% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/syndromicW.r
\docType{class}
\name{syndromicW-class}
\alias{syndromicW-class}
\title{Class \code{"syndromicW"}}
\description{
Syndromic is the main class of the \code{vetsyn} package, but it has been 
specified for different types of data streams. \code{syndromicD} is the class
used for data that are recorded and monitored DAILY, while \code{syndromicW} is
used when the time points for monitoring refer to WEEKS.
}
\details{
The \code{syndromicD} or \code{syndromicW} classes store observed data in 
a format appropriate
for monitoring, and contain several slots to store input and outputs of 
analysis (temporal monitoring).
Functions are available to create an object of the class syndromic from data 
already cleaned and prepared for monitoring, or alternatively from raw observed data.
}
\section{Slots}{

\itemize{
  \item{observed}{
    A \code{matrix} with the number of rows equal to the number of time points available
(i.e., the number of DAYS of observed data for syndromicD, or the number of WEEKS
for syndromicW); and number of columns equal to the number of 
syndromes monitored.
  }
  \item{dates}{
    A \code{DataFrame} which first column contains the dates corresponding to the observations
recorded. In syndromicD the dates are recorded in the format "yyyy-mm-dd" in the first columns,
and additional columns contain additional information extracted from the date,
such as day-of-the-week, month, holidays, etc. For syndromicW the first column
contains the week in the ISOweek format, and additional columns give the week and year in the
numerical format.
  }
  \item{baseline}{
  A \code{matrix} of dimensions exactly equal to the slot observed, where observed data have been 
  cleaned in order to remove excess noise and/or outbreak signals, generating an outbreak-free
  time series that should be used as baseline for the detection algorithms.     
  }
  \item{alarms}{
    An \code{array} containing the results of the outbreak-signal detection algorithms, for each
    of the time series being monitored (columns in observed). Alarms
    can be registered as binary values (0 for no alarm and 1 for alarm) or as a ordinal value 
    representing an alarm level (for instance 0-5). The first and second dimensions (rows and columns) 
    correspond to the dimensions of the time series monitored, but a third dimension can be added when
    multiple detection algorithms are used.
  }
  \item{UCL}{
    An \code{array} containing the upper confidence limit (UCL) of the 
    outbreak-signal detection algorithms, 
    for each of the time series being monitored (columns in observed).  
    The first and second dimensions (rows and columns) correspond to the dimensions of the 
    time series monitored, but a third dimension can be added when 
    multiple detection algorithms are used. Whether an alarm is registered or not, 
    this dimension can be used to record the minimum number that would have generated an alarm.
  }
  \item{LCL}{
    An \code{array} containing the lower confidence limit (LCL) of the outbreak-signal 
    detection algorithms, for each of the time series being monitored
     ( columns in the slot observed), when detection is based
    (also) on the detection of decreases in the number of observations.
     The first and second dimensions (rows and columns) 
    correspond to the dimensions of the time series monitored, 
    but a third dimension can be added when
    multiple detection algorithms are used. Whether an alarm is registered or not, 
    this dimension can be used
    to record the maximum number that would have generated an alarm.
  }
  \item{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=y~dow+mon for a single syndrome, where regression must take into 
    account the variables dow (day-of-week) and month;
    or formula=list(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.
    }
    }
}

\examples{
## Load data
data(observedW)
my.syndromicW <- syndromicW(observed,min.week=1, min.year=2011, 
                              max.week=22, max.year=2013)

}
