Random vs. fixed effects

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

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