Dear R-list, I am wondering whether anyone could explain what'd be the difference between running a 'generalized additive regression' versus 'generalized linear regression' with splines.
Are they same models theoretically? My apologies if this is a silly question. Any comments or direction to references will be highly appreciated. Thanks in advance, Ehsan ##################### set.seed(545) require(mgcv) n <- 200 x1 <- c(rnorm(n), 1+rnorm(n)) x2 <- sqrt(c(rnorm(n,4),rnorm(n,6))) y <- c(rep(0,n), rep(1,n)) ##################### # GAM version ##################### r1 <- gam(y~s(x1, bs = "cr")+s(x2, bs = "cr"),family=binomial) pr1 <- predict(r1, type='response') summary(pr1) hist(pr1) ##################### # GLM version ##################### r2 <- glm(y~ns(x1)+ns(x2),family=binomial) pr2 <- predict(r2, type='response') summary(pr2) hist(pr2) ##################### # Results ##################### > summary(pr1) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000394 0.0550200 0.5027000 0.5000000 0.9322000 1.0000000 > summary(pr2) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000403 0.0573300 0.5229000 0.5000000 0.9159000 0.9992000 ______________________________________________ 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.