clean_baseline_perc-methods: 'clean_baseline_perc'

In nandadorea/vetsyn: Tools for Syndromic Surveillance Implementation

clean_baseline_perc

Description

Function to retrospectively remove possible outbreak signals and excessive noise, producing an outbreak free baseline that will serve to train outbreak-signal detection algorithms during prospective analysis.

Usage

clean_baseline_perc(x, ...)

## S4 method for signature 'syndromicD'
clean_baseline_perc(x, syndromes = NULL,
  limit = 0.95, run.window = 120, plot = TRUE)

## S4 method for signature 'syndromicW'
clean_baseline_perc(x, syndromes = NULL,
  limit = 0.95, run.window = 120, plot = TRUE)

Arguments

x	a syndromic (syndromicD or syndromicW) object, which must have at least the slot of observed data and a data frame in the slot dates.
...	Additional arguments to the method.
syndromes	an optional parameter, if not specified, all columns in the slot observed of the syndromic object will be used. The user can choose to restriict the analyses to a few syndromic groups listing their name or column position in the observed matrix. See examples.
limit	the percentile to be used in identifying outliers.
run.window	the number of time points to construct the moving percentile window. By default 120 days.
plot	whether plots comparing observed data and the result of the cleaning process should be displayed.

Details

The cleaning is non-parametric, based on moving percentiles. The user sets a window of time points, around each time point, which will be used to calculate the percentile set in the user in the argument limit. Any observations falling outside that percentile are removed and substituted by the percentile itself. See examples and references. See the package caTools, function runquantile() for details of how the running quantiles function handles the beginning and end of the time series.

Value

An object of the class syndromic (syndromicD or syndromicW) which contains all elements from the object provided in x, but in which the slot baseline has been filled with an outbreak-free baseline for each syndromic group. When the user chooses to restrict analyses to some syndromes, the remaining columns are kept as is (if the slot was not empty) or filled with NAs when previously empty.

References

Fernanda C. Dorea, Crawford W. Revie, Beverly J. McEwen, W. Bruce McNab, David Kelton, Javier Sanchez (2012). Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance. Preventive Veterinary Medicine. DOI: 10.1016/j.prevetmed.2012.10.010.

Examples

 ##Examples for DAILY data
 
data(lab.daily)
my.syndromicD <- raw_to_syndromicD (id=SubmissionID,
                                 syndromes.var=Syndrome,
                                 dates.var=DateofSubmission,
                                 date.format="%d/%m/%Y",
                                 data=lab.daily)
my.syndromicD <- clean_baseline_perc(my.syndromicD)
my.syndromicD <- clean_baseline_perc(my.syndromicD,run.window=90)
my.syndromicD <- clean_baseline_perc(my.syndromicD,
                              syndromes="Musculoskeletal")
my.syndromicD <- clean_baseline_perc(my.syndromicD,
                              syndromes=c("GIT","Musculoskeletal"))
my.syndromicD <- clean_baseline_perc(my.syndromicD,
                              syndromes=3)
my.syndromicD <- clean_baseline_perc(my.syndromicD,
                              syndromes=c(1,3))
                              
## Examples for WEEKLY data
data(lab.daily)
my.syndromicW <- raw_to_syndromicW (id=SubmissionID,
                                 syndromes.var=Syndrome,
                                 dates.var=DateofSubmission,
                                 date.format="%d/%m/%Y",
                                 data=lab.daily)
my.syndromicW <- clean_baseline_perc(my.syndromicW)
my.syndromicW <- clean_baseline_perc(my.syndromicW,run.window=90)
my.syndromicW <- clean_baseline_perc(my.syndromicW,
                              syndromes="Musculoskeletal")
my.syndromicW <- clean_baseline_perc(my.syndromicW,
                              syndromes=c("GIT","Musculoskeletal"))
my.syndromicW <- clean_baseline_perc(my.syndromicW,
                              syndromes=3)
my.syndromicW <- clean_baseline_perc(my.syndromicW,
                              syndromes=c(1,3))

nandadorea/vetsyn documentation built on April 30, 2022, 1:15 a.m.