Description Usage Arguments Value See Also
View source: R/estimate_unboosted_rt.R
estimate_unboosted_rt()
is a wrapper around
prep_linelist()
and
estimate_rt()
. It exists purely for
convenience; see those functions for details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | estimate_unboosted_rt(
.data,
.collection_date = "collection_date",
.report_date = "report_date",
serial_interval_mean = 6,
serial_interval_sd = 4.17,
start_date = "2020-03-12",
trend = "30 days",
period = "7 days",
delay_period = "14 days",
pct_reported = 0.9,
cutoff = 0.05,
plot_anomalies = FALSE
)
|
.data |
A data frame containing one incident observation per row |
.collection_date |
|
.report_date |
|
serial_interval_mean |
The average number of days between infection of a primary case and a secondary case |
serial_interval_sd |
The standard deviation of the number of days between infection of a primary case and a secondary case |
start_date |
The start date of the epidemic;
defaults to |
trend |
The length of time to use in trend decomposition; can be a
time-based definition (e.g. "1 month") or an integer number of days. If
|
period |
The length of time to use in seasonal decomposition; can be a
time-based definition (e.g. "1 week") or an integer number of days. If
|
delay_period |
The length of time to use in calculating reporting
delay; can be a time-based definition (e.g. "2 weeks") or an integer number
of days. If |
pct_reported |
The percent of total cases reported before considering
a collection date to be fully observed. It is not recommended to set this
to |
cutoff |
The cutoff value for anomaly detection; controls both the maximum percentage of data points that may be considered anomalies, as well as the critical value for the Generalized Extreme Studentized Deviate test used to detect the anomalies. Can be interpreted as the desired maximum probability that an individual data point is labeled an anomaly. |
plot_anomalies |
Should anomalies be plotted for visual inspection? If
|
A tibble
with columns .t
, .pred
(the median), .pred_lower
(the lower bound of the 95% credible interval), .pred_upper
(the upper bound of the 95% credible interval), .mean
(the average), and
.cv
(the coefficient of variation)
prep_linelist()
,
calibrate_rt()
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