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June 22, 2017 21:15
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# Piecewise basis functions: splines | |
# ch. 5 of ESL: Hastie, Tibshirani & Friedman (2009) | |
# one dimension (p=1) | |
x <- seq(-0.5,1,by=0.05)[-11] | |
y <- 2 - 5*exp(-8 * x^2) + rnorm(length(x), sd=0.5) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
# kNN regression: piecewise constant | |
library(FNN) | |
x.star <- seq(-0.5,1,by=0.025) | |
kNN.fit <- knn.reg(x, y=y, test=data.frame(x=x.star)) | |
lines(x.star, kNN.fit$pred, col=2) | |
# linear basis: degree=1 | |
B <- splines::bs(sort(x), df=5, intercept = TRUE, Boundary.knots=c(-0.5,1), degree=1) | |
dim(B) | |
plot(c(0,0),c(1,1), type='n', xlab='X', ylab='B(X)',xlim=c(-0.5,1),ylim=c(0,1)) | |
#rug(x) | |
rug(attr(B,'knots'), col=2) | |
rug(attr(B,'Boundary.knots'), col=2) | |
#abline(v=attr(B,"knots"), lty=2, col=4) | |
#abline(v=attr(B,"Boundary.knots"), lty=3, col=2) | |
bpred <- predict(B, x.star) | |
for (i in 1:5) { | |
lines(x.star, bpred[,i], col=i, lty=i) | |
} | |
title(main="5 linear B-spline basis functions") | |
sp1 <- lm.fit(B, y) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
lines(x.star, kNN.fit$pred, col=2) | |
lines(x, sp1$fitted.values, col=3, lwd=2) | |
rug(attr(B,'knots'), col=3, lwd=2) | |
rug(attr(B,'Boundary.knots'), col=3, lwd=2) | |
legend("bottomright",pch=c(NA,1,NA,NA,NA),lty=c(1,NA,1,1,1),col=c(4,1,2,3),lwd=c(1,1,1,2), | |
legend=c("true function","observations","kNN regression","linear B-spline (5 knots)")) | |
# now use cubic basis | |
B <- splines::bs(sort(x), df=7, intercept = TRUE, Boundary.knots=c(-0.5,1)) | |
plot(c(0,0),c(1,1), type='n', xlab='X', ylab='B(X)',xlim=c(-0.5,1),ylim=c(0,1)) | |
#rug(x) | |
rug(attr(B,'knots'), col=2) | |
rug(attr(B,'Boundary.knots'), col=2) | |
bpred <- predict(B, x.star) | |
for (i in 1:7) { | |
lines(x.star, bpred[,i], col=i, lty=i) | |
} | |
title(main="7 cubic B-spline basis functions") | |
sp2 <- lm.fit(B, y) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
lines(x, sp1$fitted.values, col=3) | |
lines(x, sp2$fitted.values, col=6, lwd=2) | |
rug(attr(B,'knots'), col=6) | |
rug(attr(B,'Boundary.knots'), col=6) | |
legend("bottomright",pch=c(NA,1,NA,NA,NA),lty=c(1,NA,1,1,1),col=c(4,1,3,6),lwd=c(1,1,1,2), | |
legend=c("true function","observations","linear B-spline (5 knots)", | |
"cubic B-spline (5 knots)")) | |
# extrapolating outside the range of the data | |
plot(c(-1,1.5),c(-2,2), type='n', xlab='X', ylab='B(X)',xlim=c(-1,1.5),ylim=c(-2,2)) | |
#rug(x) | |
rug(attr(B,'knots'), col=2) | |
rug(attr(B,'Boundary.knots'), col=2) | |
newx <- seq(-1,1.5,by=0.025) | |
bpred <- predict(B, newx) | |
for (i in 1:7) { | |
lines(newx, bpred[,i], col=i, lty=i) | |
} | |
range(bpred) | |
# natural cubic splines | |
B <- splines::ns(sort(x), df=7, intercept = TRUE, Boundary.knots=c(-0.5,1)) | |
plot(c(-1,1.5),c(-2,2), type='n', xlab='X', ylab='B(X)',xlim=c(-1,1.5),ylim=c(-2,2)) | |
