Collinearity tests (e.g. VIF) for glmmTMB package
Thanks for your input, and apologies for the delay in reply. I just wanted to test to make sure there was no correlation between my explanatory variables. That's really helpful much appreciated. Mike -----Original Message----- From: Ben Bolker <bbolker at gmail.com> Sent: 09 May 2019 20:31 To: Fox, John <jfox at mcmaster.ca>; Williamson, Michael <michael.williamson at kcl.ac.uk> Cc: r-sig-mixed-models at r-project.org Subject: Re: Collinearity tests (e.g. VIF) for glmmTMB package I'm not sure what you else need to know about the component structures?
but I don't see where to recover the necessary information about the
structures of the component models from the "glmmTMB" object. I've been meaning to write a vif.glmmTMB method, but I was planning to just add a "component" argument to make the user choose: the names of the vcov() components are "cond" and "zi" (and there could be a "disp" component if there's a non-trivial dispersion model ...)
On 2019-05-09 3:28 p.m., Fox, John wrote:
Dear Mike, I'm not sufficiently familiar with the objects produced by glmmTMB() to answer definitively, and I'm also not entirely sure why you want to check for collinearity, but maybe the following would help: You can used vcov() to return the variances and covariances of coefficients in the various parts of the "glmmTMB" model. For example: ---------------- snip ------------
library(glmmTMB)
example("glmmTMB")
v <- vcov(m3)
v
Conditional model:
(Intercept) sppPR sppDM sppEC-A sppEC-L sppDES-L sppDF minedno
(Intercept) 0.04245503 -0.012754751 -0.013349646 -0.0125136751 -0.013436038 -0.013225977 -0.01391389 -0.0305911919
sppPR -0.01275475 0.077687602 0.011642383 0.0119168647 0.011903658 0.011843477 0.01185466 0.0013084323
sppDM -0.01334965 0.011642383 0.020980164 0.0117137251 0.011868129 0.011728938 0.01171587 0.0015986374
sppEC-A -0.01251368 0.011916865 0.011713725 0.0404883426 0.011904829 0.011680709 0.01185958 0.0009042868
sppEC-L -0.01343604 0.011903658 0.011868129 0.0119048294 0.017500122 0.011744878 0.01192195 0.0016761527
sppDES-L -0.01322598 0.011843477 0.011728938 0.0116807092 0.011744878 0.016968986 0.01186668 0.0015556516
sppDF -0.01391389 0.011854661 0.011715873 0.0118595830 0.011921947 0.011866683 0.02370581 0.0021442905
minedno -0.03059119 0.001308432 0.001598637 0.0009042868 0.001676153 0.001555652 0.00214429 0.0350573728
Zero-inflation model:
zi~(Intercept) zi~sppPR zi~sppDM zi~sppEC-A zi~sppEC-L zi~sppDES-L zi~sppDF zi~minedno
zi~(Intercept) 0.08027669 -0.055011989 -0.064230942 -0.056164325 -0.064230942 -0.066122481 -0.064230942 -0.028881293
zi~sppPR -0.05501199 0.157151941 0.060172003 0.062766076 0.060172003 0.059563719 0.060172003 -0.009287683
zi~sppDM -0.06423094 0.060172003 0.122669211 0.060357133 0.061653087 0.061956976 0.061653087 0.004639967
zi~sppEC-A -0.05616432 0.062766076 0.060357133 0.135723657 0.060357133 0.059862868 0.060357133 -0.007546778
zi~sppEC-L -0.06423094 0.060172003 0.061653087 0.060357133 0.122669211 0.061956976 0.061653087 0.004639967
zi~sppDES-L -0.06612248 0.059563719 0.061956976 0.059862868 0.061956976 0.123808814 0.061956976 0.007497634
zi~sppDF -0.06423094 0.060172003 0.061653087 0.060357133 0.061653087 0.061956976 0.122669211 0.004639967
zi~minedno -0.02888129 -0.009287683 0.004639967 -0.007546778 0.004639967 0.007497634 0.004639967 0.043632782
---------------- snip ------------
In this case, there are two components to the model -- the conditional model and the zero-inflation model -- and I believe that they are independent, so you should be able to eliminate the intercept from each and compute VIFs for the other coefficients:
---------------- snip ------------
diag(solve(cov2cor(v[[1]][-1, -1])))
sppPR sppDM sppEC-A sppEC-L sppDES-L sppDF minedno 1.154418 1.918674 1.340247 2.317812 2.344363 1.767413 1.006961
diag(solve(cov2cor(v[[2]][-1, -1])))
zi~sppPR zi~sppDM zi~sppEC-A zi~sppEC-L zi~sppDES-L zi~sppDF zi~minedno 1.503986 1.699895 1.614313 1.699895 1.707801 1.699895 1.079338 ---------------- snip ------------ Of course, it would be nice to automate this and to compute generalized VIFs for terms with more than one coefficient, but I don't see where to recover the necessary information about the structures of the component models from the "glmmTMB" object. I'm cc'ing Ben Bolker in case he has something to add (or correct). I hope this helps, John -------------------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: socialsciences.mcmaster.ca/jfox/
-----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r- project.org] On Behalf Of Williamson, Michael via R-sig-mixed-models Sent: Thursday, May 9, 2019 11:13 AM To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] Collinearity tests (e.g. VIF) for glmmTMB package Good Afternoon, I've been running a few generalised linear mixed models on my data. Due to convergence issues, down to the size of the data set, I was recommended to switch to the glmmTMB package from the glmer function in lme4.. The models are running much better now with no more convergence issues. I'm looking to test the collinearity of my models, but the VIF function in the car package does not work with the glmmTMB package. Does anyone know of any packages or functions that can be used to calculate collinearity from model outputs generated by glmmTMB? Many thanks, Mike Williamson Email: michael.williamson at kcl.ac.uk<mailto:michael.williamson at kcl.ac.uk> [[alternative HTML version deleted]]
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