[R-sig-ME] Random vs. fixed effects

[Thierry Onkelinx]

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
>
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