Re: [R] Question on approximations of full logistic regression model

Thank you for your advice, Tim. I am reading your paper and other materials in your website. I could not find R package of your bootknife method. Is there any R package for this procedure? (11/05/17 14:13), Tim Hesterberg wrote: > My usual rule is that whatever gives the widest confidence interva

Re: [R] Question on approximations of full logistic regression model

My usual rule is that whatever gives the widest confidence intervals in a particular problem is most accurate for that problem :-) Bootstrap percentile intervals tend to be too narrow. Consider the case of the sample mean; the usual formula CI is xbar +- t_alpha sqrt( (1/(n-1)) sum((x_i - xbar

Re: [R] Question on approximations of full logistic regression model

Thank you for your comment, Prof. Harrell. I would appreciate it very much if you could teach me how to simulate for the estimation. For reference, following codes are what I did (bootcov, summary, and validation). MyFullModel.boot <- bootcov(MyFullModel, B=1000, coef.reps=T) > summary(MyFull

Re: [R] Question on approximations of full logistic regression model

The choice is not clear, and requires some simulations to estimate the average absolute error of the covariance matrix estimators. Frank 細田弘吉 wrote: > > Thank you for your reply, Prof. Harrell. > > I agree with you. Dropping only one variable does not actually help a lot. > > I have one more q

Re: [R] Question on approximations of full logistic regression model

Thank you for your reply, Prof. Harrell. I agree with you. Dropping only one variable does not actually help a lot. I have one more question. During analysis of this model I found that the confidence intervals (CIs) of some coefficients provided by bootstrapping (bootcov function in rms package

Re: [R] Question on approximations of full logistic regression model

I think you are doing this correctly except for one thing. The validation and other inferential calculations should be done on the full model. Use the approximate model to get a simpler nomogram but not to get standard errors. With only dropping one variable you might consider just running the n

[R] Question on approximations of full logistic regression model

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by