[R-sig-ME] Random vs. fixed effects
Thierry Onkelinx
thierry.onkelinx at inbo.be
Mon Oct 26 11:25:58 CET 2015
Dear Shad,
It looks like you have complete separation in your dataset. Random effect
are slightly better at coping with that. But still the very high variance
of the random effect indicate that there is complete separation.
Best regards,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
2015-10-26 11:10 GMT+01:00 Shadiya Al Hashmi <saah500 op york.ac.uk>:
> Dear Thierry,
>
> Thanks for your response. I meant that the way the levels of stimulus are
> shown in the output does not look right. In addition, when I use stimulus
> as a fixed effect, R takes such a long time to produce the output compared
> to when I use it as a random effect.
>
> Besides, I'm warned as follows.
>
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.063422 (tol = 0.001,
> component 4)
>
> Here is how the output looks when stimulus is used as a fixed effect.
>
> > summary(m0.1)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) [
> glmerMod]
> Family: binomial ( logit )
> Formula: match ~ Listgp + stimulus + (1 | Listener)
> Data: PATdata
>
> AIC BIC logLik deviance df.resid
> 5154.3 5259.5 -2562.2 5124.3 8193
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -25.0764 -0.2706 -0.1939 0.2472 10.5131
>
> Random effects:
> Groups Name Variance Std.Dev.
> Listener (Intercept) 1.743 1.32
> Number of obs: 8208, groups: Listener, 228
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 2.7561 0.2657 10.371 < 2e-16 ***
> ListgpTA 0.1741 0.3147 0.553 0.580128
> ListgpTQ 0.0810 0.2575 0.315 0.753094
> stimulushaaDD -5.4415 0.2071 -26.272 < 2e-16 ***
> stimulushad -4.2953 0.1822 -23.569 < 2e-16 ***
> stimulushaDD -5.4946 0.2086 -26.337 < 2e-16 ***
> stimulushid -5.1519 0.1994 -25.832 < 2e-16 ***
> stimulushiDD -0.6708 0.1801 -3.724 0.000196 ***
> stimulushiid -5.8124 0.2186 -26.593 < 2e-16 ***
> stimulushiiDD -5.5101 0.2091 -26.353 < 2e-16 ***
> stimulushud -0.2016 0.1915 -1.053 0.292345
> stimulushuDD -5.6188 0.2123 -26.462 < 2e-16 ***
> stimulushuud -5.6107 0.2121 -26.450 < 2e-16 ***
> stimulushuuDD -5.3207 0.2038 -26.109 < 2e-16 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) LstgTA LstgTQ stimulushaaDD stimulushad stimulushaDD
> ListgpTA -0.613
> ListgpTQ -0.755 0.636
> stimulushaaDD -0.394 -0.007 0.004
> stimulushad -0.440 -0.006 0.005 0.605
> stimulushaDD -0.392 -0.007 0.003 0.555 0.601
> stimulushid -0.407 -0.007 0.004 0.572 0.624 0.569
> stimulushiDD -0.414 0.000 0.001 0.534 0.606 0.530
> stimulushiid -0.376 -0.006 0.003 0.536 0.578 0.533
> stimulushiiDD -0.391 -0.007 0.003 0.554 0.600 0.551
> stimulushud -0.386 0.000 0.000 0.497 0.564 0.493
> stimulushuDD -0.385 -0.007 0.003 0.548 0.592 0.545
> stimulushuud -0.386 -0.007 0.003 0.548 0.593 0.545
> stimulushuuDD -0.400 -0.007 0.004 0.564 0.613 0.561
> stimulushid stimulushiDD stimulushiid stimulushiiDD
> stimulushud
> ListgpTA
>
> ListgpTQ
>
> stimulushaaDD
>
> stimulushad
>
> stimulushaDD
>
> stimulushid
>
> stimulushiDD 0.554
>
> stimulushiid 0.549 0.506
>
> stimulushiiDD 0.568 0.529 0.533
>
> stimulushud 0.516 0.569 0.471 0.492
>
> stimulushuDD 0.562 0.521 0.527 0.544 0.484
>
> stimulushuud 0.562 0.522 0.528 0.545 0.485
>
> stimulushuuDD 0.579 0.543 0.542 0.560 0.505
>
> stimulushuDD stimulushuud
> ListgpTA
> ListgpTQ
> stimulushaaDD
> stimulushad
> stimulushaDD
> stimulushid
> stimulushiDD
> stimulushiid
> stimulushiiDD
> stimulushud
> stimulushuDD
> stimulushuud 0.539
> stimulushuuDD 0.554 0.554
>
>
> Compared to when it is used as a random effect.
