R glmer warnings: model fails to converge / model is nearly unidentifiable -
i have seen questions on forum, , have asked myself in previous post still haven't been able solve problem. therefore trying again, formulating question can time, detailed information possible.
my data set has binomial dependent variable, 3 categorical fixed effects , 2 categorical random effects (item , subject). using mixed effects model using glmer. here entered in r:
modelall<- glmer(moodr ~ group*context*condition + (1|subject) + ``(1|item), data=rprodhsns, family="binomial")` i 2 warnings:
warning messages: 1: in checkconv(attr(opt, "derivs"), opt$par, ctrl = control$checkconv, : model failed converge max|grad| = 0.02081 (tol = 0.001, component 11) 2: in checkconv(attr(opt, "derivs"), opt$par, ctrl = control$checkconv, : model unidentifiable: large eigenvalue ratio - rescale variables?` my summary looks this:
generalized linear mixed model fit maximum likelihood (laplace approximation) ['glmermod'] family: binomial ( logit ) formula: moodr ~ group * context * condition + (1 | subject) + (1 | item) data: rprodhsns` aic bic loglik deviance df.resid 1400.0 1479.8 -686.0 1372.0 2195 ` scaled residuals: min 1q median 3q max -8.0346 -0.2827 -0.0152 0.2038 20.6578 ` random effects: groups name variance std.dev. item (intercept) 1.475 1.215 subject (intercept) 1.900 1.378 number of obs: 2209, groups: item, 54; subject, 45 fixed effects:` estimate std. error z value pr(>|z|)` (intercept) -0.61448 42.93639 -0.014 0.988582 group1 -1.29254 42.93612 -0.030 0.975984 context1 0.09359 42.93587 0.002 0.998261 context2 -0.77262 0.22894 -3.375 0.000739*** condition1 4.99219 46.32672 0.108 0.914186 group1:context1 -0.17781 42.93585 -0.004 0.996696 group1:context2 -0.10551 0.09925 -1.063 0.287741 group1:condition1 -3.07516 46.32653 -0.066 0.947075 context1:condition1 -3.47541 46.32648 -0.075 0.940199 context2:condition1 -0.07293 0.22802 -0.320 0.749087 group1:context1:condition1 2.47882 46.32656 0.054 0.957328 group1:context2:condition1 0.30360 0.09900 3.067 0.002165 ** --- signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 correlation of fixed effects: (intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1 group1 -1.000 context1 -1.000 1.000 context2 0.001 0.000 -0.001 condition1 -0.297 0.297 0.297 0.000 grp1:cntxt1 1.000 -1.000 -1.000 0.001 -0.297 grp1:cntxt2 0.001 0.000 0.000 -0.123 0.000 0.000 grp1:cndtn1 0.297 -0.297 -0.297 -0.001 -1.000 0.297 0.000 cntxt1:cnd1 0.297 -0.297 -0.297 -0.001 -1.000 0.297 0.001 1.000 cntxt2:cnd1 0.000 0.000 -0.001 0.011 0.001 0.000 -0.197 -0.001 -0.001 grp1:cnt1:1 -0.297 0.297 0.297 0.001 1.000 -0.297 -0.001 -1.000 -1.000 0.001 grp1:cnt2:1 0.000 0.000 0.001 -0.198 0.000 -0.001 0.252 0.000 0.001 -0.136 0.000 extremely high p-values, not seem possible.
in previous post read 1 of problems fixed increasing amount of iterations inserting following in command: glmercontrol(optimizer="bobyqa", optctrl = list(maxfun = 100000))
so that's did:
modelall<- glmer(moodr ~ group*context*condition + (1|subject) + (1|item), data=rprodhsns, family="binomial", glmercontrol(optimizer="bobyqa", optctrl = list(maxfun = 100000))) now, second warning gone, first 1 still there:
> warning message: in checkconv(attr(opt, "derivs"), opt$par, ctrl = control$checkconv, : model failed converge max|grad| = 0.005384 (tol = 0.001, component 7) the summary still looks odd:
generalized linear mixed model fit maximum likelihood (laplace approximation) ['glmermod'] family: binomial ( logit ) formula: moodr ~ group * context * condition + (1 | subject) + (1 | item) data: rprodhsns control: glmercontrol(optimizer = "bobyqa", optctrl = list(maxfun = 1e+05))` aic bic loglik deviance df.resid 1400.0 1479.8 -686.0 1372.0 2195 scaled residuals: min 1q median 3q max -8.0334 -0.2827 -0.0152 0.2038 20.6610 random effects: groups name variance std.dev. item (intercept) 1.474 1.214 subject (intercept) 1.901 1.379 number of obs: 2209, groups: item, 54; subject, 45 fixed effects: estimate std. error z value pr(>|z|) (intercept) -0.64869 26.29368 -0.025 0.980317 group1 -1.25835 26.29352 -0.048 0.961830 context1 0.12772 26.29316 0.005 0.996124 context2 -0.77265 0.22886 -3.376 0.000735 *** condition1 4.97325 22.80050 0.218 0.827335 group1:context1 -0.21198 26.29303 -0.008 0.993567 group1:context2 -0.10552 0.09924 -1.063 0.287681 group1:condition1 -3.05629 22.80004 -0.134 0.893365 context1:condition1 -3.45656 22.80017 -0.152 0.879500 context2:condition1 -0.07305 0.22794 -0.320 0.748612 group1:context1:condition1 2.45996 22.80001 0.108 0.914081 group1:context2:condition1 0.30347 0.09899 3.066 0.002172 ** --- signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 correlation of fixed effects: (intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1 group1 -1.000 context1 -1.000 1.000 context2 0.000 0.000 0.000 condition1 0.123 -0.123 -0.123 -0.001 grp1:cntxt1 1.000 -1.000 -1.000 0.001 0.123 grp1:cntxt2 0.001 0.000 0.000 -0.123 0.001 0.000 grp1:cndtn1 -0.123 0.123 0.123 0.000 -1.000 -0.123 -0.001 cntxt1:cnd1 -0.123 0.123 0.123 0.000 -1.000 -0.123 0.000 1.000 cntxt2:cnd1 0.000 0.000 0.000 0.011 -0.001 0.000 -0.197 0.001 0.001 grp1:cnt1:1 0.123 -0.123 -0.123 0.000 1.000 0.123 0.000 -1.000 -1.000 -0.001 grp1:cnt2:1 0.000 -0.001 0.001 -0.198 0.001 -0.001 0.252 -0.001 0.000 -0.136 0.000 what can solve this? or can tell me warning means? (in way r-newbie myself can understand) appreciated!
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