That's indeed the information I needed. I found the solution, and I'll explain you how I reached it as well. Once you find your way around objects, you can solve these problems yourself rather easily. So bear with me for a moment. If you don't know what S4 classes are, please do a search on www.rseek.org and read the introductory material.
With showMethods() you can find which methods apply to the model object you have : showMethods(class=class(model)) This gives a list of formal methods, but in your case it doesn't give any useful function for extracting individual fits. Apparently no get-methods exist for the different slots in a mi.pooled object. Next step is to dive into the object itself. > str(model) Formal class 'mi.pooled' [package "mi"] with 4 slots ..@ .Data : list() ..@ call : language lmer.mi(formula = y ~ x1 + x2 + (1 | x3), mi.object = IMP) ..@ mi.pooled:List of 2 .. ..$ coefficients: Named num [1:3] 1.193 0.847 0.947 .. .. ..- attr(*, "names")= chr [1:3] "(Intercept)" "x1" "x2" .. ..$ se : Named num [1:3] 0.819 0.157 0.263 .. .. ..- attr(*, "names")= chr [1:3] "(Intercept)" "x1" "x2" ..@ mi.fit :List of 3 .. ..$ Chain1:Formal class 'mer' [package "lme4"] with 34 slots ... (tons more output) This tells you that : 1. the object is a formal class (and hence an S4 class). You can see that by the @ symbol, which is used for formal objects. If the list contains only $ symbols, then it is a pure list in the strict sense of the word. 2. There are 4 slots in the object. They can be objects again, or lists, or ... 3. The slot mi.fit is a list of 3 objects, and they appear to be of the 'mer' class. That's the S4 class which originates from the lme4 package and hence contains the fits. This is the thing you need. I ran it on the example I found in the help files of mi, so you might have more items in that list, but the idea stays the same. The correct way of reaching a slot is by either using the method for it or when that one is not there, to use the function slot() : fits <- slot(model,"mi.fit") This in turn gives a list of 3 fits, each fit for a chain. You can access them by : fits$Chain1 etc. or fits[[1]] etc. Alternatively, you can use a dirty hack : fits <- mo...@mi.fit # gives the same list fit1 <- mo...@mi.fit$chain1 # gives the fit for the first chain ... This direct accessing of slots is advised against for a number of very good reasons, but I found it handy in a number of cases, especially to extract specific data in very complex objects without having to call a number of functions. Hope this helps. Cheers Joris On Fri, Jun 18, 2010 at 12:50 PM, KDT <dkaden...@gmail.com> wrote: > > Thanks Joris and pardon me for over assuming. let me add more information. > My data is very huge and it is nested with repeated measurements. This is a > sample of the dataset. > id sex lang sch age chapt item length Resp > 1 1 0 8 27.02095 3 1 4 0 > 1 1 0 8 27.02095 3 2 10 0 > 1 1 0 8 27.02095 1 3 10 0 > 1 1 0 8 27.02095 2 4 68 0 > 1 1 0 8 27.02095 2 5 63 NA > 2 1 1 4 21.04946 3 1 4 NA > 2 1 1 4 21.04946 3 2 10 1 > 2 1 1 4 21.04946 1 3 10 0 > 2 1 1 4 21.04946 2 4 68 NA > 2 1 1 4 21.04946 2 5 63 NA > 3 1 0 1 29.69218 3 1 4 NA > 3 1 0 1 29.69218 3 2 10 1 > 3 1 0 1 29.69218 1 3 10 1 > 3 1 0 1 29.69218 2 4 68 1 > 3 1 0 1 29.69218 2 5 63 1 > 4 1 0 3 26.95328 3 1 4 0 > 4 1 0 3 26.95328 3 2 10 NA > 4 1 0 3 26.95328 1 3 10 1 > 4 1 0 3 26.95328 2 4 68 0 > 4 1 0 3 26.95328 2 5 63 NA > he imputation model and the model I am fitting are is as follows: > imp <- mi(mydata,n.iter=6,n.imp=3, rand.imp.method="bootstrap", > preprocess=F, > run.past.convergence=F, check.coef.convergence=T,add.noise=F, > post.run=F) > > model <- lmer.mi(Resp~1+ sex + lang + age + length + (1|id) + (1|item)+ > (1|sch) + (1|chapt),imp, family=binomial(link="logit")) > print(modelmi) > display(modelmi) > > After fitting a model, I can use display(model) to visualize the pooled > estimates as well as estimates of each imputed dataset. I can visualize > these also by typing print(model). > > However I would like to know how I can extract estimates of single imputed > datasets. I have tried several commands for the first imputed dataset like > mi.pooled$coefficients[[1]] , summary(model$analyses[[1]], etc etc but each > do not work and i keep getting an error "object of type 'closure' is not > subsettable" > > Hope this makes my question clear. > > Thanks > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Extract-estimates-from-each-dataset-MI-package-tp2259864p2260019.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > 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. > -- Joris Meys Statistical consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php ______________________________________________ R-help@r-project.org mailing list 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.
