Chapter 5: Generalized Linear Models: Structure

Dunn, Peter K.; Smyth, Gordon K.

doi:10.1007/978-1-4419-0118-7_5

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In our study, we utilized GLM of the binary family utilizing the suggestions from previous research [ 52 , 78 ]. The GLM framework involves specifying the probability distribution of the response variable (binomial distribution in the case of binary regression), the link function, and the linear predictor that models the relationship between the predictors and the expected value of the response variable [ 79 ]. The probability distribution function and the link function in our binomial GLM were computed using equations (2) and (3 ), respectively.…”

Section: Methodsmentioning

confidence: 99%

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

Joshi,

Adhikari,

Bhattarai

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

Joshi,

Adhikari,

Bhattarai

et al. 2024

Heliyon

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Modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks, random forest, and generalized linear models

2023

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Background Abiotic factors exert different impacts on the abundance of individual tree species in the forest but little has been known about the impact of abiotic factors on the individual plant, particularly, in a tropical forest. This study identified the impact of abiotic factors on the abundances of Podocarpus falcatus, Croton macrostachyus, Celtis africana, Syzygium guineense, Olea capensis, Diospyros abyssinica, Feliucium decipenses, and Coffea arabica. A systematic sample design was used in the Harana forest, where 1122 plots were established to collect the abundance of species. Random forest (RF), artificial neural network (ANN), and generalized linear model (GLM) models were used to examine the impacts of topographic, climatic, and edaphic factors on the log abundances of woody species. The RF model was used to predict the spatial distribution maps of the log abundances of each species. Results The RF model achieved a better prediction accuracy with R2 = 71% and a mean squared error (MSE) of 0.28 for Feliucium decipenses. The RF model differentiated elevation, temperature, precipitation, clay, and potassium were the top variables that influenced the abundance of species. The ANN model showed that elevation induced a negative impact on the log abundances of all woody species. The GLM model reaffirmed the negative impact of elevation on all woody species except the log abundances of Syzygium guineense and Olea capensis. The ANN model indicated that soil organic matter (SOM) could positively affect the log abundances of all woody species. The GLM showed a similar positive impact of SOM, except for a negative impact on the log abundance of Celtis africana at p < 0.05. The spatial distributions of the log abundances of Coffee arabica, Filicium decipenses, and Celtis africana were confined to the eastern parts, while the log abundance of Olea capensis was limited to the western parts. Conclusions The impacts of abiotic factors on the abundance of woody species may vary with species. This ecological understanding could guide the restoration activity of individual species. The prediction maps in this study provide spatially explicit information which can enhance the successful implementation of species conservation.

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Chapter 5: Generalized Linear Models: Structure

Cited by 2 publications

References 9 publications

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

Modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks, random forest, and generalized linear models

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