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 question. > During analysis of this model I found that the confidence > intervals (CIs) of some coefficients provided by bootstrapping (bootcov > function in rms package) was narrower than CIs provided by usual > variance-covariance matrix and CIs of other coefficients wider. My data > has no cluster structure. I am wondering which CIs are better. > I guess bootstrapping one, but is it right? > > I would appreciate your help in advance. > -- > KH > > > > (11/05/16 12:25), Frank Harrell wrote: >> 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 >> nomogram on the entire model. >> Frank >> >> >> KH wrote: >>> >>> 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 step-down technique predicting L from all of the >>> componet variables using ordinary least squares (ols in rms package) as >>> the followings. I would like to know whether I am doing right or not. >>> >>>> library(rms) >>>> plogit<- predict(full.model) >>>> full.ols<- ols(plogit ~ stenosis+x1+x2+ClinicalScore+procedure, >>>> sigma=1) >>>> fastbw(full.ols, aics=1e10) >>> >>> Deleted Chi-Sq d.f. P Residual d.f. P AIC R2 >>> stenosis 1.41 1 0.2354 1.41 1 0.2354 -0.59 0.991 >>> x2 16.78 1 0.0000 18.19 2 0.0001 14.19 0.882 >>> procedure 26.12 1 0.0000 44.31 3 0.0000 38.31 0.711 >>> ClinicalScore 25.75 1 0.0000 70.06 4 0.0000 62.06 0.544 >>> x1 83.42 1 0.0000 153.49 5 0.0000 143.49 0.000 >>> >>> Then, fitted an approximation to the full model using most imprtant >>> variable (R^2 for predictions from the reduced model against the >>> original Y drops below 0.95), that is, dropping "stenosis". >>> >>>> full.ols.approx<- ols(plogit ~ x1+x2+ClinicalScore+procedure) >>>> full.ols.approx$stats >>> n Model L.R. d.f. R2 g Sigma >>> 104.0000000 487.9006640 4.0000000 0.9908257 1.3341718 0.1192622 >>> >>> This approximate model had R^2 against the full model of 0.99. >>> Therefore, I updated the original full logistic model dropping >>> "stenosis" as predictor. >>> >>>> full.approx.lrm<- update(full.model, ~ . -stenosis) >>> >>>> validate(full.model, bw=F, B=1000) >>> index.orig training test optimism index.corrected n >>> Dxy 0.6425 0.7017 0.6131 0.0887 0.5539 1000 >>> R2 0.3270 0.3716 0.3335 0.0382 0.2888 1000 >>> Intercept 0.0000 0.0000 0.0821 -0.0821 0.0821 1000 >>> Slope 1.0000 1.0000 1.0548 -0.0548 1.0548 1000 >>> Emax 0.0000 0.0000 0.0263 0.0263 0.0263 1000 >>> >>>> validate(full.approx.lrm, bw=F, B=1000) >>> index.orig training test optimism index.corrected n >>> Dxy 0.6446 0.6891 0.6265 0.0626 0.5820 1000 >>> R2 0.3245 0.3592 0.3428 0.0164 0.3081 1000 >>> Intercept 0.0000 0.0000 0.1281 -0.1281 0.1281 1000 >>> Slope 1.0000 1.0000 1.1104 -0.1104 1.1104 1000 >>> Emax 0.0000 0.0000 0.0444 0.0444 0.0444 1000 >>> >>> Validatin revealed this approximation was not bad. >>> Then, I made a nomogram. >>> >>>> full.approx.lrm.nom<- nomogram(full.approx.lrm, >>> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis) >>>> plot(full.approx.lrm.nom) >>> >>> Another nomogram using ols model, >>> >>>> full.ols.approx.nom<- nomogram(full.ols.approx, >>> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis) >>>> plot(full.ols.approx.nom) >>> >>> These two nomograms are very similar but a little bit different. >>> >>> My questions are; >>> >>> 1. Am I doing right? >>> >>> 2. Which nomogram is correct >>> >>> I would appreciate your help in advance. >>> >>> -- >>> KH >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> >> >> ----- >> Frank Harrell >> Department of Biostatistics, Vanderbilt University >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/Question-on-approximations-of-full-logistic-regression-model-tp3524294p3525372.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > > E-mail address > Office: khos...@med.kobe-u.ac.jp > Home : khos...@venus.dti.ne.jp > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Question-on-approximations-of-full-logistic-regression-model-tp3524294p3526155.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
