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Simulating multilevel dataset

4 messages · Stuart Luppescu, Thompson,Paul

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On Fri, 2013-07-05 at 16:53 +0000, Thompson,Paul wrote:
The model would look something like this (in lmer notation): meas ~ var1.indic + var2.indic + (var1.indic|school) + (var2.indic|school)
Does this address your questions?

  
    
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Well, that's how it would be analyzed. But you have means and variability measures to generate the data. Do you have any notion of the dependencies/conditionalities inherent in the data? Are conditionalities additive? Are you considering some variance effects?

Prior to modeling conditionality, you need parameters to model in the direction of.

-----Original Message-----
From: Stuart Luppescu [mailto:slu at ccsr.uchicago.edu] 
Sent: Friday, July 05, 2013 12:19 PM
To: Thompson,Paul
Cc: r-sig-mixed-models
Subject: RE: [R-sig-ME] Simulating multilevel dataset
On Fri, 2013-07-05 at 16:53 +0000, Thompson,Paul wrote:
The model would look something like this (in lmer notation): meas ~ var1.indic + var2.indic + (var1.indic|school) + (var2.indic|school) Does this address your questions?
--
Stuart Luppescu <slu at ccsr.uchicago.edu>
University of Chicago


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On Fri, 2013-07-05 at 12:18 -0500, Stuart Luppescu wrote:
Oops. I forgot to indicate that there is no intercept. I guess I should
have put a -1 in there.
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On Fri, 2013-07-05 at 17:25 +0000, Thompson,Paul wrote:
The variance-covariances at the individual level are
 3.308523 1.896117
 1.896117 5.694732

at the school level they are

      0.75137       0.59118 
      0.59118       0.93246
Not sure what you mean here. I'm not assuming there are any cross-level
interactions, if that's what you're referring to.
Again, I apologize for my ignorance, but I don't understand this. The
only parameters in the model are the individual means, variances and
covariances; and the school means, variances and covariances. Do I need
something else?