[R-sig-ME] LRT significant but new variable's beta not
Ben Bolker
bbolker at gmail.com
Tue Apr 19 20:37:41 CEST 2016
Good catch! That's probably it. I'm a little bit confused that
this isn't caught by lmer, as in the following example:
> library(lme4)
> ss <- subset(sleepstudy,Days<8)
> fm1 <- lmer(Reaction~Days+(1|Subject),data=sleepstudy)
> fm2 <- lmer(Reaction~(1|Subject),data=ss)
> anova(fm1,fm2)
Error in anova.merMod(fm1, fm2) :
models were not all fitted to the same size of dataset
On Tue, Apr 19, 2016 at 2:32 PM, Thompson,Paul
<Paul.Thompson at sanfordhealth.org> wrote:
> The first model has 5172 obs
> The second model has 4863 obs
>
> That is a difference of 309 obs. Clearly the new variable has a bunch of missing values. I would pay attention to that. You are fitting models in different groups, although they do overlap.
>
> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Clara Neudecker
> Sent: Tuesday, April 19, 2016 3:21 AM
> To: R-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] LRT significant but new variable's beta not
>
> Dear all,
>
> I'm looking for some hints on how to interprete my results. I have a logistic mixed effects model in which I include a single new variable.
> Comparing the old and new model with a likelihood ratio test yields a significant difference (p < .001), but when I look at the new variable's beta it's not significant at all.
>
> How do I interprete this? After thinking and googling I have only one suspicion left: Is it possible that including the new variable makes the other variables more informative because there is some kind of supressor effect in the data? Or is there another explanation?
>
> (The phenomenon cannot be a coincidence; the same happens with other variables as well.)
>
> I attach some output in case it helps.
>
> Best regards and thanks in advance,
> Clara Neudecker
>
>
>
> My model without the new variable:
>
>> summary(fm501)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: umzug50000 ~ 1 + gebjahr_c + sex + (1 | zp12401) + (1 | ror96)
> Data: master_5
>
> AIC BIC logLik deviance df.resid
> 1077.9 1110.7 -534.0 1067.9 5167
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -0.547 -0.169 -0.102 -0.065 35.188
>
> Random effects:
> Groups Name Variance Std.Dev.
> ror96 (Intercept) 0.23757 0.4874
> zp12401 (Intercept) 0.01509 0.1229
> Number of obs: 5172, groups: ror96, 96; zp12401, 7
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -4.433991 0.001499 -2957.3 <2e-16 ***
> gebjahr_c 0.075061 0.001449 51.8 <2e-16 ***
> sex.L 0.113758 0.001498 75.9 <2e-16 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) gbjhr_
> gebjahr_c -0.001
> sex.L 0.000 0.000
>
>
>
>
> With the new variable pol_fit_ror:
>
>> summary(fm510)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: umzug50000 ~ 1 + gebjahr_c + sex + pol_fit_ror + (1 | zp12401)
> + (1 | ror96)
> Data: master_5
>
> AIC BIC logLik deviance df.resid
> 1027.6 1066.5 -507.8 1015.6 4857
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -0.564 -0.171 -0.104 -0.066 33.626
>
> Random effects:
> Groups Name Variance Std.Dev.
> ror96 (Intercept) 0.2125987 0.46108
> zp12401 (Intercept) 0.0008857 0.02976
> Number of obs: 4863, groups: ror96, 88; zp12401, 7
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -4.086460 0.351692 -11.619 <2e-16 ***
> gebjahr_c 0.074675 0.006972 10.711 <2e-16 ***
> sex.L 0.158924 0.132901 1.196 0.232
> pol_fit_ror -0.256504 0.288639 -0.889 0.374
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) gbjhr_ sex.L
> gebjahr_c -0.293
> sex.L 0.084 -0.152
> pol_fit_ror -0.872 -0.030 -0.010
>
> LRT of the two models:
>
>> anova(fm501, fm510)
> Data: master_5
> Models:
> fm501: umzug50000 ~ 1 + gebjahr_c + sex + (1 | zp12401) + (1 | ror96)
> fm510: umzug50000 ~ 1 + gebjahr_c + sex + pol_fit_ror + (1 | zp12401) +
> fm510: (1 | ror96)
> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
> fm501 5 1077.9 1110.7 -533.96 1067.9
> fm510 6 1027.5 1066.5 -507.78 1015.5 52.362 1 4.615e-13 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
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