[R-lang] lmer for by item and by subject analysis

[Tine Mooshammer]

I have RT for 20 subjects from a delayed naming experiment with 
different syllable structures (VC, CV, CVC etc.). Therefore, item and 
structure (the experimental condition) are confounded.

Example for the data:
sp code2 structure logAc
1 F01 cake CVC 5.544396
2 F01 cape CVC 5.459586
3 F01 Kay CV 5.450609
4 F01 lake CVC 4.830711
5 F01 lay CV 4.705016
6 F01 pape CVC 5.446306
7 F01 pate CVC 5.319590
8 F01 pay CV 5.535364
9 F01 skate CCVC 5.116795
10 F01 skay CCV 5.189060


The lmer works well for the simple model:
RTE.lmer=lmer(logAc ~ structure + (1|sp) + (1|code2), latrmE)

but I get the following error messages for the more complicated model:
RTE.lmerS=lmer(logAc ~ structure + (1+structure|sp) + (1|code2), latrmE)
Warning messages:
1: In .local(x, ..., value) :
Estimated variance-covariance for factor ‘sp’ is singular

2: In .local(x, ..., value) :
nlminb returned message false convergence (8)

Does that mean that I don't have to account for different speaker slopes 
or is there an error in the specification of the model or empty cells in 
the data (I'm not aware of that)?

Furthermore, a slightly different specification for the model seems to be

 > RTE.lmerS=lmer(logAc ~ structure + (1|sp:structure) + (1|code2), latrmE)

but then I get the following error messages:

Error in sp:structure : NA/NaN argument
In addition: Warning messages:
1: In sp:structure :
numerical expression has 520 elements: only the first used
2: In sp:structure :
numerical expression has 520 elements: only the first used
3: In inherits(x, "factor") : NAs introduced by coercion


What is difference between the two models? I'm puzzled.

Bye
Tine

-- 
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Dr. Christine Mooshammer

New address/Neue Adresse:
Haskins Laboratories
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Phone: ++1 203 865 6163 315
Email: tine at haskins.yale.edu

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