prep_linelist.Rdprep_linelist() converts a linelist to an incidence curve, replaces
anomalous counts with the expected value, and decomposes the result into
trend, seasonality, and remainder components. It also filters out reporting
errors and truncates the data at the last fully observed incidence date
(as defined by pct_reported, see that parameter for details). All
calculations are performed on the log scale, but the result is returned on
the input scale (assumed linear).
prep_linelist(
.data,
.collection_date = "collection_date",
.report_date = "report_date",
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
)A data frame containing one incident observation per row
<tidy-select> A Date column to use as the
collection date of the observed case
<tidy-select> A Date column to use as the report
date of the observed case
The start date of the epidemic;
defaults to "2020-03-12", which is the beginning of the contiguous
part of Shelby County's observed cases (at least one case observed per
day since that date).
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
NULL or "auto", trend is set automatically using the tunable
heuristics in the timetk package.
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
NULL or "auto", period is set automatically using the tunable
heuristics in the timetk package.
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 NULL, delay_period is set to "14 days".
The percent of total cases reported before considering
a collection date to be fully observed. It is not recommended to set this
to 1, as reporting delays typically contain very large outliers which
will skew the results. The default is 0.9, which strikes a balance
between sensitivity and robustness in Shelby County data.
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.
Should anomalies be plotted for visual inspection? If
TRUE, the plot will be on the log-scale.
A tibble with a date column (named the same as the column specified
by .collection_date) and observed, season, trend, and remaindercolumns. All numeric columns have outlier replaced.