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Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity

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Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity (English)

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Rob Slotow

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Shomen Mukherjee

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Shomen Mukherjee

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Jayanti Ray-Mukherjee

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1

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Jayanti Ray-Mukherjee

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Michelle Hamer

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Michelle Hamer

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Kim Nimon

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2

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Douglas W. Morris

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4

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publication date

20 March 2014

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published in

Methods in Ecology and Evolution

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volume

5

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issue

4

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page(s)

320-328

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Five (or so) challenges for species distribution modelling

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

Tools to support interpreting multiple regression in the face of multicollinearity

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

A general and simple method for obtainingR2from generalized linear mixed-effects models

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

retrieved

21 January 2018

An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

What drives chytrid infections in newt populations? Associations with substrate, temperature, and shade

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

Playing statistical ouija board with commonality analysis: good questions, wrong assumptions

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

Why do we still use stepwise modelling in ecology and behaviour?

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https://api.crossref.org/works/10.1111%2F2041-210X.12166

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21 January 2018

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10.1111/2041-210X.12166

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(Q57248688)

Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity

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