The method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.
Usage
short_term_trend(x, ...)
# S3 method for csfmt_rts_data_v1
short_term_trend(
x,
numerator,
denominator = NULL,
prX = 100,
trend_isoyearweeks = 6,
remove_last_isoyearweeks = 0,
forecast_isoyearweeks = trend_isoyearweeks,
numerator_naming_prefix = "from_numerator",
denominator_naming_prefix = "from_denominator",
statistics_naming_prefix = "universal",
remove_training_data = FALSE,
include_decreasing = FALSE,
alpha = 0.05,
...
)Arguments
- x
Data object
- ...
Not in use.
- numerator
Character of name of numerator
- denominator
Character of name of denominator (optional)
- prX
If using denominator, what scaling factor should be used for numerator/denominator?
- trend_isoyearweeks
Same as trend_dates, but used if granularity_geo=='isoyearweek'
- remove_last_isoyearweeks
Same as remove_last_dates, but used if granularity_geo=='isoyearweek'
- forecast_isoyearweeks
Same as forecast_dates, but used if granularity_geo=='isoyearweek'
- numerator_naming_prefix
"from_numerator", "generic", or a custom prefix
- denominator_naming_prefix
"from_denominator", "generic", or a custom prefix
- statistics_naming_prefix
"universal" (one variable for trend status, one variable for doubling dates), "from_numerator_and_prX" (If denominator is NULL, then one variable corresponding to numerator. If denominator exists, then one variable for each of the prXs)
- remove_training_data
Boolean. If TRUE, removes the training data (i.e. 1:(trend_dates-1) or 1:(trend_isoyearweeks-1)) from the returned dataset.
- include_decreasing
If true, then *_trend*_status contains the levels c("training", "forecast", "decreasing", "null", "increasing"), otherwise the levels c("training", "forecast", "notincreasing", "increasing").
- alpha
Significance level for change in trend.
Value
The original csfmt_rts_data_v1 dataset with extra columns. *_trend*_status contains a factor with levels c("training", "forecast", "decreasing", "null", "increasing"), while *_doublingdays* contains the expected number of days before the numerator doubles.
Examples
d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
res <- csalert::short_term_trend(
d,
numerator = "hospitalization_with_covid19_as_primary_cause_n",
trend_isoyearweeks = 6
)
print(res[, .(
isoyearweek,
hospitalization_with_covid19_as_primary_cause_n,
hospitalization_with_covid19_as_primary_cause_trend0_41_status
)])
#> isoyearweek hospitalization_with_covid19_as_primary_cause_n
#> 1: 2020-08 0
#> 2: 2020-09 0
#> 3: 2020-10 2
#> 4: 2020-11 50
#> 5: 2020-12 188
#> ---
#> 118: 2022-20 NA
#> 119: 2022-21 NA
#> 120: 2022-22 NA
#> 121: 2022-23 NA
#> 122: 2022-24 NA
#> hospitalization_with_covid19_as_primary_cause_trend0_41_status
#> 1: training
#> 2: training
#> 3: training
#> 4: training
#> 5: training
#> ---
#> 118: forecast
#> 119: forecast
#> 120: forecast
#> 121: forecast
#> 122: forecast