# 1. for loop int <- 0 for(i in 1:10){ int <- int + i } b <- 1:10 s <- array(0,c(1,10)) s[1] <- b[1] for(i in 2:10){ s[i] <- s[i-1] + b[i] } cumsum(b) rho = 0.7 aa <- NULL sa <- NULL for(i in 0:20){ aa <- c(aa,rho^(i)) sa <- c(sa,sum(aa)) } plot(sa,ylim=c(-0.2,3.5),pch="+") points(aa,pch="*",col="red") # 2. Fitting a dynamic model library(dyn) d.gas<-read.table("AUTO2.txt",header=T) attach(d.gas) gas<-ts(gas,start=1959,frequency=4) price<-ts(price,start=1959,frequency=4) income<-ts(income,start=1959,frequency=4) miles<-ts(miles,start=1959,frequency=4) par(mfrow=c(2,1)) plot(gas) plot(price) acf(gas) acf(price) f1 <- dyn$lm(gas~lag(gas,-1)+price) summary(f1) fitted(f1) f2 <- dyn$lm(gas~lag(gas,-1) + price + diff(gas)) summary(f2) f3 <- dyn$lm(gas~lag(gas,-1) + price + diff(gas) + lag(diff(gas),-1)) summary(f3) # 3. AIC & BIC n <- length(fitted(f3)) AIC(f3,k=log(n)) AIC(f2,k=log(length(fitted(f2)))) AIC(f1,k=log(length(fitted(f1)))) m1 <- gas ~ lag(gas,-1) m2 <- gas ~ lag(gas,-1) + price m3 <- gas ~ lag(gas,-1) + price + diff(gas) m4 <- gas ~ lag(gas,-1) + price + diff(gas) + lag(diff(gas),-1) M <- c(m1,m2,m3,m4) A <- array(0,c(4,2)) for(i in 1:4){ g <- dyn$lm(M[[i]]) A[i,1] <- AIC(g,k=2) A[i,2] <- AIC(g,k=log(length(fitted(g)))) }