Richard Ulrich <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>... > On 10 Apr 2004 09:37:16 -0700, [EMAIL PROTECTED] (Roger Levy) > wrote: > > [snip, earlier posts of his and mine] > me > > Now you have confused me, a lot. > > > By 'cases in the smaller group', I am using the common metaphor > > > of logistic regression, where the prediction is being made between > > > cases and non-cases. > RL > > > Ah, I think I misunderstood you. I'm not familiar with the > > cases/non-cases terminology of logistic regression -- could you > > explain this usage? > > I will explain by way of providing this extract from a useful > reference, which includes the point I was making - from > http://www2.chass.ncsu.edu/garson/pa765/logistic.htm > [ after a number of pages ] > "How many independents can I have? > > "There is no precise answer to this question, but the more > independents, the more likelihood of multicollinearity. > Also, if you have 20 independents, at the .05 level of > significance you would expect one to be found to be > significant just by chance. A rule of thumb is that there > should be no more than 1 independent for each 10 cases in > the sample. In applying this rule of thumb, keep in mind > that if there are categorical independents, such as > dichotomies, the number of cases should be considered to > be the lesser of the groups (ex., in a dichotomy with > 500 0's and 10 1's, effective size would be 10). " > ---- end of extract from Garson.
Thanks for the reference, but unfortunately it doesn't seem to use the term "non-case" at all so this doesn't really answer my question. > > You might find the whole document interesting to scan. > [snip, more of mine] > > > > By a "distinct covariate vector" I mean the following: with n > > covariates (i.e., predictors) X_1,...,X_n, a covariate vector is a > > value [x_1,...,x_n] for a given data point. So, for example, if I > > have a half-dozen binary covariates, there are 2^6=64 logically > > possible covariate vectors. > > Now I wonder what computer program you are using. I'm sorry, but I fail to see how that is relevant at all. The idea of a covariate vector is high-level and theoretical and has nothing to do with implementation. As a term, incidentally, "covariate vector" is quite widespread (try searching for it in "Books" on Amazon) and I don't see where the confusion is arising. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
