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ImportanceIndice (version 0.0.2)

Analyzing Data Through of Percentage of Importance Indice and Its Derivations

Description

The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) . This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(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) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) .

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Version

Install

install.packages('ImportanceIndice')

Monthly Downloads

169

Version

0.0.2

License

GPL-3

Maintainer

Alcinei Mistico Azevedo

Last Published

September 13th, 2022

Functions in ImportanceIndice (0.0.2)

DataNumberSamples

Number samples data
SolutionSource

Obtaining indexes associated with the solution sources.
DataSolutionSource

Solution sources data
NonAttentionLevel

Estimates levels of non-attention.
SelectEffectivenessOfSolution

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

Obtaining indices associated with loss of production.
DataProduction

Production data
LossSource

Obtaining indices associated with sources of loss
Distribution_LossSource

Loss source distribution information
Distribution_SolutionSource

Solution source distribution information
EffectivenessOfSolution

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

Analyzing data through of percentage of importance indice and its derivations
DataLossSource

Loss sources data