Hi All,
I hope you will give me a hand with the checkConv problems. have two
datasets, vowels and qaaf, and both have many columns. I am interested in
these 8 columns clarified as follows:
1. convergence: DV (whether participants succeeded to use CA (Cairo
Arabic) instead of MA (Minia Arabic)
2. speaker: 62 participants
3. item: as pronounced
4. style: careful/casual
5. gender: males/females
6. age: continues variable
7. residence: urbanite/rural migrant/villager
8. education: secondary or below/university/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:
A. modelvowels <- glmer(convergence ~ gender + age + residence +
education + style+ (1|lexical.item) + (1|speaker), data=vowels,
family='binomial')
The message came on the screen:
B. 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:
C. modelvowels <- glmer(convergence ~ gender + age + residence +
education + (1|lexical.item) + (1|speaker), data=vowels,
family='binomial')
This produced a result. Then, I ran
D. 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
E. modelqaaf <- glmer(convergence ~ gender + real.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
F. 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:
G. modelqaaf <- glmer(convergence ~ gender + real.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:
H. modelqaaf1<- glmer(convergence ~ real.age + residence + education +
(1|lexical.item) + (1|speaker), data=qaaf,
family='binomial')
It also worked well and produced a result.
Now, my questions:
? Why did not A work, why did C work, why did not E work though it has
the same four predictors of C, why G and H worked with only three
predictors?
? What are the packages that must be installed with, before or after
the lme4 package?
Please, find attached the datasets.
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 ???? ???????
28-30th July 2015 - call for papers now open
https://sites.google.com/a/york.ac.uk/fal2015/