getting started first draft

Author: dsweber2

R/flatline_forecaster.R

@@ -16,7 +16,7 @@
 #'
 #' @param epi_data An [epiprocess::epi_df][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
-#' @param args_list A list of dditional arguments as created by the
+#' @param args_list A list of additional arguments as created by the
 #'   [flatline_args_list()] constructor function.
 #'
 #' @return A data frame of point (and optionally interval) forecasts at a single

README.Rmd

@@ -169,7 +169,11 @@ cases_deaths |>
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -181,7 +185,7 @@ make it actually usable; we'll use some utilities from
 First, to eliminate some of the noise coming from daily reporting, we do 7 day
 averaging over a trailing window[^1]:
 
-[^1]: This makes it so that any given day of the processed timeseries only
+[^1]: This makes it so that any given day of the processed time-series only
     depends on the previous week, which means that we avoid leaking future
     values when making a forecast.
 
@@ -236,16 +240,21 @@ forecast_date_label <-
     geo_value = rep(used_locations, 2),
     .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
-    heights = c(rep(150, 4), rep(1.0, 4))
+    heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  cases_deaths |>
+  covid_case_death_rates |>
+  filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free"
+  ) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -273,7 +282,8 @@ four_week_ahead <- arx_forecaster(
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 4 * 7
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
   )
 )
 four_week_ahead
@@ -288,7 +298,7 @@ predicted values (and prediction intervals) for each location 28 days after the
 forecast date.
 Plotting the prediction intervals on our subset above[^2]: 
 
-[^2]: Alternatively, you could call `auto_plot(four_week_ahead)` to get the full
+[^2]: Alternatively, you could call `autoplot(four_week_ahead)` to get the full
     collection of forecasts. This is too busy for the space we have for plotting
     here.
 
@@ -318,8 +328,15 @@ forecast_plot <-
 forecast_plot
 ```
 
+And as a tibble of quantile level-value pairs:
+```{r pivot_wider}
+four_week_ahead$predictions |>
+  select(-.pred) |>
+  pivot_quantiles_longer(.pred_distn)
+```
+
 The black dot gives the median prediction, while the blue intervals give the
-25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges.
+25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges[^4].
 For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
@@ -334,7 +351,10 @@ A couple of things to note:
 ## Getting Help
 If you encounter a bug or have a feature request, feel free to file an [issue on
 our github page](https://github.com/cmu-delphi/epipredict/issues).
-For other
-questions, feel free to reach out to the authors, either via this [contact
+For other questions, feel free to reach out to the authors, either via this
+[contact
 form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
 email, or the Insightnet slack.
+
+[^4]: Note that these are not the same quantiles that we fit when creating
+    `four_week_ahead`. They are extrapolated from those quantiles using `extrapolate_quantiles()` (which assumes an exponential decay in the tails).

README.md

@@ -102,7 +102,10 @@ cases_deaths |>
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free") +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -171,16 +174,19 @@ forecast_date_label <-
     geo_value = rep(used_locations, 2),
     .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
-    heights = c(rep(150, 4), rep(1.0, 4))
+    heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  cases_deaths |>
+  covid_case_death_rates |> filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -206,13 +212,14 @@ four_week_ahead <- arx_forecaster(
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 4 * 7
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
   )
 )
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-01-27 16:36:10.
+#> This forecaster was fit on 2025-01-31 10:46:32.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -265,9 +272,27 @@ forecast_plot <-
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
 
+And as a tibble of quantile level-value pairs:
+
+``` r
+four_week_ahead$predictions |>
+  select(-.pred) |>
+  pivot_quantiles_longer(.pred_distn)
+#> # A tibble: 20 × 5
+#>   geo_value values quantile_levels forecast_date target_date
+#>   <chr>      <dbl>           <dbl> <date>        <date>     
+#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
+#> # ℹ 14 more rows
+```
+
 The black dot gives the median prediction, while the blue intervals give
-the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges. For this
-particular day and these locations, the forecasts are relatively
+the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^4]. For
+this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
 
@@ -289,13 +314,18 @@ questions, feel free to reach out to the authors, either via this
 form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
 email, or the Insightnet slack.
 
-[^1]: This makes it so that any given day of the processed timeseries
+[^1]: This makes it so that any given day of the processed time-series
     only depends on the previous week, which means that we avoid leaking
     future values when making a forecast.
 
 [^2]: lagged by 3 in this context meaning using the value from 3 days
     ago.
 
-[^3]: Alternatively, you could call `auto_plot(four_week_ahead)` to get
+[^3]: Alternatively, you could call `autoplot(four_week_ahead)` to get
     the full collection of forecasts. This is too busy for the space we
     have for plotting here.
+
+[^4]: Note that these are not the same quantiles that we fit when
+    creating `four_week_ahead`. They are extrapolated from those
+    quantiles using `extrapolate_quantiles()` (which assumes an
+    exponential decay in the tails).

_pkgdown.yml

@@ -15,6 +15,7 @@ articles:
       - backtesting
       - arx-classifier
       - update
+      - guts
   - title: Advanced methods
     contents:
       - articles/smooth-qr
@@ -47,6 +48,7 @@ reference:
     contents:
       - contains("args_list")
       - contains("_epi_workflow")
+
   - title: Helper functions for Hub submission
     contents:
       - flusight_hub_formatter

inst/pkgdown-watch.R

@@ -13,6 +13,7 @@
 rlang::check_installed(c("pkgdown", "servr", "devtools", "here", "cli", "fs"))
 library(pkgdown)
 pkg <- pkgdown::as_pkgdown(here::here())
+devtools::build_readme()
 pkgdown::build_articles(pkg)
 pkgdown::build_site(pkg, lazy = FALSE, examples = FALSE, devel = TRUE, preview = FALSE)
 
@@ -55,7 +56,6 @@ servr::httw(
     readme <- grep("README[.]rmd$", files, value = TRUE, ignore.case = TRUE)
     if (length(readme)) {
       devtools::build_readme()
-      pkgdown::build_home()
       pkgdown::build_site(pkg, lazy = TRUE, examples = FALSE, devel = TRUE, preview = FALSE)
     }
 

man/figures/README-date-1.png

@@ -1,6 +1,6 @@
 library(dplyr)
 # Test ideas that were dropped:
-# - "epi_adjust_latency works correctly when there's gaps in the timeseries"
+# - "epi_adjust_latency works correctly when there's gaps in the time-series"
 # - "epi_adjust_latency extend_ahead uses the same adjustment when predicting on new data after being baked"
 # - "`step_adjust_latency` only allows one instance of itself"
 # - "data with epi_df shorn off works"