#rug(x) | |
rug(attr(B,'knots'), col=2) | |
rug(attr(B,'Boundary.knots'), col=2) | |
bpred <- predict(B, newx) | |
for (i in 1:7) { | |
lines(newx, bpred[,i], col=i, lty=i) | |
} | |
range(bpred) | |
sp3 <- lm.fit(B, y) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
lines(x, sp1$fitted.values, col=3) | |
lines(x, sp2$fitted.values, col=6) | |
lines(x, sp3$fitted.values, col=1) | |
legend("bottomright",pch=c(NA,1,NA,NA,NA,NA),lty=c(1,NA,1,1,1,1),col=c(4,1,3,6,1), | |
legend=c("true function","observations","linear B-spline (5 knots)", | |
"cubic B-spline (5 knots)","natural cublic spline")) | |
# increase the number of knots: overfitting! | |
B <- splines::ns(sort(x), df=20, intercept = TRUE, Boundary.knots=c(-0.5,1)) | |
image(B) | |
sp4 <- lm.fit(B, y) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
lines(x, sp3$fitted.values, col=1) | |
lines(x, sp4$fitted.values, col=2) | |
rug(attr(B,'knots'), col=2) | |
rug(attr(B,'Boundary.knots'), col=2) | |
legend("bottomright",pch=c(NA,1,NA,NA,NA),lty=c(1,NA,1,1,1),col=c(4,1,1,2), | |
legend=c("true function","observations","B-spline (5 knots)","B-spline (20 knots)")) | |
# Sect. 5.4: Penalised Splines | |
sp4$coefficients | |
prior.betaSD <- 3 | |
NB <- 20 | |
prior.betaCov <- diag(prior.betaSD^2, NB, NB) | |
n.iter <- 20 | |
pen <- 0.1 # fixed smoothing penalty, lambda | |
# prior precision matrix for the spline coefficients, beta | |
library(Matrix) | |
B <- splines::bs(sort(x), df=20, intercept = TRUE, Boundary.knots=c(-0.5,1)) | |
# matrix of 2nd derivatives (Eilers & Marx, 1996) | |
# WARNING: assumes equidistant knots! | |
D <- diag(NB) | |
D <- diff(diff(D)) | |
g0 <- Matrix(crossprod(D), sparse=TRUE) | |
bTb <- Matrix(crossprod(B), sparse=TRUE) | |
giPrecMx <- bTb + length(y)*pen*g0 | |
giChol <- Cholesky(giPrecMx) | |
beta.mu <- as.vector(solve(giChol, crossprod(B, y))) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
bpred <- predict(B, x.star) | |
lines(x.star, bpred %*% beta.mu, col=2) | |
#lines(x.star, bpred %*% sp4$coefficients, col=6) | |
# inverse gamma prior for the noise | |
priorNoiseNu <- 4 | |
priorNoiseSD <- 1 | |
priorNoiseSS <- priorNoiseNu * priorNoiseSD^2 | |
sqDiff <- sum(diag(crossprod(y))) - sum(diag(t(beta.mu) %*% giPrecMx %*% beta.mu)) | |
newSS = (priorNoiseSS + sqDiff)/2.0 | |
sdVec = rgamma(n.iter, (priorNoiseNu + length(y))/2.0) | |
newTau = sdVec / newSS; | |
sigma_prop = 1/sqrt(newTau) | |
# posterior samples of the spline function | |
beta.samp <- matrix(nrow=n.iter, ncol=NB) | |
for (it in 1:n.iter) { | |
stdNorm <- rnorm(NB) | |
blChol <- Cholesky(giPrecMx * newTau[it]) | |
beta.samp[it,] <- as.vector(beta.mu + solve(blChol, stdNorm)) | |
lines(x.star, bpred %*% beta.samp[it,], col=2, lty=3) | |
} | |
# integrated squared second derivative (Green & Silverman, 1994) | |
B <- splines::ns(sort(x), df=20, intercept = TRUE, Boundary.knots=c(-0.5,1)) | |
t <- c(attr(B,'Boundary.knots')[1], attr(B,'knots'), attr(B,'Boundary.knots')[2]) | |
NK <- length(t) | |
