Mlwin supplement: autocorrelated errors in continuous time
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Mlwin supplement: autocorrelated errors in continuous time
Hello. I am hoping someone will be able to clarify a point on this for me. I see from the Mlwin supplement, when fitting a multilevel/longitudinal model to an outcome which is being treated as continuous, it is possible to estimate both between subject/within subject variances, and additionally allow for autocorrelated errors at level 1. However, when fitting a multilevel model to an outcome which is non-normal, my understanding is that common options are either to fit a population averaged model (which allows for autocorrelation between successive timepoints), or a subject specific model (which allows for between subject variances, with the within subject variances a fixed function of pi). Does this mean then that with non-normal outcomes it is not possible to simultaneously fit a subject specific model and also allow for autocorrelated errors at level 1, in the way one can with normal outcomes, and if so is there a simple mathematical explanation for this?
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Re: Mlwin supplement: autocorrelated errors in continuous time
That's right. If you have a binary response you need to move to a multivariate model. see
Barbosa and Goldstein (2000), Quality and quantity, 34, pp323-330
Barbosa and Goldstein (2000), Quality and quantity, 34, pp323-330
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Re: Mlwin supplement: autocorrelated errors in continuous time
Thanks very much for clarifying this and pointing me in the right direction. Is it the case that the multivariate approach only works for Bernoulli responses (ie 0 or 1) rather than grouped binomial data (eg 3 successes out of 7 trials) in Mlwin?