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.