The Art of R Programming

59 # the 1 accommodates the constant term
60 predy[i] <- betahat %% c(1,xmat[i,])
61 }
62 return(predy)
63 }
64
65 # polynomial function of x, coefficients cfs
66 poly <- function(x,cfs) {
67 val <- cfs[1]
68 prod <- 1
69 dg <- length(cfs) - 1
70 for (i in 1:dg) {
71 prod <- prodx
72 val <- val + cfs[i+1]*prod
73 }
74 }

As you can see,"polyreg"consists ofpolyfit(), the constructor function,

andprint.polyreg(), a print function tailored to this class. It also contains

several utility functions to evaluate powers and polynomials and to perform

cross-validation. (Note that in some cases here, efficiency has been sacrificed

for clarity.)

As an example of using the class, we’ll generate some artificial data and

create an object of class"polyreg"from it, printing out the results.

>n<-60

> x <- (1:n)/n

> y <- vector(length=n)

> for (i in 1:n) y[i] <- sin((3*pi/2)*x[i]) + x[i]^2 + rnorm(1,mean=0,sd=0.5)

>dg<-15

> (lmo <- polyfit(y,x,dg))

mean squared prediction errors, by degree

MSPE

[1,] 0.4200127

[2,] 0.3212241

[3,] 0.2977433

[4,] 0.2998716

[5,] 0.3102032

[6,] 0.3247325

[7,] 0.3120066

[8,] 0.3246087

[9,] 0.3463628

[10,] 0.4502341

[11,] 0.6089814

[12,] 0.4499055

[13,] NA

[14,] NA

[15,] NA

Object-Oriented Programming 221