DeepAI AI Chat
Log In Sign Up

On the goodness-of-fit of generalized linear geostatistical models

by   Emanuele Giorgi, et al.

We propose a generalization of Zhang's coefficient of determination to generalized linear geostatistical models and illustrate its application to river-blindness mapping. The generalized coefficient of determination has a more intuitive interpretation than other measures of predictive performance and allows to assess the individual contribution of each explanatory variable and the random effects to spatial prediction. The developed methodology is also more widely applicable to any generalized linear mixed model.


page 1

page 2

page 3

page 4


A Coefficient of Determination (R2) for Linear Mixed Models

Extensions of linear models are very commonly used in the analysis of bi...

A Coefficient of Determination for Probabilistic Topic Models

This research proposes a new (old) metric for evaluating goodness of fit...

Coefficients of Determination for Mixed-Effects Models

In consistency with the law of total variance, the coefficient of determ...

Decomposition of the Explained Variation in the Linear Mixed Model

The concept of variation explained is widely used to assess the relevanc...

The R2D2 Prior for Generalized Linear Mixed Models

In Bayesian analysis, the selection of a prior distribution is typically...

A New Pathway to Approximate Energy Expenditure and Recovery of an Athlete

This work proposes to use evolutionary computation as a pathway to allow...