Re: [R] recommendation on B for validate.lrm () ?

Yes that's how it works, but a single run does not provide sufficient precision unless your sample size is enormous. When you partition into tenths again the partitions will be different so yes there is some randomness. Averaging over 100 times averages out the randomness. Or just use the bootst

Re: [R] recommendation on B for validate.lrm () ?

Thanks so much for the reply it was exceptionally helpful! A couple of questions: 1. I was under the impression that k-fold with B=10 would train on 9/10, validate on 1/10, and repeat 10 times for each different 1/10th. Is this how the procedure works in R? 2. Is the reason you recommend repea

Re: [R] recommendation on B for validate.lrm () ?

For this case B=200 should work well if using the bootstrap. For cross-val. you can use B=10-fold cross-val and repeat the process 100 times for adequate precision, averaging over the 100 as done in http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/logistic.val.pdf (note this was using the Design

[R] recommendation on B for validate.lrm () ?

I have a logistic regression model I'm trying to do k-fold cross validation on. The number of observations is approximately 550 and an event rate of about 30% Does anyone have a recommendation for a B value to use for this data set? -- View this message in context: http://r.789695.n4.nabble.co