Search found 5 matches
- Tue May 21, 2024 3:15 am
- Forum: MLwiN user forum
- Topic: Finding multilevel model level three residuals
- Replies: 1
- Views: 10
Finding multilevel model level three residuals
Dear all, Thank you for reading my conundrum. For my PhD research, I am computing variables using the ecometric technique (Raudenbusch & Sampson, 1999). This requires fitting a three-level multilevel item response model (items nested in subjects nested in neighborhoods) and extracting the level ...
- Thu Apr 25, 2024 4:50 am
- Forum: MLwiN user forum
- Topic: Problem with variance absolute
- Replies: 3
- Views: 15729
Re: Problem with variance absolute
Hello, When you talk about a quadratic relationship, you're probably talking about it on a global scale, because having only two measures per child would only allow for a linear relationship. When someone uses terminology like baseline and follow-up, it implies that something happens in between, but...
- Thu Feb 01, 2024 1:49 am
- Forum: runmlwin user forum
- Topic: Running margins on predictions from a runmlwin model fit
- Replies: 5
- Views: 9161
Re: Running margins on predictions from a runmlwin model fit
Chris and George, you are very much appreciated. I am hoping that both of you are being well geometry dash lite
- Thu Jan 25, 2024 1:12 am
- Forum: MLwiN user forum
- Topic: Weighting in pooled survey analysis - 3 levels
- Replies: 2
- Views: 5132
Re: Weighting in pooled survey analysis - 3 levels
The third level of weight calculation may be performed once the samples have been aligned. Gather the first two levels' weights and combine them to get the total survey weight moto x3m. The next step is to make proportionate weight adjustments such that the third level reference population is satisf...
- Tue Jan 23, 2024 2:21 am
- Forum: R2MLwiN user forum
- Topic: mlwinfitMCMC object fitted values (propensity scores) storage
- Replies: 1
- Views: 3451
mlwinfitMCMC object fitted values (propensity scores) storage
I fitted a 2-level binomial model using mcmc. I want to apply predisposition score matching with calculated probability.
How can I convert mlwinfitMCMC fitted propensity scores (the predicted prob of Y for all obs) to numeric objects?
Base R commands are simple: p < model$fitted.values
How can I convert mlwinfitMCMC fitted propensity scores (the predicted prob of Y for all obs) to numeric objects?
Base R commands are simple: p < model$fitted.values