Nothing
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(aspline) library(ggplot2) library(dplyr) library(tidyr) library(mgcv) library(splines2) data(helmet) x <- helmet$x y <- helmet$y
k <- 40 knots <- seq(min(x), max(x), length = k + 2)[-c(1, k + 2)] pen <- 10 ^ seq(-4, 4, 0.25) x_seq <- seq(min(x), max(x), length = 1000) aridge <- aspline::aspline(x, y, knots, pen) a_fit <- lm(y ~ splines2::bSpline(x, knots = aridge$knots_sel[[which.min(aridge$ebic)]])) X_seq <- splines2::bSpline(x_seq, knots = aridge$knots_sel[[which.min(aridge$ebic)]], intercept = TRUE) a_basis <- (X_seq %*% diag(coef(a_fit))) %>% as.data.frame() %>% dplyr::mutate(x = x_seq) %>% tidyr::pivot_longer(cols = paste0("V", 1:9), names_to = "spline_n", values_to = "y") %>% dplyr::filter(y != 0) a_predict <- dplyr::data_frame(x = x_seq, pred = predict(a_fit, data.frame(x = x_seq))) ggplot2::ggplot() + ggplot2::geom_point(data = helmet, aes(x, y), shape = 1) + ggplot2::geom_line(data = a_predict, aes(x, pred), size = 0.5) + ggplot2::geom_line(data = a_basis, aes(x, y, group = spline_n), linetype = 1, size = 0.1) + ggplot2::theme(legend.position = "none") + ggplot2::ylab("") + ggplot2::xlab("")
p_fit <- mgcv::gam(y ~ s(x, bs = "ps", k = length(knots) + 3 + 1, m = c(3, 2))) X <- splines2::bSpline(x_seq, knots = knots, intercept = TRUE) p_basis <- (X %*% diag(coef(p_fit))) %>% as.data.frame() %>% dplyr::mutate(x = x_seq) %>% tidyr::pivot_longer(cols = paste0("V", 1:9), names_to = "spline_n", values_to = "y") %>% dplyr::as_tibble() %>% dplyr::filter(y != 0) p_predict <- dplyr::data_frame(x = x_seq, pred = predict(p_fit, data.frame(x = x_seq))) ggplot2::ggplot() + ggplot2::geom_point(data = helmet, aes(x, y), shape = 1) + ggplot2::geom_line(data = p_predict, aes(x, pred), size = 0.5) + ggplot2::geom_line(data = p_basis, aes(x, y, group = spline_n), linetype = 1, size = 0.1) + ggplot2::theme(legend.position = "none") + ggplot2::ylab("") + ggplot2::xlab("")
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