[R-sig-ME] Issue with boundary (singular) fit: see ?isSingular

[Ben Bolker]

On 10/4/21 10:05 AM, Sasha Vasconcelos wrote:
> Hi,
> 
> I'm running a piecewise SEM with 3 component models:
> 
> lmer(response variable1 ~ predictors + (1|Point) + (1|Year), input_table)
> 
> glmer(response variable2 ~ predictors + (1| Point) + (1|Year), family =
> "binomial", input_table)
> 
> glmer(response variable3 ~ predictors + (1| Point) + (1|Year), family =
> "binomial", input_table)
> 
> Because sampling involved visiting 18 points in spring of 2018 and again in
> spring of 2019, I specified samping point and year as random effects.

   If there are only two years, it's not surprising that you'll get 
estimates of zero variance for (1|Year).  I would probably make Year a 
fixed effect.

> 
> When I run the model, this warning message appears:
> Check model convergence: log-likelihood estimates lead to negative
> Chi-squared!

   I can't find this warning message anywhere, even in the development 
branch of piecewiseSEM:

https://github.com/jslefche/piecewiseSEM/search?q=convergence

??

> 
> This message also appears:
> boundary (singular) fit: see ?isSingular
> 
>  From what I've read about the second message, it could be due to random
> effect variance estimates of zero. I checked and this happens in the 1st
> and 3rd component models. In the 1st model "Point" has zero variance, and
> in the 3rd model "Year" has zero variance.
> 
> My first question is (and I apologize in advance if this is silly to ask)
> whether this means that there's not really an effect coming from Point in
> component model 1 and from Year in component model 2? If so, would it be
> possible to remove those random effects to end up with:
> 
> lmer(Response variable1 ~ Predictors + (1|Year), input_table)
> 
> glmer(Response variable2 ~Predictors + (1| Point) + (1|Year), family =
> "binomial", input_table)
> 
> glmer(Response variable3 ~ Predictors + (1| Point), family = "binomial",
> input_table)

   Seems reasonable.
> 
> My second question is whether the warning "Check model convergence:
> log-likelihood estimates lead to negative Chi-squared!" is related to these
> singularity issues?
> 
> Oh and I am using the development version of the piecewise SEM package
> installed using devtools. This is because this version provides additional
> standardized coefficients for GLMM.
> 
> 
> Thanks!
> 
> 

-- 
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
Graduate chair, Mathematics & Statistics