>
> m0.1 <- glmer(match ~ Listgp + (1|stimulus) + (1|Listener), data =
> PATdata, family = "binomial")
> > summary(m0.1)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) [
> glmerMod]
> Family: binomial ( logit )
> Formula: match ~ Listgp + (1 | stimulus) + (1 | Listener)
> Data: PATdata
>
> AIC BIC logLik deviance df.resid
> 5218.3 5253.4 -2604.2 5208.3 8203
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -21.9276 -0.2804 -0.2059 0.2740 9.4275
>
> Random effects:
> Groups Name Variance Std.Dev.
> Listener (Intercept) 1.676 1.294
> stimulus (Intercept) 4.949 2.225
> Number of obs: 8208, groups: Listener, 228; stimulus, 12
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.3754 0.6792 -2.025 0.0429 *
> ListgpTA 0.2284 0.3073 0.743 0.4572
> ListgpTQ 0.1432 0.2513 0.570 0.5687
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) LstgTA
> ListgpTA -0.235
> ListgpTQ -0.288 0.636
>
>
> Thanks,
>
> Shad
>
>
> On 26 October 2015 at 12:47, Thierry Onkelinx <thierry.onkelinx op inbo.be>
> wrote:
>
>> Dear Shad,
>>
>> Please don't post in HTML since it makes the model output unreadable.
>>
>> You need to be more clear on "R seems not to like it".
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>> and Forest
>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>> Kliniekstraat 25
>> 1070 Anderlecht
>> Belgium
>>
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> 2015-10-26 10:37 GMT+01:00 Shadiya Al Hashmi <saah500 op york.ac.uk>:
>>
>>> I'm using a binomial glmer mixed effects model.
>>>
>>> One variable that I have, 'stimulus' has 12 levels. The levels were not
>>> randomly selected but were rather chosen as per the study design, so I
>>> have
>>> used the variable “stimulus” as a fixed variable in the basic model but R
>>> seems not to like it (at least this is my interpretation) given the way
>>> the
>>> output looks and the amount of time R takes to process it.
>>>
>>> m0.1 <- glmer(match ~ Listgp + stimulus + (1|Listener), data = PATdata,
>>> family = "binomial")
>>>
>>>
>>>
>>> summary(m0.1) Generalized linear mixed model fit by maximum likelihood
>>> (Laplace Approximation) [ glmerMod] Family: binomial ( logit ) Formula:
>>> match ~ Listgp + stimulus + (1 | Listener) Data: PATdata
>>>
>>> AIC BIC logLik deviance df.resid
>>>
>>> 5154.3 5259.5 -2562.2 5124.3 8193
>>>
>>>
>>>
>>> Scaled residuals: Min 1Q Median 3Q Max -25.0764 -0.2706 -0.1939 0.2472
>>> 10.5131
>>>
>>>
>>>
>>> Random effects: Groups Name Variance Std.Dev. Listener (Intercept) 1.743
>>> 1.32
>>>
>>> Number of obs: 8208, groups: Listener, 228
>>>
>>>
>>>
>>> Fixed effects: Estimate Std. Error z value Pr(>|z|)
>>>
>>> (Intercept) 2.7561 0.2657 10.371 < 2e-16 * ListgpTA 0.1741 0.3147 0.553
>>> 0.580128
>>>
>>> ListgpTQ 0.0810 0.2575 0.315 0.753094
>>>
>>> stimulushaaDD -5.4415 0.2071 -26.272 < 2e-16 stimulushad -4.2953 0.1822
>>> -23.569 < 2e-16 stimulushaDD -5.4946 0.2086 -26.337 < 2e-16 stimulushid
>>> -5.1519 0.1994 -25.832 < 2e-16 stimulushiDD -0.6708 0.1801 -3.724
>>> 0.000196
>>> stimulushiid -5.8124 0.2186 -26.593 < 2e-16 stimulushiiDD -5.5101 0.2091
>>> -26.353 < 2e-16 stimulushud -0.2016 0.1915 -1.053 0.292345
>>>
>>> stimulushuDD -5.6188 0.2123 -26.462 < 2e-16 stimulushuud -5.6107 0.2121
