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lme4 convergence warnings and confirmatory path analysis with hierarchical data
2 messages · Margaret Metz, Ben Bolker
5 days later
Margaret Metz <mrmetz at ...> writes:
Hello all --
I am attempting a confirmatory path analysis that uses lme4 for linear mixed effects models with hierarchically structured data (following methods in Shipley 2009 Ecology -- http://www.esajournals.org/doi/pdf/10.1890/08-1034.1). The data are about tree survival with predictors measured at the level of the individual tree or at the level of the plot within which the trees are found.
My models are triggering errors and warnings that have been the subject of quite a few recent posts on this list. I have tried some of the checks that Ben Bolker has generously suggested, but I do not know how to interpret the outcomes. The errors/warnings have also been changing as new versions of lme4 are available. I would appreciate help to know whether it is possible for me to resolve these errors or whether I should seed a different analysis approach.
Short answer -- I haven't gotten very far into this yet. It did inspire me to post a utility function for trying out all known optimizers: https://raw.githubusercontent.com/lme4/lme4/master/misc/issues/allFit.R When I try this on your data, ## setwd("~/R/pkgs/lme4git/lme4/misc/issues") source("path.data.example.R") library(lme4) fm1 <- lmer(env1 ~ env2 + env3 + (1|plot) + (1|species), data=data) source("allFit.R") ## URL above rr <- allFit(fm1) rr2 <- rr[!sapply(rr,inherits,"try-error")] sapply(rr2,fixef) ## bobyqa, nlminb, L-BFGS-B all give similar fixed effects sapply(rr2,function(x) unlist(VarCorr(x))) ## and RE variances lapply(rr2,function(x) x at optinfo$conv$lme4$messages) ## all complain lapply(rr2,function(x) x at optinfo$warnings) But my results are quite different from yours -- are we using the same subset of the data?
REML criterion at convergence: -9807.396
Random effects:
Groups Name Std.Dev.
plot (Intercept) 2.555e-01
species (Intercept) 4.179e-12
Residual 8.358e-08
Number of obs: 356, groups: plot, 24; species, 9
Fixed Effects:
(Intercept) env2 env3
0.02395 0.56535 -0.16874
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 3795.17 (tol = 0.002)
This message goes away in my local branch, where I scale the gradient differently
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge: degenerate Hessian with 1 negative eigenvalues
Some models run with the full dataset give errors that "Downdated VtV is not positive definite." Some model fits are singular, I think, but I do not fully understand the implications of that for proceeding.
You know what it means, right? (Zero variances/perfect correlations in the estimated random effects variance-covariance matrix). I haven't followed Shipley 2009, but if everything _else_ about your data were reliable, and if your focus was on the fixed-effect estimates, I might not worry about it too much. Or you could remove those terms from the model. It really depends a bit more on the context of your analysis.