On 15 April 2013 13:18, Thomas <thomasfox...@aol.com> wrote: > > Dear List, > > I am using both the clm() and clmm() functions from the R package 'ordinal'. > > I am fitting an ordinal dependent variable with 5 categories to 9 continuous > predictors, all of which have been normalised (mean subtracted then divided > by standard deviation), using a probit link function. From this global model > I am generating a confidence set of 200 models using clm() and the 'glmulti' > R package. This produces these errors: > > /> model.2.10 <- glmulti(as.factor(dependent) ~ > predictor_1*predictor_2*predictor_3*predictor_4*predictor_5*predictor_6*predictor_7*predictor_8*predictor_9, > data = database, fitfunc = clm, link = "probit", method = "g", crit = aicc, > confsetsize = 200, marginality = TRUE) > ... > After 670 generations: > Best model: > as.factor(dependent)~1+predictor_1+predictor_2+predictor_3+predictor_4+predictor_5+predictor_6+predictor_8+predictor_9+predictor_4:predictor_3+predictor_6:predictor_2+predictor_8:predictor_5+predictor_9:predictor_1+predictor_9:predictor_4+predictor_9:predictor_5+predictor_9:predictor_6 > Crit= 183.716706496392 > Mean crit= 202.022138576506 > Improvements in best and average IC have bebingo en below the specified goals. > Algorithm is declared to have converged. > Completed. > There were 24 warnings (use warnings() to see them) > > warnings() > Warning messages: > 1: optimization failed: step factor reduced below minimum > 2: optimization failed: step factor reduced below minimum > 3: optimization failed: step factor reduced below minimum/ > etc. > > > I am then re-fitting each of the 200 models with the clmm() function, with 2 > random factors (family nested within order). I get this error in a few of the > re-fitted models: > > /> model.2.glmm.2 <- clmm(as.factor(dependent) ~ 1 + predictor_1 + > predictor_2 + predictor_3 + predictor_6 + predictor_7 + predictor_8 + > predictor_9 + predictor_6:predictor_2 + predictor_7:predictor_2 + > predictor_7:predictor_3 + predictor_8:predictor_2 + predictor_9:predictor_1 + > predictor_9:predictor_2 + predictor_9:predictor_3 + predictor_9:predictor_6 + > predictor_9:predictor_7 + predictor_9:predictor_8+ (1|order/family), link = > "probit", data = database) > > summary(model.2.glmm.2) > > > Cumulative Link Mixed Model fitted with the Laplace approximation > > formula: as.factor(dependent) ~ 1 + predictor_1 + predictor_2 + predictor_3 + > predictor_6 + predictor_7 + predictor_8 + predictor_9 + > predictor_6:predictor_2 + predictor_7:predictor_2 + > predictor_7:predictor_3 + predictor_8:predictor_2 + predictor_9:predictor_1 + > predictor_9:predictor_2 + > predictor_9:predictor_3 + predictor_9:predictor_6 + predictor_9:predictor_7 + > predictor_9:predictor_8 + (1 | order/family) > data: database > > link threshold nobs logLik AIC niter max.grad cond.H > probit flexible 103 -65.56 173.13 58(3225) 8.13e-06 4.3e+03 > > Random effects: > Var Std.Dev > family:order 7.493e-11 8.656e-06 > order 1.917e-12 1.385e-06 > Number of groups: family:order 12, order 4 > > Coefficients: > Estimate Std. Error z value Pr(>|z|) > predictor_1 0.40802 0.78685 0.519 0.6041 > predictor_2 0.02431 0.26570 0.092 0.9271 > predictor_3 -0.84486 0.32056 -2.636 0.0084 ** > predictor_6 0.65392 0.34348 1.904 0.0569 . > predictor_7 0.71730 0.29596 2.424 0.0154 * > predictor_8 -1.37692 0.75660 -1.820 0.0688 . > predictor_9 0.15642 0.28969 0.540 0.5892 > predictor_2:predictor_6 -0.46880 0.18829 -2.490 0.0128 * > predictor_2:predictor_7 4.97365 0.82692 6.015 1.80e-09 *** > predictor_3:predictor_7 -1.13192 0.46639 -2.427 0.0152 * > predictor_2:predictor_8 -5.52913 0.88476 -6.249 4.12e-10 *** > predictor_1:predictor_9 4.28519 NA NA NA > predictor_2:predictor_9 -0.26558 0.10541 -2.520 0.0117 * > predictor_3:predictor_9 -1.49790 NA NA NA > predictor_6:predictor_9 -1.31538 NA NA NA > predictor_7:predictor_9 -4.41998 NA NA NA > predictor_8:predictor_9 3.99709 NA NA NA > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Threshold coefficients: > Estimate Std. Error z value > 0|1 -0.2236 0.3072 -0.728 > 1|2 1.4229 0.3634 3.915 > (211 observations deleted due to missingness) > Warning message: > In sqrt(diag(vc)[1:npar]) : NaNs produced/ >
This warning is due to a (near) singular variance-covariance matrix of the model parameters, which in turn is due to the fact that the model converged to a boundary solution: both random effects variance parameters are zero. If you exclude the random terms and refit the model with clm, the variance-covariance matrix will probably be well defined and standard errors can be computed. Another thing is that you are fitting 17 regression parameters and 2 random effect terms (which in the end do not count) to only 103 observations. I would be worried about overfitting or perhaps even non-fitting. I think I would also be concerned about the 211 observations that are incomplete, and I would be careful with automatic model selection/averaging etc. on incomplete data (though I don't know how/if glmulti actually deals with that). > > I have tried a number of different approaches, each has its own problems. I > have fixed these using various suggestions from online forums (eg > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015328.html, > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q2/016165.html) and > this is as good as I can get it. > > After the first stage (generating the model set with glmulti) I tested every > model in the confidence set individually - there were no errors - but there > was clearly a problem during the model selection process. Should I be worried? I don't know - I don't use glmulti or automatic model selection regularly, so I don't know what the consequences might be. The question seems to be what caused the potential non-convergences for some of the models that were not chosen. If they didn't converge because the models are not identifiable, then I suppose all is ok, but if they are relevant models that should have converged, then there might be a problem. However, if a model does not converge, there is usually a good reason for it, so I am not particularly worried that there are relevant models among those that did not converge. Without considering a particular model, it is hard to tell why it might not have converged, but if you can pinpoint the models that trigger the warnings/errors, I would be happy to take further look at them. Hope this helps, Rune > > No errors appear in the top 5% of re-fitted models (which are the only ones I > will be using) however I am concerned that errors may be indicative of a > problem with my approach. > > A further worry is that the errors might be removing models that could > otherwise be included. > > > Any help would be much appreciated. > > Tom > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
