ears_t_reweighted | R Documentation |
The simple 7 day running mean version of the Early Aberration Reporting System (EARS) algorithm is extended as follows:
proper computation of the prediction interval
downweighting of previous signals, similar to the approach by Farrington (1996)
ears_t_reweighted(
ts,
alpha = 0.025,
alpha_outlier = 0.05,
k_decay = 4,
no_historic = 7L,
same_weekday_baseline = FALSE
)
ts |
A numeric vector containing the counts of the univariate time series to monitor. The last time point in ts is investigated |
alpha |
The alpha is used to compute the upper limit of the prediction interval: (1-alpha) * 100%, default: 0.025 |
alpha_outlier |
Residuals beyond 1-alpha_outlier quantile of the the t(n-k-1) distribution are downweighted, default: 0.05 |
k_decay |
Power k in the expression (r_star/r_threshold)^k determining the weight, default: 4 |
no_historic |
Number of previous values i.e -1, -2, ..., no_historic to include when computing baseline parameters, default: 7 |
same_weekday_baseline |
whether to calculate baseline using same weekdays or any day, default: FALSE |
for algorithm details see package vignette.
A dataframe containing the monitored time point, the upper limit and whether a signal is detected or not.
Michael Hoehle <https://www.math.su.se/~hoehle>
if(FALSE){
library(epitweetr)
#Running the modifies version of the ears algorithm for a particular data series
ts <- c(150, 130, 122, 160, 155, 128, 144, 125, 300, 319, 289, 277, 500)
show(ears_t_reweighted(ts))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.