update_syndromic-methods: 'update_syndromic'

In nandadorea/vetsyn: Tools for Syndromic Surveillance Implementation

update_syndromic

Description

Updates an object of the class syndromicD or syndromicW, adding additional rows (additional time points) from raw, observed data. To understand a syndromic object, please refer to the help page for the syndromic class.

Usage

update_syndromic(x, ...)

## S4 method for signature 'syndromicD'
update_syndromic(x, id, syndromes.var,
  add.syndromes = TRUE, dates.var, date.format = "%d/%m/%Y",
  remove.dow = FALSE, add.to = 0, replace.dates = TRUE, data = NULL)

## S4 method for signature 'syndromicW'
update_syndromic(x, id, syndromes.var,
  add.syndromes = TRUE, dates.var, date.format = "%d/%m/%Y",
  replace.dates = TRUE, data = NULL)

Arguments

x	the syndromic object to be updated (if one does not already exist, please use "raw_to_syndromic(D or W)" or "syndromic(D or W)")
...	Additional arguments to the method.
id	indicates a variable (or multiple variables) which should be used to identify unique events in the data. It can be provided as an R vector (p.e. mydata$myid), as the name of a Data Frame column (p.e. id=myid, data=my.data), or as multiple column names (p.e. id=list(id1,id2,id3), data=my.data).
syndromes.var	the variable that identifies group membership (in general the syndromic grouping). Can be numeric, character or factor.
add.syndromes	a logical argument indicating whether syndromic groups found in the new data and not existing in the old syndromic object should be added (set to TRUE) or ignored (set to FALSE).
dates.var	The vector (dates.var=mydata$mydates) or column name (dates.var=mydates, data=mydata) where the dates of the events are to be found.
date.format	The date.format of the date.variable. Default is d/m/Y. See strptime() for format specifications. If the data have been recorded weekly, then the column "dates.var" MUST be in the ISOweek format, and the date.format must be set to "ISOweek".
remove.dow	An optional argument, by default set to FALSE. This allows the user to specify weekdays that must be removed from the dataset, for instance when weekends are not relevant. This must be se to integers between 0 and 6 specifying the day of the week to be removed. To remove saturdays and sundays, for instance, set remove.dow=c(6,0)
add.to	when remove.dow is used, the user has the option to completely remove any counts assigned to the days of week to be remove (set add.to=0) or add them to the following or precedent day. For instance when removing weekends, the counts registered during weekends can be assigned to the following Monday or the preceding Friday, using add.to=1 or add.to=-1 respectively. Please note that: (i) the vector add.to must have the exact same dimensions as remove.dow. To remove weekends adding any observed counts to the following Monday the user would need to set remove.dow=c(6,0) and add.to=c(2,1) (Saturdays added to 2 days ahead, and Sunday to 1 day ahead)
replace.dates	a logical argument indicating whether dates in the new dataset for which data were already available in the syndromic object should replace the old values (set to TRUE) or be ignored (set to FALSE)
data	Optional argument. If used the other arguments can be specified as column names within the dataset provided through this argument
formula	A formula, or list of formulas, specifying the regression formula to be used when removing temporal patterns from each of the syndromes in @observed. For instance formula=list(y~dow+mon) for a single syndrome, where regression must take into account the variables dow (day-of-week) and month; or formula=c(y~dow, y~dow+mon) specifying two different formulas for two syndromes. The names of the variables given should exist in the columns of the slot @dates. Make sure that formulas' index match the columns in observed (for instance the second formula should correspond to the second syndrome, or second column in the observed matrix).You can provide NA for syndromes which should not be associated with any formula. This parameter is often only filled after some analysis in the data, not at the time of object creation.

Value

an updated object of the class syndromicD with the following slots: (1) OBSERVED: The previous observed matrix is updated adding lines corresponding to the new events found in the new dataset; (2) DATES: also updated to include extra time points; (3) BASELINE: if a baseline matrix was available lines are added, with data copied from the slot observed; (4) ALARMS: the same number of lines added to observed are added, but given a value of NA to make clear that aberration detection analyses were not yet carried out in these data; (5) UCL and (6) LCL: same as alarms.

Examples

  ## examples for DAILY data
data(lab.daily)
data(lab.daily.update)
my.syndromicD <- raw_to_syndromicD (id=SubmissionID,
                                  syndromes.var=Syndrome,
                                  dates.var=DateofSubmission,
                                  date.format="%d/%m/%Y",
                                remove.dow=c(6,0),
                                add.to=c(2,1),                               
                                  data=lab.daily)
 my.syndromicD <- update_syndromicD(x=my.syndromicD,
                                id=SubmissionID,
                                syndromes.var=Syndrome, 
                                add.syndromes=TRUE,
                                dates.var=DateofSubmission, 
                                date.format="%d/%m/%Y", 
                                remove.dow=c(6,0),
                                add.to=c(2,1),
                                replace.dates=FALSE,
                                data=lab.daily.update)

## examples for WEEKLY data
data(lab.daily)
data(lab.daily.update)
my.syndromicW <- raw_to_syndromicW (id=SubmissionID,
                                  syndromes.var=Syndrome,
                                  dates.var=DateofSubmission,
                                  date.format="%d/%m/%Y",
                                  data=lab.daily)
my.syndromicW <- update_syndromic(x=my.syndromicW,
                                 id=lab.daily.update$SubmissionID,
                                 syndromes.var=lab.daily.update$Syndrome, 
                                 add.syndromes=TRUE,
                                 dates.var=lab.daily.update$DateofSubmission, 
                                 date.format="%d/%m/%Y", 
                                 replace.dates=TRUE)


my.syndromicW <- update_syndromic(x=my.syndromicW,
                                 id=SubmissionID,
                                 syndromes.var=Syndrome, 
                                 add.syndromes=TRUE,
                                 dates.var=DateofSubmission, 
                                 date.format="ISOweek", 
                                 replace.dates=TRUE,
                                 data=lab.weekly.update)##'

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