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

[Shadiya Al Hashmi]

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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