quantile_levels = c(.025, .1, .25, .75, .9, .975)
-#'   ) %>%
+#'   layer_residual_quantiles() %>%
 #'   layer_threshold(starts_with(".pred")) %>%
 #'   layer_add_target_date()
 #'
@@ -85,7 +83,7 @@ NULL
 #' @rdname autoplot-epipred
 autoplot.epi_workflow <- function(
     object, predictions = NULL,
-    .levels = c(.5, .8, .95), ...,
+    .levels = c(.5, .8, .9), ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
@@ -183,7 +181,7 @@ autoplot.epi_workflow <- function(
   }
 
   if (".pred" %in% names(predictions)) {
-    ntarget_dates <- n_distinct(predictions$time_value)
+    ntarget_dates <- dplyr::n_distinct(predictions$time_value)
     if (ntarget_dates > 1L) {
       bp <- bp +
         geom_line(
@@ -231,24 +229,25 @@ starts_with_impl <- function(x, vars) {
 
 plot_bands <- function(
     base_plot, predictions,
-    levels = c(.5, .8, .95),
+    levels = c(.5, .8, .9),
     fill = "blue4",
     alpha = 0.6,
     linewidth = 0.05) {
   innames <- names(predictions)
-  n <- length(levels)
-  alpha <- alpha / (n - 1)
-  l <- (1 - levels) / 2
-  l <- c(rev(l), 1 - l)
+  n_levels <- length(levels)
+  alpha <- alpha / (n_levels - 1)
+  # generate the corresponding level that is 1 - level
+  levels <- (1 - levels) / 2
+  levels <- c(rev(levels), 1 - levels)
 
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, l), l)) %>%
+    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, levels), levels)) %>%
     pivot_quantiles_wider(.pred_distn)
   qnames <- setdiff(names(predictions), innames)
 
-  for (i in 1:n) {
+  for (i in 1:n_levels) {
     bottom <- qnames[i]
     top <- rev(qnames)[i]
     if (i == 1) {

R/extract.R

@@ -13,7 +13,7 @@
 #' @examples
 #' f <- frosting() %>%
 #'   layer_predict() %>%
-#'   layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) %>%
+#'   layer_residual_quantiles(symmetrize = FALSE) %>%
 #'   layer_naomit(.pred)
 #'
 #' extract_argument(f, "layer_residual_quantiles", "symmetrize")

R/flatline_forecaster.R

@@ -108,7 +108,7 @@ flatline_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),

R/layer_quantile_distn.R

@@ -44,7 +44,7 @@
 #' p
 layer_quantile_distn <- function(frosting,
                                  ...,
-                                 quantile_levels = c(.25, .75),
+                                 quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
                                  truncate = c(-Inf, Inf),
                                  name = ".pred_distn",
                                  id = rand_id("quantile_distn")) {

R/layer_residual_quantiles.R

@@ -3,7 +3,8 @@
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
 #' @param quantile_levels numeric vector of probabilities with values in (0,1)
-#'   referring to the desired quantile.
+#'   referring to the desired quantile. Note that 0.5 will always be included
+#'   even if left out by the user.
 #' @param symmetrize logical. If `TRUE` then interval will be symmetric.
 #' @param by_key A character vector of keys to group the residuals by before
 #'   calculating quantiles. The default, `c()` performs no grouping.
@@ -28,7 +29,7 @@
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_residual_quantiles(
-#'     quantile_levels = c(0.0275, 0.975),
+#'     quantile_levels = c(0.025, 0.975),
 #'     symmetrize = FALSE
 #'   ) %>%
 #'   layer_naomit(.pred)
@@ -48,7 +49,7 @@
 #' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     by_key = character(0L),
     name = ".pred_distn",
@@ -59,6 +60,7 @@ layer_residual_quantiles <- function(
   arg_is_chr(by_key, allow_empty = TRUE)
   arg_is_probabilities(quantile_levels)
   arg_is_lgl(symmetrize)
+  quantile_levels <- sort(unique(c(0.5, quantile_levels)))
   add_layer(
     frosting,
     layer_residual_quantiles_new(

R/make_grf_quantiles.R

@@ -141,7 +141,7 @@ make_grf_quantiles <- function() {
       data = c(x = "X", y = "Y"),
       func = c(pkg = "grf", fun = "quantile_forest"),
       defaults = list(
-        quantiles = c(0.1, 0.5, 0.9),
+        quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         num.threads = 1L,
         seed = rlang::expr(stats::runif(1, 0, .Machine$integer.max))
       )

R/make_quantile_reg.R

@@ -12,7 +12,7 @@
 #' @param engine Character string naming the fitting function. Currently, only
 #'   "rq" and "grf" are supported.
 #' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#'   quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).
 #' @param method A fitting method used by [quantreg::rq()]. See the
 #'   documentation for a list of options.
 #'
@@ -27,7 +27,9 @@
 #' rq_spec <- quantile_reg(quantile_levels = c(.2, .8)) %>% set_engine("rq")
 #' ff <- rq_spec %>% fit(y ~ ., data = tib)
 #' predict(ff, new_data = tib)
-quantile_reg <- function(mode = "regression", engine = "rq", quantile_levels = 0.5, method = "br") {
+quantile_reg <- function(mode = "regression", engine = "rq",
+                         quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
+                         method = "br") {
   # Check for correct mode
   if (mode != "regression") {
     cli_abort("`mode` must be 'regression'")

R/make_smooth_quantile_reg.R

@@ -5,12 +5,7 @@
 #' the [tidymodels](https://www.tidymodels.org/) framework. Currently, the
 #' only supported engine is [smoothqr::smooth_qr()].
 #'
-#' @param mode A single character string for the type of model.
-#'   The only possible value for this model is "regression".
-#' @param engine Character string naming the fitting function. Currently, only
-#'   "smooth_qr" is supported.
-#' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#' @inheritParams quantile_reg
 #' @param outcome_locations Defaults to the vector `1:ncol(y)` but if the
 #'   responses are observed at a different spacing (or appear in a different
 #'   order), that information should be used here. This
@@ -76,7 +71,7 @@ smooth_quantile_reg <- function(
     mode = "regression",
     engine = "smoothqr",
     outcome_locations = NULL,
-    quantile_levels = 0.5,
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     degree = 3L) {
   # Check for correct mode
   if (mode != "regression") cli_abort("`mode` must be 'regression'")