>>> -26.450 < 2e-16 *
>>>
>>>
>>>
>>> stimulushuuDD -5.3207 0.2038 -26.109 < 2e-16 ***
>>>
>>>
>>>
>>> Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>
>>>
>>>
>>> Correlation of Fixed Effects: (Intr) LstgTA LstgTQ stimulushaaDD
>>> stimulushad stimulushaDD ListgpTA -0.613
>>>
>>> ListgpTQ -0.755 0.636
>>>
>>> stimulushaaDD -0.394 -0.007 0.004
>>>
>>> stimulushad -0.440 -0.006 0.005 0.605
>>>
>>> stimulushaDD -0.392 -0.007 0.003 0.555 0.601
>>>
>>> stimulushid -0.407 -0.007 0.004 0.572 0.624 0.569
>>>
>>> stimulushiDD -0.414 0.000 0.001 0.534 0.606 0.530
>>>
>>> stimulushiid -0.376 -0.006 0.003 0.536 0.578 0.533
>>>
>>> stimulushiiDD -0.391 -0.007 0.003 0.554 0.600 0.551
>>>
>>> stimulushud -0.386 0.000 0.000 0.497 0.564 0.493
>>>
>>> stimulushuDD -0.385 -0.007 0.003 0.548 0.592 0.545
>>>
>>> stimulushuud -0.386 -0.007 0.003 0.548 0.593 0.545
>>>
>>> stimulushuuDD -0.400 -0.007 0.004 0.564 0.613 0.561
>>>
>>> stimulushid stimulushiDD stimulushiid stimulushiiDD stimulushud ListgpTA
>>>
>>> ListgpTQ
>>>
>>> stimulushaaDD
>>>
>>> stimulushad
>>>
>>> stimulushaDD
>>>
>>> stimulushid
>>>
>>> stimulushiDD 0.554
>>>
>>> stimulushiid 0.549 0.506
>>>
>>> stimulushiiDD 0.568 0.529 0.533
>>>
>>> stimulushud 0.516 0.569 0.471 0.492
>>>
>>> stimulushuDD 0.562 0.521 0.527 0.544 0.484
>>>
>>> stimulushuud 0.562 0.522 0.528 0.545 0.485
>>>
>>> stimulushuuDD 0.579 0.543 0.542 0.560 0.505
>>>
>>> stimulushuDD stimulushuud ListgpTA
>>>
>>> ListgpTQ
>>>
>>> stimulushaaDD
>>>
>>> stimulushad
>>>
>>> stimulushaDD
>>>
>>> stimulushid
>>>
>>> stimulushiDD
>>>
>>> stimulushiid
>>>
>>> stimulushiiDD
>>>
>>> stimulushud
>>>
>>> stimulushuDD
>>>
>>> stimulushuud 0.539
>>>
>>> stimulushuuDD 0.554 0.554
>>>
>>> So, my question is, can I consider 'stimulus' as a random effect instead
>>> since the model become more sensible from a programming point of view?
>>>
>>> m0.1 <- glmer(match ~ Listgp + (1|stimulus) + (1|Listener), data =
>>> PATdata,
>>> family = "binomial") summary(m0.1) Generalized linear mixed model fit by
>>> maximum likelihood (Laplace Approximation) [ glmerMod] Family: binomial (
>>> logit ) Formula: match ~ Listgp + (1 | stimulus) + (1 | Listener) Data:
>>> PATdata
>>>
>>> AIC BIC logLik deviance df.resid
>>>
>>> 5218.3 5253.4 -2604.2 5208.3 8203
>>>
>>>
>>>
>>> Scaled residuals: Min 1Q Median 3Q Max -21.9276 -0.2804 -0.2059 0.2740
>>> 9.4275
>>>
>>>
>>>
>>> Random effects: Groups Name Variance Std.Dev. Listener (Intercept) 1.676
>>> 1.294
>>>
>>> stimulus (Intercept) 4.949 2.225
>>>
>>> Number of obs: 8208, groups: Listener, 228; stimulus, 12
>>>
>>>
>>>
>>> Fixed effects: Estimate Std. Error z value Pr(>|z|)
>>>
>>> (Intercept) -1.3754 0.6792 -2.025 0.0429 * ListgpTA 0.2284 0.3073 0.743
>>> 0.4572
>>>
>>>
>>>
>>> ListgpTQ 0.1432 0.2513 0.570 0.5687
>>>
>>>
>>>
>>> Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>
>>>
>>>
>>> Correlation of Fixed Effects: (Intr) LstgTA ListgpTA -0.235
>>>
>>> ListgpTQ -0.288 0.636
>>>
>>>
>>>
>>> Thank you,
>>>
>>> Shad
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models op r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>
>
>
>
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