R/signal_detection_hlm.R
signal_detection_hlm.RdThe method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.
signal_detection_hlm(x, ...)
# S3 method for class 'csfmt_rts_data_v1'
signal_detection_hlm(
x,
value,
baseline_isoyears = 5,
remove_last_isoyearweeks = 0,
forecast_isoyearweeks = 2,
value_naming_prefix = "from_numerator",
remove_training_data = FALSE,
...
)Data object
Not in use.
Character of name of value
Number of years in the past you want to include as baseline
Number of isoyearweeks you want to remove at the end (due to unreliable data)
Number of isoyearweeks you want to forecast into the future
"from_numerator", "generic", or a custom prefix
Boolean. If TRUE, removes the training data (i.e. 1:(trend_isoyearweeks-1)) from the returned dataset.
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.
d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
res <- csalert::signal_detection_hlm(
d,
value = "hospitalization_with_covid19_as_primary_cause_n",
baseline_isoyears = 1
)
print(res[, .(
isoyearweek,
hospitalization_with_covid19_as_primary_cause_n,
hospitalization_with_covid19_as_primary_cause_forecasted_n,
hospitalization_with_covid19_as_primary_cause_forecasted_n_forecast,
hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q50x0_n,
hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q99x5_n,
hospitalization_with_covid19_as_primary_cause_n_status
)])
#> isoyearweek hospitalization_with_covid19_as_primary_cause_n
#> <char> <int>
#> 1: 2020-08 0
#> 2: 2020-09 0
#> 3: 2020-10 2
#> 4: 2020-11 50
#> 5: 2020-12 188
#> ---
#> 114: 2022-16 137
#> 115: 2022-17 74
#> 116: 2022-18 10
#> 117: 2022-19 NA
#> 118: 2022-20 NA
#> hospitalization_with_covid19_as_primary_cause_forecasted_n
#> <int>
#> 1: 0
#> 2: 0
#> 3: 2
#> 4: 50
#> 5: 188
#> ---
#> 114: 137
#> 115: 74
#> 116: 10
#> 117: 66
#> 118: 59
#> hospitalization_with_covid19_as_primary_cause_forecasted_n_forecast
#> <lgcl>
#> 1: FALSE
#> 2: FALSE
#> 3: FALSE
#> 4: FALSE
#> 5: FALSE
#> ---
#> 114: FALSE
#> 115: FALSE
#> 116: FALSE
#> 117: TRUE
#> 118: TRUE
#> hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q50x0_n
#> <num>
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> ---
#> 114: 125
#> 115: 92
#> 116: 69
#> 117: 66
#> 118: 59
#> hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q99x5_n
#> <num>
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> ---
#> 114: 255
#> 115: 184
#> 116: 77
#> 117: 79
#> 118: 79
#> hospitalization_with_covid19_as_primary_cause_n_status
#> <fctr>
#> 1: training
#> 2: training
#> 3: training
#> 4: training
#> 5: training
#> ---
#> 114: null
#> 115: null
#> 116: null
#> 117: forecast
#> 118: forecast