rug(t,col=2) | |
h <- diff(t) | |
Q <- matrix(0,nrow=NK,ncol=NK-2) | |
for (j in 2:(NK-1)) { | |
Q[(j-1),(j-1)] <- 1/h[j-1] | |
Q[j,(j-1)] <- -1/h[j-1] - 1/h[j] | |
Q[(j+1),(j-1)] <- 1/h[j] | |
} | |
R <- matrix(0,nrow=NK-2,ncol=NK-2) | |
for (i in 2:(NK-2)) { | |
R[(i-1),(i-1)] <- (h[i-1] + h[i])/3 | |
R[(i-1),i] <- R[i,(i-1)] <- h[i]/6 | |
} | |
R[i,i] <- (h[i-1] + h[i])/3 | |
K <- Q %*% solve(R) %*% t(Q) | |
# Gibbs sampling for beta and lambda (Ruppert, Wand & Carroll, 2003) | |
betaGibbs <- function(y, var_noise, giPrecMx, B) { | |
stdNorm <- rnorm(ncol(B)) | |
giChol <- Cholesky(giPrecMx) | |
beta.mu <- as.vector(solve(giChol, crossprod(B, y))) | |
tau <- 1/var_noise | |
blChol <- chol(giPrecMx * tau) | |
return(as.vector(beta.mu + solve(blChol, stdNorm))) | |
} | |
varNoiseGibbs <- function(A, n, B, ssd) { | |
Aprime <- A + n/2 | |
Bprime <- 1/B + ssd/2 | |
return(1/rgamma(1, Aprime, Bprime)) | |
} | |
A_e <- B_e <- A_b <- B_b <- 0.1 # inverse gamma priors for variance | |
n_iter <- 2000 | |
samp_SdB <- samp_SdE <- samp_L <- numeric(length=n_iter) | |
samp_Beta <- matrix(nrow=n_iter, ncol=NK) | |
var_noise <- 1/rgamma(1,A_e,1/B_e) | |
var_beta <- 1/rgamma(1,A_b,1/B_b) | |
for (it in 1:n_iter) { | |
lambda <- var_noise/var_beta | |
# giPrecMx <- Matrix(diag(NK) + lambda*K, sparse=TRUE) | |
giPrecMx <- bTb + length(y)*lambda*g0 | |
samp_Beta[it,] <- beta <- betaGibbs(y, var_noise, giPrecMx, B) | |
ssd_beta <- crossprod(y - B %*% beta) | |
var_noise <- varNoiseGibbs(A_e, length(y), B_e, ssd_beta) | |
samp_SdE[it] <- sqrt(var_noise) | |
var_beta <- varNoiseGibbs(A_b, NK, B_b, crossprod(beta)) | |
samp_SdB[it] <- sqrt(var_beta) | |
samp_L[it] <- var_noise/var_beta | |
} | |
library(coda) | |
samp_coda <- mcmc(cbind(samp_SdE,samp_SdB,samp_L)) | |
varnames(samp_coda) <- c("sigma[epsilon]","sigma[beta]","lambda") | |
plot(samp_coda) | |
nburn <- n_iter/2 | |
samp_coda <- mcmc(cbind(samp_SdE,samp_SdB,samp_L,samp_Beta)) | |
varnames(samp_coda) <- c("sigma[epsilon]","sigma[beta]","lambda", paste0("beta[",1:NK,"]")) | |
samp <- window(samp_coda, start=nburn+1) | |
effectiveSize(samp) | |
summary(samp) | |
samp_idx <- nburn + sample(1:(n_iter-nburn), 20) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
bpred <- predict(B, x.star) | |
for (idx in samp_idx) { | |
lines(x.star, bpred %*% samp_Beta[idx,], col=2, lty=3) | |
} | |
legend("bottomright",legend=c("observations","true function","posterior samples"), | |
lty=c(NA,1,3),col=c(1,4,2),pch=c(1,NA,NA), lwd=c(1,1,2)) | |
title(main=paste(length(samp_idx), "posterior samples for Bayesian P-spline")) | |
pen <- 1e-2 | |
giPrecMx <- Matrix(diag(NK) + pen*K, sparse=TRUE) | |
giChol <- Cholesky(giPrecMx) | |
beta.mu <- as.vector(solve(giChol, crossprod(B, y))) | |
plot(x,y, ylim=range(-3,2,y)) | |
curve(2 - 5*exp(-8 * x^2), from=-0.5, to=1, n=length(x), col=4, add=T) | |
bpred <- predict(B, x.star) | |
lines(x.star, bpred %*% beta.mu, col=2) |
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