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 -- ++++++++++++++++++++++++++++++++ Dr. Christine Mooshammer New address/Neue Adresse: Haskins Laboratories 300 George St., Suite 900 New Haven, CT 06511 USA Phone: ++1 203 865 6163 315 Email: [EMAIL PROTECTED] +++++++++++++++++++++++++++++++++ _______________________________________________ R-lang mailing list [email protected] http://pidgin.ucsd.edu/mailman/listinfo/r-lang
