Package 'IIProductionUnknown'

Title: Analyzing Data Through of Percentage of Importance Indice (Production Unknown) and Its Derivations
Description: The Importance Index (I.I.) can determine the loss and solution sources for a system in certain knowledge areas (e.g., agronomy), when production (e.g., fruits) is known (Demolin-Leite, 2021). Events (e.g., agricultural pest) can have different magnitudes (numerical measurements), frequencies, and distributions (aggregate, random, or regular) of event occurrence, and I.I. bases in this triplet (Demolin-Leite, 2021) <https://cjascience.com/index.php/CJAS/article/view/1009/1319>. Usually, the higher the magnitude and frequency of aggregated distribution, the greater the problem or the solution (e.g., natural enemies versus pests) for the system (Demolin-Leite, 2021). However, the final production of the system is not always known or is difficult to determine (e.g., degraded area recovery). A derivation of the I.I. is the percentage of Importance Index-Production Unknown (% I.I.-PU) that can detect the loss or solution sources, when production is unknown for the system (Demolin-Leite, 2024) <DOI:10.1590/1519-6984.253218>.
Authors: Germano Leao Demolin-Leite [aut] , Alcinei Mistico Azevedo [aut, cre]
Maintainer: Alcinei Mistico Azevedo <[email protected]>
License: GPL-3
Version: 0.0.3
Built: 2024-10-31 06:39:46 UTC
Source: CRAN

Help Index

Loss and solution sources distribution informations

Description

Indicates the distribution of loss and solution sources: aggregate, random or regular.

Usage

ChisqTest_Distribution(Data)

Arguments

Data

It is a data frame object containing data from total number per repetition of loss source and solution source.

Value

Return distribution of loss and solution sources: aggregate, random, or regular. This information is important to check whether the problem or solution occurs randomly or not. This has an impact on the decision making associated with increasing or reducing of the problem.

Author(s)

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

See Also

EffectivenessOfSolution , LossSource , ReductionDamage

Examples

data("DataLossSource")
ChisqTest_Distribution(DataLossSource)

data("DataSolutionSource")
ChisqTest_Distribution(DataSolutionSource)

Data damage

Description

An example with data from data damage.

Usage

data(DataDamage)

Format

A data frame with sources of solution, one in each column.

Author(s)

Germano Leao Demolin Leite : [email protected]

Alcinei Mistico Azevedo : [email protected]

References

Demolin-Leite, G. L. (2024), Percentage of importance indice-production unknown: loss and solution sources identification on system. Brazilian Journal of Biology 84, e253218. <https://doi.org/10.1590/1519-6984.253218>.

Data defoliation

Description

An example with data from data defoliation.

Usage

data(DataDefoliation)

Format

A data frame with data defoliation.

Author(s)

Germano Leao Demolin Leite : [email protected]

Alcinei Mistico Azevedo : [email protected]

References

Demolin-Leite, G. L. (2024), Percentage of importance indice-production unknown: loss and solution sources identification on system. Brazilian Journal of Biology 84, e253218. <https://doi.org/10.1590/1519-6984.253218>.

Loss sources data

Description

An example with data from loss sources.

Usage

data(DataLossSource)

Format

A data frame with sources of loss, one in each column.

Author(s)

Germano Leao Demolin Leite : [email protected]

Alcinei Mistico Azevedo : [email protected]

References

Demolin-Leite, G. L. (2024), Percentage of importance indice-production unknown: loss and solution sources identification on system. Brazilian Journal of Biology 84, e253218. <https://doi.org/10.1590/1519-6984.253218>.

Solution sources data

Description

An example with data from solution sources .

Usage

data(DataSolutionSource)

Format

A data frame with sources of solution, one in each column.

Author(s)

Germano Leao Demolin Leite : [email protected]

Alcinei Mistico Azevedo : [email protected]

References

Demolin-Leite, G. L. (2024), Percentage of importance indice-production unknown: loss and solution sources identification on system. Brazilian Journal of Biology 84, e253218. <https://doi.org/10.1590/1519-6984.253218>.

Function to estimate the effectiveness of solution sources (S.S.) by loss source (Percentage_I.I. > 0.00) in the production system.

