|From||David Airey <email@example.com>|
|Subject||re: st: Re: xtlogit and logistic-cluster (REVISED)|
|Date||Sun, 8 Aug 2004 15:03:16 -0500|
David Airey queried:
But when stuck with a small data set, why not run a model designed for that data structure, as opposed to running a model not designed for the data structure? When does ignoring the clustering become more favorable to acknowledging the presence of fewer than an optimal number clusters? Why is it not the case that a good model on a small data set is not always better than a bad model on the same small data set? I hope I'm clear.
Joseph Coveney replied:
Ricardo didn't mention what the objective is of his usage. If it involves the
latter type, I could imagine a reviewer--either a journal referee or a
regulatory agency reviewer--answering David's question in stating that a good
model on a small dataset is not better than a bad model on the same dataset
when the sample size is not sufficient for the good model's intended use.
And I wouldn't count on a pre-emptive mea culpa plea acknowledging the presence
of fewer than an optimal (adequate) number of clusters to get me off the hook
in this situation.