library(mgcv) set.seed(1) dat <- gamSim(eg = 1, n = 400, dist = "normal", scale = 2) ## Gu & Wahba 4 term additive modelThe simulated in dat contains the “truth” in the f variables:
str(dat) ## 'data.frame': 400 obs. of 10 variables: ## $ y : num 3.3407 -0.0758 10.6832 8.7291 14.9911 ... ## $ x0: num 0.266 0.372 0.573 0.908 0.202 ... ## $ x1: num 0.659 0.185 0.954 0.898 0.944 ... ## $ x2: num 0.8587 0.0344 0.971 0.7451 0.2733 ... ## $ x3: num 0.367 0.741 0.934 0.673 0.701 ... ## $ f : num 5.51 3.58 8.69 8.75 16.19 ... ## $ f0: num 1.481 1.841 1.948 0.569 1.184 ... ## $ f1: num 3.74 1.45 6.74 6.02 6.6 ... ## $ f2: num 2.98e-01 2.88e-01 8.61e-05 2.16 8.40 ... ## $ f3: num 0 0 0 0 0 0 0 0 0 0 ... fit_lm <- lm(f ~ f0 + f1 + f2 + f3, data = dat) plot(dat$f, predict(fit_lm)) abline(0, 1)And what can ...
Copyright © 2022 | MH Corporate basic by MH Themes