On Jun 13, 2010, at 10:20 PM, array chip wrote:
> Hi, this is not R technical question per se. I know there are many
excellent statisticians in this list, so here my questions: I have dataset with
~1800 observations and 50 independent variables, so there are about 35 samples
per variable. Is it wise to build a stable multiple logistic model with 50
independent variables? Any problem with this approach? Thanks
>
> John
The general rule of thumb is to have 10-20 'events' per covariate degree
of freedom. Frank has suggested that in some cases that number should be as high
as 25.
The number of events is the smaller of the two possible outcomes for your binary
dependent variable.
Covariate degrees of freedom refers to the number of columns in the model
matrix. Continuous variables are 1, binary factors are 1, K-level factors are K
- 1.
So if out of your 1800 records, you have at least 500 to 1000 events, depending
upon how many of your 50 variables are K-level factors and whether or not you
need to consider interactions, you may be OK. Better if towards the high end of
that range, especially if the model is for prediction versus explanation.
Two excellent references would be Frank's book:
http://www.amazon.com/Regression-Modeling-Strategies-Frank-Harrell/dp/0387952322/
and Steyerberg's book:
http://www.amazon.com/Clinical-Prediction-Models-Development-Validation/dp/038777243X/
to assist in providing guidance for model building/validation techniques.
HTH,
Marc Schwartz