[R-sig-ME] Random vs. fixed effects
Highland Statistics Ltd
highstat at highstat.com
Mon Oct 26 11:23:51 CET 2015
> ------------------------------
>
> Message: 3
> Date: Mon, 26 Oct 2015 13:10:46 +0300
> From: Shadiya Al Hashmi <saah500 at york.ac.uk>
> To: Thierry Onkelinx <thierry.onkelinx at inbo.be>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Random vs. fixed effects
> Message-ID:
> <CACrevpmwqyoh1QHS3+WpwsimiBmP6NovYwn_-bUgunVOi6-Ukg at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Dear Thierry,
>
> Thanks for your response. I meant that the way the levels of stimulus are
> shown in the output does not look right.
What exactly 'doesn't look right'? How did you expect it to look? Do you
mean the order of the levels?
> 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.
Maybe you want to share the information how many observations per level
of your stimulus you have?
Without the data and R code it is not easy to give a sensible answer.
As to the warning message....see:
http://glmm.wikidot.com/faq
Alain Zuur
>
> 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 at 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 at 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 at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
> [[alternative HTML version deleted]]
>
>
>
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>
--
Dr. Alain F. Zuur
First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).
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