I have about 20 000 cases with discrete variables (some are counts, some are factors). I'm interested in fitting a series of models outcome ~ 1 outcome ~ sex outcome ~ sex + age outcome ~ age * sex outcome ~ age * sex + location ... I do NOT expect to get any statistical significance out of this at all; it's purely exploratory (this is a small sample of the full data set). I'm trying multinom (present continuous because it is not a fast method); what are recommended ways of doing it? As part of this question, what would fitting a "Naive Bayes" model look like in R?
How to fit a classification model in R?
2 messages · Richard O'Keefe, Christian Schulz
Perhaps step(lm.object) helps, what use a backward elimination of attributes , further i remind in library(leaps) some helpfuel things for modellimg selection. christian Am Dienstag, 16. M?rz 2004 05:53 schrieb Richard A. O'Keefe:
I have about 20 000 cases with discrete variables (some are counts, some are factors). I'm interested in fitting a series of models outcome ~ 1 outcome ~ sex outcome ~ sex + age outcome ~ age * sex outcome ~ age * sex + location ... I do NOT expect to get any statistical significance out of this at all; it's purely exploratory (this is a small sample of the full data set). I'm trying multinom (present continuous because it is not a fast method); what are recommended ways of doing it? As part of this question, what would fitting a "Naive Bayes" model look like in R?
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