A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. It can also include the generalized linear model. However, there are other types of models also available, including decision trees and artificial neural networks under the general term of predictive modeling. Predictive modeling includes nearest neighbor discriminant analysis, also known as memory based reasoning. These other models are nonparametric and do not require that you know the probability distribution of the underlying patient population. Therefore, they are much more flexible when used to examine patient outcomes. Because predictive modeling uses regression in addition to these other models, the end results will improve upon those found using just regression by itself.

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Influence diagnostics on a reparameterized t-Student spatial linear model

Spatial Statistics ◽

10.1016/j.spasta.2020.100481 ◽

2021 ◽

Vol 41 ◽

pp. 100481

Author(s):

Miguel Angel Uribe-Opazo ◽

Fernanda De Bastiani ◽

Manuel Galea ◽

Rosangela Carline Schemmer ◽

Rosangela Aparecida Botinha Assumpção

Keyword(s):

Linear Model ◽

Influence Diagnostics ◽

Spatial Linear Model

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Robust trend parameters in a multivariate spatial linear model

Test ◽

10.1007/bf02595724 ◽

2003 ◽

Vol 12 (2) ◽

pp. 445-457 ◽

Cited By ~ 5

Author(s):

Ana F. Militino ◽

M. Blanca Palacios ◽

M. Dolores Ugarte

Keyword(s):

Linear Model ◽

Spatial Linear Model

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Global Influence Diagnostics in Gaussian Spatial Linear Model with Multiple Repetitions

Procedia Environmental Sciences ◽

10.1016/j.proenv.2015.05.002 ◽

2015 ◽

Vol 26 ◽

pp. 74-77 ◽

Cited By ~ 1

Author(s):

Fernanda De Bastiani ◽

Miguel A. Uribe-Opazo ◽

Audrey H. M.A. Cysneiros ◽

Manuel Galea

Keyword(s):

Linear Model ◽

Influence Diagnostics ◽

Spatial Linear Model ◽

Global Influence

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Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

Engenharia Agrícola ◽

10.1590/s0100-69162012000200019 ◽

2012 ◽

Vol 32 (2) ◽

pp. 393-404 ◽

Cited By ~ 5

Author(s):

Fernanda de Bastiani ◽

Miguel A. Uribe-Opazo ◽

Gustavo H. Dalposso

Keyword(s):

Spatial Variability ◽

Linear Model ◽

Confusion Matrix ◽

Soil Resistance ◽

Thematic Maps ◽

Estimation Model ◽

The North ◽

The Matrix ◽

Spatial Linear Model ◽

Resistance To Penetration

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

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