Predictability of Statistical models in the evaluate site productivity Fagus Orientalis Lipsky trees

Subject Areas : rash

Somayeh Dehghaninezhad ¹ (Department of Forestry, Faculty of Natural Resources, University of Tarbiat Modares)
Seyed Jalil Alavi ² (Assistant Professor, Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University)
Seyed Mohsen Hosseini ³ (Professor, Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran)

Received: 2018-03-10 Accepted : 2018-07-30 Published : 2018-08-23

Keywords: Cross-validation, Site productivity, Root mean square error, Generalized linear model, Generalized additive model,

Abstract :

In the present study, evaluated predictability of generalized additive and linear models in R software by applying selection variable different method for dominant height of beech species as a high criterion for site productivity. Dominant height defined as average height of three highest trees in any sample plots. For this purpose, locate 127 circular sample plots with an area of 1000 square meters in beech dominated forests in research forest of Tarbiat Modares university and in each of them height and diameter of Fagus Orientalis Lipsky trees that greater than a diameter of 7.5 cm within each of plot was recorded along with elevation and percent slope and azimuth. Also, at the center of each sample plot, soil samples from 0-10 cm depth were taken, and several soil physical and chemical variables were measured. In this study, performance of five variable-selection methods evaluated individually for each of generalized linear and additive modeles. In order to compared the performance of variable-selection methods in generalized linear model, is used cross-validation with 2500 repeated and for generalized additive model is cross-validation 10-fold. After selecting the best method of variable selection in each of generalized linear and additive models, obtained relative importance of any important variable that finally altitude variable explored as the most important effective variable on dominant height of beech species. Then Using the evaluation criteria of data modeling which showed generalized additive model of evaluation criteria,has better performance than generalized linear model.

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