46
RSS.min <- function(X, y, T) {
m <- ncol(T)
S.min <- Inf
for (j in 1:m) {
q <- T[, j]
S <- sum((lm(y ~ X[, q])$fitted.values - y) ^ 2) / n
if (S < S.min) {
S.min <- S
set.q <- q
}
}
return(list(value = S.min, set = set.q))
}
library(MASS)
df <- Boston
X <- as.matrix(df[, c(1, 3, 5, 6, 7, 8, 10, 11, 12, 13)])
y <- df[[14]]
p <- ncol(X)
n <- length(y)
AIC.min <- Inf
for (k in 1:p) {
T <- combn(1:p, k)
res <- RSS.min(X, y, T)
AIC <- n * log(res$value / n) + 2 * k
if (AIC < AIC.min) {
AIC.min <- AIC
set.min <- res$set
}
}
AIC.min
## [1] -1530.84
set.min
## [1] 1 3 4 6 8 9 10
47
df <- Boston
X <- as.matrix(df[, c(1, 3, 5, 6, 7, 8, 10, 11, 12, 13)])
y <- df[[14]]
p <- ncol(X)
n <- length(y)
BIC.min <- Inf
for (k in 1:p) {
T <- combn(1:p, k)
res <- RSS.min(X, y, T)
BIC <- n * log(res$value / n) + k * log(n)
if (BIC < BIC.min) {
BIC.min <- AIC
set.min <- res$set
}
}
BIC.min
## [1] -1526.553
set.min
## [1] 10
AR2 <- function(X,y){
p <- ncol(X)
n <- length(y)
y.bar <- mean(y)
TSS <- sum((y - y.bar) ^ 2)
D.max <- -Inf
for (k in 1:p) {
T <- combn(1:p, k)
res <- RSS.min(X, y, T)
D <- 1 - res$value / (n - k - 1) / TSS * (n - 1)
if (D > D.max) {
D.max <- D;
set.max <- res$set
}
}
print(D.max)
set.max
}
AR2(X,y)
## [1] 0.9994328
## [1] 1 3 4 6 8 9 10
48
library(MASS)
df <- Boston
X <- as.matrix(df[, c(1, 3, 5, 6, 7, 8, 10, 11, 12, 13)])
y <- df[[14]]
n <- nrow(X)
p <- ncol(X)
IC <- function(k) {
T <- combn(1:p, k)
res <- RSS.min(X, y, T)
AIC <- n * log(res$value / n) + 2 * k
BIC <- n * log(res$value / n) + k * log(n)
return(list(AIC = AIC, BIC = BIC))
}
AIC.seq <- NULL
BIC.seq <- NULL
for (k in 1:p) {
AIC.seq <- c(AIC.seq, IC(k)$AIC)
BIC.seq <- c(BIC.seq, IC(k)$BIC)
}
plot(1:p, ylim = c(min(AIC.seq), max(BIC.seq)), type = "n",
xlab = "Number of Variables", ylab = "AIC/BIC Value")
lines(AIC.seq, col = "red")
lines(BIC.seq, col = "blue")
legend("topleft", legend = c("AIC", "BIC"), col = c("red", "blue"),
lwd = 1, cex = .8)