[R-sig-ME] Non-significant fixed effect allows approximation of variance-covariance matrix

Jacob Bukoski jbukoski1 at gmail.com
Sun Mar 13 02:33:38 CET 2016

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

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