IIProductionUnknown: Analyzing Data Through of Percentage of Importance Indice
(Production Unknown) and Its Derivations
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>.
| Version: |
0.0.3 |
| Depends: |
crayon |
| Published: |
2023-02-01 |
| Author: |
Germano Leao Demolin-Leite
 [aut],
Alcinei Mistico Azevedo
[aut, cre] |
| Maintainer: |
Alcinei Mistico Azevedo <alcineimistico at hotmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
NEWS |
| CRAN checks: |
IIProductionUnknown results |
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