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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