LossSource: Obtaining indices associated with sources of loss

In IIProductionUnknown: Analyzing Data Through of Percentage of Importance Indice (Production Unknown) and Its Derivations

Obtaining indices associated with sources of loss

Description

These functions allow to calculate the total n of the L.S. (n), R.P., ks, c, ds, n.I.I., S.n.I.I., and percentage of I.I. (P.I.I.) by each L.S..
Equations: R.P. = Damage or defoliation
n=total n per sample
k.s.= R.P./n
c = SUM of occurrence of L.S. on the samples, where, absence = 0 or presence = 1.
ds = 1 - P of the chi-square test of L.S. on the samples.
n.I.I.=ks x c x ds
S.n.I.I. = sum of all n.I.I.
Percentage of I.I. (P.I.I.)=(n.I.I. of each L.S./sum of all n.I.I.)*100

Usage

LossSource(DataLoss,DataResult,Cols=c(1,3,5),verbose)

Arguments

DataLoss	It is a data frame or matrix object containing data from loss sources. Sources of loss refers to the number of individuals per observation that cause damage to the system.
DataResult	Matrix or data frame with loss sources. Solution sources refers to the number of individuals per observation that cause a reduction in the sources of loss in the system.
Cols	Most important data loss columns.
verbose	Logical value (TRUE/FALSE). TRUE displays the results of the analysis.

Value

The function returns the Percentage of Importance Index-Production Unknown and estimates of variables used in its construction.

Author(s)

Germano Leao Demolin-Leite (Instituto de Ciencias Agrarias da UFMG)
Alcinei Mistico Azevedo (Instituto de Ciencias Agrarias da UFMG)

See Also

EffectivenessOfSolution , SolutionSource

Examples

data("DataLossSource")
ChisqTest_Distribution(DataLossSource)

data("DataSolutionSource")
ChisqTest_Distribution(DataSolutionSource)

data("DataDefoliation")
data("DataDamage")

DataResult<-cbind(DataDefoliation,DataDamage$D.L.S.2,DataDefoliation,
DataDamage$D.L.S.4,DataDefoliation)
ResultLossSource<-LossSource(DataLoss = DataLossSource,DataResult =DataResult,
Cols=c(1,3,5),verbose=TRUE)

EOS<-EffectivenessOfSolution(DataLossSource =DataLossSource,
                            DataSolutionSource =DataSolutionSource,
                            ResultLossSource = ResultLossSource)

EOS
#Put: y and y
# ID=SelectEffectivenessOfSolution(EOS)
ID<-c(FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)
ResultSolutionSource<-SolutionSource(SolutionData =DataSolutionSource,Production =DataResult,
                                    EffectivenessOfSolution =EOS ,Id = ID,Verbose = TRUE  )
ResultSolutionSource

# Put: y,n,y,n,y,n and y
# ReductionAbundance(ResultSolutionSource,ResultLossSource,
#                  EffectivenessOfSolution=EOS)

###################################################
EOSDamage<-EffectivenessOfSolution(DataLossSource =DataDamage,
                                  DataSolutionSource =DataSolutionSource,
                                  ResultLossSource = NULL)


EOSDamage

# Put: y, n and y
#ReductionDamage(ResultSolutionSource,LossSource=DataDamage,
#                EffectivenessOfSolution=EOSDamage)

IIProductionUnknown documentation built on Feb. 16, 2023, 6:23 p.m.