effInd: Return the global efficiency indicator.

In david-salgado/categObsPredModelParam: Class and methods for the parameters of a categorical observation- prediction model

Description

effInd computes the global efficiency indicator. It is defined as the difference between the area under the polygonal line of the absolute relative pseudo-bias, and the area under the straight line corresponding to random selection.

Usage

effInd(matrix)

Arguments

matrix

an object of class matrix, containing two columns: the absolute relative pseudo-bias, ARB, for every value in the column edPriority, which represents the number of units analyzed.

Value

an object of class numeric with the value of global efficiency indicator.

Examples

## Not run: 
 fitPar <- new(Class = 'fitParam',
  edData = FFall_AS.StQ, rawData = FGall_AS.StQ, 
  selParam = list(ntreeTry=1000, stepFactor=2, improve=0.05, 
                  trace=TRUE, plot=TRUE, doBest = TRUE, 
                  ptrain = 0.8, DD = DDactu),
                  valParam = list(edEffInd = effInd, priorBin = 5, 
                  dataVal = c('Train','Test')))
                  
 ObsPredPar1 <- new(Class = 'categObsPredModelParam',
                  Data = FGall_AS.StQ,
                  VarRoles = list(Units = IDUnits,
                  Domains = character(0),
                  DesignW = DesignW,
                  Regressands = Regressands,
                  Regressors = Regressors
                  ))
                  
ObsPredPar1 <-  fitModels(ObsPredPar1, fitPar, na.as.category)
ObsPredPar1 <- computeVal(ObsPredPar1, fitPar, na.as.category)
# computeVal calls computeEdEfficiency calls (computeRunningEstim and effInd)

## End(Not run)#'

david-salgado/categObsPredModelParam documentation built on Dec. 3, 2020, 1:42 p.m.