Thank you very much for your help, Prof. Harrell. I was making the bad mistake of judging the appearance of the calibration plots without actually calculating the regression line. I was misjudging slopes of 0.8 or 0.9 as being slopes greater than 1.
Kind regards, Mark Seeto > Mark, > > Try > > set.seed(1) > slope1 <- slope2 <- numeric(100) > > for(i in 1:100) { > x1 <- rnorm(200, 0, 1) > x2 <- rnorm(200, 0, 1) > x3 <- rnorm(200, 0, 1) > x4 <- rnorm(200, 0, 1) > x5 <- rnorm(200, 0, 1) > x6 <- rnorm(200, 0, 1) > y <- x1 + x2 + rnorm(200, 0, 2) > d <- data.frame(y, x1, x2, x3, x4, x5, x6) > > lm1 <- lm(y ~ ., data = d[1:100,]) > lm2 <- lm(y ~ x1 + x2, data = d[1:100,]) > > slope1[i] <- coef(lsfit(predict(lm1, d[101:200, ]), d$y[101:200]))[2] > slope2[i] <- coef(lsfit(predict(lm2, d[101:200, ]), d$y[101:200]))[2] > } > > mean(slope1) > mean(slope2) > > I get > >> [1] 0.8873878 >> [1] 0.9603158 > > Frank > > > >> >> ______________________________________________ >> 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 E Harrell Jr Professor and Chairman School of Medicine > Department of Biostatistics Vanderbilt University > ______________________________________________ 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.