[R-sig-ME] Mixed Models convergence problems, Jordi Rosich
Jordi Rosich
jordirosich16 at gmail.com
Mon Nov 14 22:33:51 CET 2016
Hello,
I'm Jordi Rosich, a student currently collaborating with the Biology
Conservation Group of the University of Barcelona. I'm writing you because
I'm having model convergence troubles with some GLMMs using the function
glmer of the package lme4 of R.
Our research addresses nest-site selection of the Goshawk, a territorial
bird of prey, and specifically we aim to understand which environmental
variables are relevant for nest site-selection in this species.
*Our approach**:*
1) We sampled several environmental variables in sites used by this
bird species in nest (1) and unoccupied sites (0), and therefore occupation
status (0/1) is our dependent variable and the environmental variables our
independent variables.
2) We performed three analysis at 3 different spatial-scales: nest
tree, nest-site forest (being the 18 meters radius circular area around the
nest-tree), and landscape around nest-site (500 meters radius circular area
around the nest-tree).
3) We sampled 29 Goshawk nests comprised in 13 breeding pairs
territories (each territory may hold several nests) and 30 control
non-occupied random trees comprised in 25 "pseudo-territories" (the near
trees being included in this "territories"). To avoid pseudoreplication of
nests of the same breeding pair (or territory) we have considered the
factor "Territory" as our random factor in the mixed models. The model
definition is approximately Y = explanatory variables + (1|id Territory),
where Y is the occupation status (0/1) (See an example below).
4) Our independent variables are both categorical (e.g. tree species;
aspect: an angle recoded into 4 categories) and continuous (components
resulting of a PCA on several original environmental variables, performed
to reduce the number of original variables). For more details for each
analysis:
-Nest-tree scale: 2 continuous variables (FAC1TreeHeight, FAC2TreeWidth)
and 1 four level categorical variable (TreeSpecies).
-Nest-site forest scale: 4 continuous variables (FAC1BroadLeavedTrees,
FAC2YoungPines, FAC3MaturePines, FAC4SlopeAndShrubs) and 1 four level
categorical variable (ForestAspect).
-Landscape scale: 3 continuous variables (FAC1PinusVSQuercus,
FAC2HumanizedLand, FAC3DistanceToRoads).
One example of the script used to model would be:
mod1 <- glmer(Occupation~FAC1Treeheight+FAC2TreeWidth+Sp+(1|Territory),
family=binomial, data=trees)
*The problem: *
While creating the candidate models with glmer function to later select the
best ones by their AICcs, we've faced some warnings telling that some of
the models are failing to converge:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0483015 (tol = 0.001,
component 1)
It happens specially, although not always, with the models with more
parameters and also with those containing categorical variables.
*My question: *
Given our variables, random factor and data is there any particular reason
why our models could fail to converge? If so, is there a possible solution
to the convergence problem?
Thank you very much in advance. Waiting for your answer,
Jordi Rosich
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