get ebole vignette passing by checking pp and related inputs

R/marginal_model.R

@@ -144,7 +144,7 @@ epidist_formula_model.epidist_marginal_model <- function(
   # data is only used to dispatch on
   formula <- stats::update(
     formula, delay_lwr | weights(n) +
-      vreal(delay_upr, relative_obs_time, pwindow, swindow) ~ .
+      vreal(relative_obs_time, pwindow, swindow, delay_upr) ~ .
   )
   return(formula)
 }

inst/stan/marginal_model/functions.stan

@@ -11,18 +11,17 @@
   *
   * @param y Real value of observed delay
   * @param dpars_A Distribution parameters (replaced via regex)
-  * @param y_upper Upper bound of delay interval
   * @param relative_obs_t Observation time relative to primary window start
   * @param pwindow_width Primary window width (actual time scale)
   * @param swindow_width Secondary window width (actual time scale)
+  * @param y_upper Upper bound of delay interval
   * @param primary_params Array of parameters for primary distribution
   *
   * @return Log probability mass with censoring adjustment for marginal model
   */
-  real marginal_family_lpmf(data int y, dpars_A, data real y_upper,
-                            data real relative_obs_t, data real pwindow_width,
-                            data real swindow_width,
-                            array[] real primary_params) {
+  real marginal_family_lpmf(data int y, dpars_A, data real relative_obs_t,
+                            data real pwindow_width, data real swindow_width,
+                            data real y_upper, array[] real primary_params) {
 
   return primarycensored_lpmf(
       y | dist_id, {dpars_B}, pwindow_width, y_upper, relative_obs_t,

vignettes/ebola.Rmd

@@ -406,20 +406,18 @@ In this section, we demonstrate how to produce either a discrete probability mas
 ### Discrete probability mass function
 
 To generate a discrete probability mass function (PMF) we predict the delay distribution that would be observed with daily censoring and no right truncation.
-To do this, we set each of `pwindow` and `swindow` to 1 for daily censoring, and `relative_obs_time` to 1000 for no censoring.
+To do this, we set each of `pwindow` and `swindow` to 1 for daily censoring, and `relative_obs_time` to `Inf` for no censoring.
 Figure \@ref(fig:pmf) shows the result, where the few delays greater than 30 are omitted from the figure.
 
 ```{r}
 add_marginal_pmf_vars <- function(data) {
   data |>
     mutate(
-      relative_obs_time = 1000, pwindow = 1, swindow = 1,
-      delay_upr = .data$delay_lwr + .data$swindow
+      relative_obs_time = Inf, pwindow = 1, swindow = 1, delay_upr = NA
     )
 }
 
 draws_pmf <- obs_prep |>
-  as.data.frame() |>
   add_marginal_pmf_vars() |>
   add_predicted_draws(fit, ndraws = 1000)
 
@@ -430,7 +428,6 @@ pmf_base_figure <- ggplot(draws_pmf, aes(x = .prediction)) +
   theme_minimal()
 
 draws_sex_pmf <- obs_prep |>
-  as.data.frame() |>
   data_grid(sex) |>
   add_marginal_pmf_vars() |>
   add_predicted_draws(fit_sex, ndraws = 1000)
@@ -444,7 +441,6 @@ pmf_sex_figure <- draws_sex_pmf |>
   theme_minimal()
 
 draws_sex_district_pmf <- obs_prep |>
-  as.data.frame() |>
   data_grid(sex, district) |>
   add_marginal_pmf_vars() |>
   add_predicted_draws(fit_sex_district, ndraws = 1000)
@@ -485,13 +481,11 @@ That is to produce continuous delay times (Figure \@ref(fig:pdf)):
 add_marginal_pdf_vars <- function(data) {
   data |>
     mutate(
-      relative_obs_time = 1000, pwindow = 0, swindow = 0,
-      delay_upr = .data$delay_lwr + .data$swindow
+      relative_obs_time = Inf, pwindow = 0, swindow = 0, delay_upr = NA
     )
 }
 
 draws_pdf <- obs_prep |>
-  as.data.frame() |>
   add_marginal_pdf_vars() |>
   add_predicted_draws(fit, ndraws = 1000)
 
@@ -502,7 +496,6 @@ pdf_base_figure <- ggplot(draws_pdf, aes(x = .prediction)) +
   theme_minimal()
 
 draws_sex_pdf <- obs_prep |>
-  as.data.frame() |>
   data_grid(sex) |>
   add_marginal_pdf_vars() |>
   add_predicted_draws(fit_sex, ndraws = 1000)
@@ -516,7 +509,6 @@ pdf_sex_figure <- draws_sex_pdf |>
   theme_minimal()
 
 draws_sex_district_pdf <- obs_prep |>
-  as.data.frame() |>
   data_grid(sex, district) |>
   add_marginal_pdf_vars() |>
   add_predicted_draws(fit_sex_district, ndraws = 1000)