Iberian–Balearic fern regions and their explanatory variables

Modelling with multiple explanatory variables.

Forward selection of explanatory variables.

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. T...

Regression Transformation Diagnostics for Explanatory Variables

Two types of diagnostics are presented for the transformation of explanatory variables in regression. One is based on the likelihood displacement proposed by Cook and Weisberg (1982) for assessing the in uence of individual cases on the maximum likelihood estimate of a transformation parameter. The other is based on the local in uence theory proposed by Cook (1986) for assessing the in uence of...

Integer Valued AR Processes with Explanatory Variables

Integer valued AR (INAR) processes are perfectly suited for modelling count data. We consider the inclusion of explanatory variables into the INAR model to extend the applicability of INAR models and give an alternative to Poisson regression models. An efficient MCMC algorithm is constructed to analyze the model and incorporates both explanatory variable and order selection. The methodology is ...

Linear regression with compositional explanatory variables

Compositional explanatory variables should not be directly used in a linear regression model because any inference statistic can become misleading. While various approaches for this problem were proposed, here an approach based on the isometric logratio (ilr) transformation is used. It turns out that the resulting model is easy to handle, and that parameter estimation can be done like in usual ...