Search found 63 matches
- Thu Aug 10, 2017 1:10 pm
- Forum: MLwiN user forum
- Topic: Zero-inflated negative binomial models?
- Replies: 5
- Views: 8481
Re: Zero-inflated negative binomial models?
Thanks for the link, Chris. Interesting article. As the article states, we have theoretical reasons to test the statistical efficiency of a multilevel zero-inflated negative binomial model and a regular multilevel negative binomial model. I assume you or Kelvyn are not aware of any (R) package that ...
- Tue Aug 08, 2017 2:31 pm
- Forum: MLwiN user forum
- Topic: Zero-inflated negative binomial models?
- Replies: 5
- Views: 8481
Zero-inflated negative binomial models?
Hello,
Can Mlwin fit zero-inflated negative binomial models? I know that we can fit Poisson and negative binomial models.
thanks in advance
Adel
Can Mlwin fit zero-inflated negative binomial models? I know that we can fit Poisson and negative binomial models.
thanks in advance
Adel
- Thu Mar 09, 2017 3:10 pm
- Forum: R2MLwiN user forum
- Topic: check the homoscedasticity assumption in logistic models
- Replies: 9
- Views: 21019
Re: check the homoscedasticity assumption in logistic models
Thanks, again, Chris. Regarding the behavior of the predict() function. 1) does it take the sample data stored in the mymodel object? 2) not getting what the difference between the terms= argument and the params= argument (from the manual and my own experimentation)? Could you please help me underst...
- Wed Mar 08, 2017 5:43 pm
- Forum: R2MLwiN user forum
- Topic: check the homoscedasticity assumption in logistic models
- Replies: 9
- Views: 21019
Re: check the homoscedasticity assumption in logistic models
Thanks for the clarifications, Chris, and some additional questions: 1) Does the predict(mymodel) function takes the mean of the covariates or the actual data values for each observation (and plug them into the model)? [it looks like it is taking the actual values…] What if I want to set some covari...
- Wed Mar 08, 2017 1:14 pm
- Forum: R2MLwiN user forum
- Topic: Using caterpillarR with NA values
- Replies: 5
- Views: 6191
Re: Using caterpillarR with NA values
ok, strange, because I specified resi.store=T, with the following options:
resi.store = TRUE, resioptions= c("standardised", "leverage", "influence", "deletion"),
Should I specify the "resioptions" differently?
resi.store = TRUE, resioptions= c("standardised", "leverage", "influence", "deletion"),
Should I specify the "resioptions" differently?
- Tue Mar 07, 2017 11:27 pm
- Forum: MLwiN user forum
- Topic: homoscedasticity assumption in logistic models?
- Replies: 3
- Views: 13024
Re: homoscedasticity assumption in logistic models?
Thanks George for the reply. So the “the standardised residual x fixed part prediction” in the Mlwin enviroment should show me this, right?
Best,
Adel
Best,
Adel
- Tue Mar 07, 2017 11:24 pm
- Forum: R2MLwiN user forum
- Topic: check the homoscedasticity assumption in logistic models
- Replies: 9
- Views: 21019
check the homoscedasticity assumption in logistic models
Hi I would like to check the homoscedasticity assumption in my logistic multilevel model. According to this post https://www.cmm.bris.ac.uk/forum/viewtopic.php?f=1&t=2006&e=1&view=unread#p4567 , “You can assess the homoskedasticity assumption by plotting the predicted random effects agai...
- Tue Mar 07, 2017 11:15 pm
- Forum: R2MLwiN user forum
- Topic: Using caterpillarR with NA values
- Replies: 5
- Views: 6191
Re: Using caterpillarR with NA values
Hi Chris, I am not sure if this is the code that is causing an error. The following is my model, with the residuals stored: > myresi <- result.l_imf.educ.collapsed.urban.int[[1]]@residual > names(myresi) [1] "lev_2_resi_est_Intercept" "lev_2_resi_se_Intercept" "lev_2_std_res...
- Fri Mar 03, 2017 7:13 pm
- Forum: MLwiN user forum
- Topic: homoscedasticity assumption in logistic models?
- Replies: 3
- Views: 13024
homoscedasticity assumption in logistic models?
Hi 1) It is usually suggested that it is not meaningful to check the homoscedasticity assumption in single level logistic models because the outcome is binary, but is it meaningful to check homoscedasticity on the log-odds scale? Probably the answer is still no, since one does not assume homoscedast...
- Sun Feb 26, 2017 11:42 am
- Forum: R2MLwiN user forum
- Topic: Using caterpillarR with NA values
- Replies: 5
- Views: 6191
Using caterpillarR with NA values
Hi I would like to draw Quantile-Quantile plots to check model assumptions. But when--and this is not uncommon-- I have missing observations, the caterpillarR throws an error at me: > caterpillarR(mymodel['residual'], lev = 2) Error in tt[1, 1, ] <- var : number of items to replace is not a multiple...