One more pass for language

R/data.R

@@ -5,15 +5,14 @@
 #' considerations for measuring the effective reproductive number, Rt."
 #' PLoS Computational Biology 16.12 (2020): e1008409. The data are simulated
 #' from a stochastic SEIR compartmental model. The original timeseries and Rt
-#' are available in the `incidence` and `true_rt` columns and with other
-#' added or modified to increase noise and add a day-of-week effect.
+#' are available in the `incidence` and `true_rt` columns and with additional
+#' columns added or modified to increase noise and add a day-of-week effect.
 #'
 #' This synthetic dataset has a number of desirable properties:
 #'
 #' 1. The force of infection changes depending on the Rt, allowing for sudden
 #' changes in the Rt. This allows for modeling of sudden changes in infection
-#' dynamics, which might otherwise be difficult to capture. Rt estimation
-#' framework
+#' dynamics, which might otherwise be difficult to capture.
 #'
 #' 2. The realized Rt is known at each timepoint
 #'
@@ -24,24 +23,24 @@
 #' frameworks, providing practical guidance on how to use this dataset to
 #' evaluate Rt estimates.
 #'
-#' In practice, we've found that the amount of observation noise in the
-#' incidence and/or observed cases is often undesirably low for testing. Many
-#' empirical datasets are much noisier. As a result, models built with these
-#' settings in mind can perform poorly on this dataset or fail to converge. We
-#' manually add observation noise and a day-of-week reporting effect.
+#' In practice, we've found that the amount of observation noise is often
+#' undesirably low for testing. Many empirical datasets are much noisier. As a
+#' result, models built with these realistically noisy settings in mind can
+#' perform poorly on this dataset or fail to converge. To better reflect realistic
+#' settings, we manually add observation noise and a day-of-week reporting effect.
 #'
 #' @name stochastic_sir_rt
 #' @format `stochastic_sir_rt` A data frame with 299 rows and 6 columns:
 #' \describe{
-#'    \item{reference_date}{The date the cases were observed.}
+#'    \item{reference_date}{The date cases were observed.}
 #'    \item{true_rt}{The known, true Rt of the epidemic system.}
 #'    \item{dow}{The magnitude of the day-of-week effect added to the log of `incidence`.}
-#'    \item{incidence}{The true number of cases occurring on the `date` in the
-#'       simulated system.}
+#'    \item{true_cases}{The true number of cases occurring on the `date` in the
+#'       simulated system before observation noise or day-of-week effects.}
 #'    \item{obs_cases}{The observed number of cases on `date` after the day-of-week
-#'      reporting effect and observation noise have been applied to `incidence`.}
+#'      reporting effect and observation noise have been applied to `true_cases`.}
 #'    \item{obs_cases_no_dow}{The observed number of cases on `date` after observation
-#'      noise has been applied to `incidence`. It does not include a day of week effect.}
+#'      noise has been applied to `true_cases`. It does not include a day of week effect.}
 #' }
 #' @source
 #' <https://github.com/cobeylab/Rt_estimation/tree/d9d8977ba8492ac1a3b8287d2f470b313bfb9f1d>