Hi All, I have two datasets, vowels and qaaf, and both have 8 columns clarified as follows:
1. convergence: DV (whether participants succeeded to use CA (Cairene Arabic) or fail to do so; hence, they use MA (Minia Arabic) 2. speaker: 62 participants 3. lexical.item: as pronounced 4. style: careful and casual 5. gender: males and females 6. age: continues variable 7. residence: urbanite, migrant to town or villager 8. education: secondary or below, university or postgraduate The only difference between the two datasets is the number of items. With the vowels dataset, there are 1339 items; in the qaaf dataset there are 4064 items. The aim of the test done was to know which independent variable is more responsible for using CA forms. I used the lme4 package, function glmer. I ran the model: 1. modelvowels <- glmer(convergence ~ gender + age + residence + education + style+ (1|lexical.item) + (1|speaker), data=vowels, family='binomial') The message came on the screen: 2. Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00210845 (tol = 0.001, component 1) Then I ran the model after removing STYLE as follows: 3. modelvowels <- glmer(convergence ~ gender + age + residence + education + (1|lexical.item) + (1|speaker), data=vowels, family='binomial') This produced a result. Then, I ran 4. plot(allEffects(modelvowels)) and this gave four charts (for the four independent variables: gender, age, residence and education). Then, I moved to the qaaf dataset (4064 items) and ran the same model 5. modelqaaf <- glmer(convergence ~ gender + age + residence + education + (1|lexical.item) + (1|speaker), data=qaaf, family='binomial') which gave results with the vowels dataset but there was a warning message this time 6. Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.429623 (tol = 0.001, component 8) So, I removed one independent variable (residence) and ran this model again: 7. modelqaaf <- glmer(convergence ~ gender + age + education + (1|lexical.item) + (1|speaker), data=qaaf, family='binomial') This gave a result. I removed another independent variable (gender) after returning (residence) and ran the model: 8. modelqaaf1 <- glmer(convergence ~ residence + age + education + (1|lexical.item) + (1|speaker), data=q, family='binomial') This gave a result as well. Then, I tried to create some graphs using 9. plot(allEffects(modelqaaf)) and 10. plot(allEffects(modelqaaf1)) but there was the same error for both 11. Error in as.data.frame.default(data, optional = TRUE) : cannot coerce class ""function"" to a data.frame Now, my questions: a. why 1 did not work, why 3 worked, why 5 did not work though it has the same four IVs of 3, why 7 and 8 worked with only three IVs, and why 9 and 10 did not work though they are like 4 which worked well. b. What are the packages that must be installed with, before or after the lme4 package? Best -- Saudi Sadiq, Assistant Lecturer, English Department, Faculty of Al-Alsun,Minia University, Minia City, Egypt & PhD Student, Language and Linguistic Science Department, University of York, York, North Yorkshire, UK, YO10 5DD http://york.academia.edu/SaudiSadiq https://www.researchgate.net/profile/Saudi_Sadiq Certified Interpreter by Pearl Linguistics Forum for Arabic Linguistics conference رواق العربية 28-30th July 2015 - call for papers now open https://sites.google.com/a/york.ac.uk/fal2015/ [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
