[R-sig-ME] R-sig-mixed-models Digest, Vol 111, Issue 9

Gregoire, Timothy timothy.gregoire at yale.edu
Mon Mar 14 05:06:18 CET 2016

Hi Jacob,

I will defer to Doug, as he is the ultimate source. 

I would not fret. It is all about numerical instability when trying to invert (Xprime\Sigma X) matrix, I suspect. 


Timothy G. Gregoire
J. P. Weyerhaeuser Professor of Forest Management
School of Forestry & Environmental Studies
Yale University
360 Prospect St, New Haven, CT, U.S.A. 06511
Ph: 1.203.432.9398 mob: 1.203.508.4014  fax:1.203.432.3809

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Today's Topics:

   1. Non-significant fixed effect allows approximation of
      variance-covariance matrix (Jacob Bukoski)


Message: 1
Date: Sat, 12 Mar 2016 20:33:38 -0500
From: Jacob Bukoski <jbukoski1 at gmail.com>
To: "r-sig-mixed-models at r-project.org"
	<r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Non-significant fixed effect allows approximation
	of variance-covariance matrix
	<CAOES0Vjh3vEb=w9VaORWOP0MRTgDFuABpoGCSBAfa0WXnH6t6g at mail.gmail.com>
Content-Type: text/plain; charset="UTF-8"

Dear all,

I am using lme() to run a mixed effects model on soil carbon observations, with fixed effects specified for latitude (continuous), and dominant genera of tree (factor with five levels), and random effects specified for site.

The data is heteroscedastic, which I can account for well with a varIdent weights specification; however, when I do so I receive a "non-positive definite approx. var-covar matrix" output from the call to $apVar.

When I add in a third fixed effect (Geomorphic setting, a factor with three levels), the variance-covariance matrix is approximated successfully, but the levels of the added third fixed effect are non significant.

I've been trying to read up on why this might be occurring, but can't for the life of me figure out why a more complex model (including non-significant predictors) would allow for the successful approximation of the variance-covariance matrix.

I'm hoping to use the model for predictive purposes, and ideally would not include non-significant effects in its final form.

Does anyone have any ideas on why this might be occurring, or intermediate-level resources per non-positive definite variance-covariance matrices that I could look into?

Many kind thanks,

P.S. If it helps, the model specification is here:

*lme.C.density <- lme(C.density ~ Latitude + Genus + Geomorph, random=(~1|Site), weights=varIdent(form=~Genus|Site), data=model.c.dens, method = "REML")*

Jacob J. Bukoski
Master of Environmental Science Candidate, 2016 School of Forestry and Environmental Studies, Yale University jbukoski1 at gmail.com | jacob.bukoski at yale.edu | LinkedIn <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.linkedin.com_profile_view-3Fid-3DAAIAAAdWVW8BMzqU-5F2EGNbEkyuy8O7K1Jyhd8ps-26trk-3Dnav-5Fresponsive-5Ftab-5Fprofile-5Fpic&d=AwICAg&c=-dg2m7zWuuDZ0MUcV7Sdqw&r=atRKEKX5W2zm-GsIgYzLo4oYM9D-Qn-eMFObHZtnEnI&m=V1qtLiJ8zqDTfpgXN00RSeCx0zncNSopJhLyHBW2k9g&s=3DY9F_FwFIGYXmf7IW5gKhKEFL7oBCG_vb5ZW2ReKdU&e= >

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