Description

This function allows to calculate E.S. of each S.S. by L.S. (significant in the reduction of defoliation or damage) in the system. Equation: E.S. = R2 x (1 - P) when it is of the first degree, or E.S. = ((R2 x (1 - P))x(B2/B1) when it is of the second degree. Where, R2 = determination coefficient and P = significance of ANOVA, B1 = regression coefficient, and B2 = regression coefficient (variable2), of the simple regression equation of the S.S..

Usage

EffectivenessOfSolution (DataLossSource,DataSolutionSource,ResultLossSource, verbose=TRUE)

Arguments

DataLossSource

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.
DataSolutionSource

It is a data frame or matrix object containing data from solution sources. Solution sources refers to the number of individuals per observation that cause a reduction in the sources of loss in the system.
ResultLossSource

Output of LossSource function.
verbose

Logical value (TRUE/FALSE). TRUE displays the results of the effectiveness of solution

Value

The function returns the indices associated with the effectiveness of solution source in relation to the loss source, considering the coefficients obtained in the regression analyses.

Author(s)

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

See Also

ReductionDamage , 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)

Analyzing data through of percentage of importance indice-production unknown and its derivations.

Description

The Percentage of Importance Indice-production unknown (Percentage_I.I.P.U.) bases in magnitudes, frequencies, and distributions of occurrence of an event. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of damage or damage reduction in the system. The Percentage_I.I.P.U. = ((ks1 x c1 x ds1)/SUM(ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)) x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Reduction of the total n. of the L.S. (R.L.S.)/Total n. of the solution source and ii) Percentage of the R.L.S. per S.S..

Author(s)

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

References

Demolin-Leite, G. L. (2024), Percentage of importance indice-production unknown: loss and solution sources identification on system. Brazilian Journal of Biology 84, e253218. <https://doi.org/10.1590/1519-6984.253218>.

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)

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)

Estimate of the abundance reduction

Description

Function to estimate of the abundance reduction. In this function you are asked to indicate 'y' or 'n' in order to define whether the association of the source of solution with the source of loss is important.

Usage

ReductionAbundance(ResultSolutionSource,ResultLossSource,EffectivenessOfSolution)

Arguments

ResultSolutionSource

Output of the SolutionSource function.
ResultLossSource

Output of the LossSource function.
EffectivenessOfSolution

Output of the EffectivenessOfSolution function.

Value

The function returns the estimate of the reduction in abundance (loss source) due to solution source.

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)

Estimate of the damage reduction

Description

Function to estimate of the damage reduction

Usage

ReductionDamage(ResultSolutionSource,LossSource,EffectivenessOfSolution)

Arguments

ResultSolutionSource

Output of the SolutionSource function.
LossSource

Loss Source data. Sources of loss refers to the number of individuals per observation that cause damage to the system.
EffectivenessOfSolution

Output of the EffectivenessOfSolution function.

Value

The function returns the estimate of the reduction in damage.

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)

Determine the pair by pair effects that are important for the analysis.

Description

Selects, pair by pair, the effect of S.S. on L.S.

Usage

SelectEffectivenessOfSolution(EffectivenessOfSolution)

Arguments

EffectivenessOfSolution

Output generated by the function 'EffectivenessOfSolution'

Value

Returns a vector with logical values demonstrating the interactions considered important for the analysis.

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 , LossSource

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)

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)

Obtaining indexes associated with the solution sources.

Description

Function to estimate the total n of the S.S. (n), E.S., ks, c, ds, n.I.I., Sum.n.I.I., and percentage of I.I. (P.I.I.) by each S.S..

Usage

SolutionSource(SolutionData,Production,EffectivenessOfSolution,Id,Verbose=TRUE)

Arguments

SolutionData

It is a data frame or matrix object containing data from Solution sources. Solution sources refers to the number of individuals per observation that cause a reduction in the sources of loss in the system.
Production

Matrix or data frame with a column containing the damage or defoliation data.
EffectivenessOfSolution

Output generated by the function 'EffectivenessOfSolution'
Id

Logical vector indicating the lines of the 'EffectivenessOfSolution' that are relevant. Output generated by the function SelectEffectivenessOfSolution
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)

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)