[R-sig-ME] Random vs. fixed effects
Shadiya Al Hashmi
saah500 at york.ac.uk
Mon Oct 26 11:10:46 CET 2015
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 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
>
>
>
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