Dear all,



Hi, a few days ago I have posted a message to my problem. Thank you very
much for your help! Now I wonder if any of you can help me with my linear
mixed model with R because I got an error message in R. I tried to
google the solution, but to no avail. To recall, I am trying to predict
manager's performance with one fixed effect and two random effects. The
fixed effect is scoring method (only 2 different methods), and the random
effects are the type of exercise (interpersonal, in-baskets... together 9
different exercises) and the managers in the study (46 managers, this
variable is named id in the datafile).



Therefore, all 46 managers engage in all 9 exercises. Each of the exercises
was assessed two ways.



I do not have problems when I examine the main effect of the variables
(performance ~ method + (1|id) + (1|exercise). However, I start to encounter
problems when I examine the interactions between these three variables. I
wonder if you have any clues why it is the case.



*When I just try to examine the main effects (without interactions), the
result is fine:*

* *

*> Model1 <- lmer(performance ~ method + (1|id) + (1|exercise), data=mtmm)*

*> summary (Model1)*

Linear mixed model fit by REML

Formula: performance ~ method + (1 | id) + (1 | exercise)

Data: mtmm

AIC BIC logLik deviance REMLdev

1617 1641 -803.6 1599 1607

Random effects:

Groups Name Variance Std.Dev.

id (Intercept) 0.113182 0.33642

exercise (Intercept) 0.020629 0.14363

Residual 0.358530 0.59877

Number of obs: 827, groups: id, 46; exercise, 9

Fixed effects:

Estimate Std. Error t value

(Intercept) 3.57527 0.07497 47.69

method 0.13553 0.04164 3.25

Correlation of Fixed Effects:

(Intr)

method -0.278



*Problems occurs, however, when I specify intearctions. For example:*

* *

*(BETWEEN TWO RANDOM EFFECTS)***

*> Model1 <- lmer(performance ~ method + (1|id) + (1|exercise) +
(1|id:exercise), data=mtmm)*

Error: length(f1) == length(f2) is not TRUE

In addition: Warning messages:

1: In id:exercise :

numerical expression has 827 elements: only the first used

2: In id:exercise :

numerical expression has 827 elements: only the first used



*(BETWEEN ONE FIXED EFFECT AND ONE RANDOM EFFECT)*

*> Model1 <- lmer(performance ~ method + (1|id) + (1|exercise) +
(1|method:exercise), mtmm)*

Error: length(f1) == length(f2) is not TRUE

In addition: Warning messages:

1: In method:exercise :

numerical expression has 827 elements: only the first used

2: In method:exercise :

numerical expression has 827 elements: only the first used

* *

*The descriptive statistics of the data is as follow:*

*> summary(mtmm)*

id exercise method performance

40,873 : 18 Min. :1.000 Min. :0.0000 Min. :1.000

40,888 : 18 1st Qu.:3.000 1st Qu.:0.0000 1st Qu.:3.200

40,894 : 18 Median :5.000 Median :1.0000 Median :3.670

40,897 : 18 Mean :4.996 Mean :0.5006 Mean :3.643

40,903 : 18 3rd Qu.:7.000 3rd Qu.:1.0000 3rd Qu.:4.200

40,915 : 18 Max. :9.000 Max. :1.0000 Max. :5.000

(Other):719

* *

*A sample data is as follow:*

id

exercise

method

performance

40873

1

0

3.2

40873

1

1

3

40873

2

0

3.2

40873

2

1

3.8

40873

3

0

4.8

40873

3

1

4

40873

4

0

2.2

40873

4

1

3.2

40873

5

0

4.73

40873

5

1

4.73

40873

6

0

4.4

40873

6

1

3

40873

7

0

3.57

40873

7

1

3.92

40873

8

0

3.8

40873

8

1

3.8

40873

9

0

4.27

40873

9

1

3.96

40888

1

0

2.4

40888

1

1

3

40888

2

0

3.6

40888

2

1

2.4

40888

3

0

3.4

40888

3

1

3

40888

4

0

3.8

40888

4

1

3.4

40888

5

0

2.87

40888

5

1

3.13

40888

6

0

3.4

40888

6

1

3.8

40888

7

0

4.75

40888

7

1

4.58

40888

8

0

3.1

40888

8

1

4.2

40888

9

0

3.79

40888

9

1

2.48

 *So I wonder what is wrong in my setup. Or, I need a supercomputer, with
plenty of processing power, in order to assess my data?*

* *

*In addition, I wonder if there is a difference between (1|exercise:method)
and (1|method:exercise), or between (1|exercise:id) and (1|id:exercise)?*

* *

*Thanks so much for help me again! I appreciate it a lot!*

* *

*Chester*

*PhD Candidate*

*University of Western Ontario*

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