poly {stats}  R Documentation 
Returns or evaluates orthogonal polynomials of degree 1 to
degree
over the specified set of points x
. These are all
orthogonal to the constant polynomial of degree 0. Alternatively,
evaluate raw polynomials.
poly(x, ..., degree = 1, coefs = NULL, raw = FALSE) polym(..., degree = 1, raw = FALSE) ## S3 method for class 'poly': predict(object, newdata, ...)
x, newdata 
a numeric vector at which to evaluate the
polynomial. x can also be a matrix. Missing values are not
allowed in x . 
degree 
the degree of the polynomial 
coefs 
for prediction, coefficients from a previous fit. 
raw 
if true, use raw and not orthogonal polynomials. 
object 
an object inheriting from class "poly" , normally
the result of a call to poly with a single vector argument. 
... 
poly, polym : further vectors.predict.poly : arguments to be passed to or from other methods.

Although formally degree
should be named (as it follows
...
), an unnamed second argument of length 1 will be
interpreted as the degree.
The orthogonal polynomial is summarized by the coefficients, which can be used to evaluate it via the threeterm recursion given in Kennedy & Gentle (1980, pp. 343–4), and used in the “predict” part of the code.
For poly
with a single vector argument:
A matrix with rows corresponding to points in x
and columns
corresponding to the degree, with attributes "degree"
specifying
the degrees of the columns and (unless raw = TRUE
)
"coefs"
which contains the centering and normalization
constants used in constructing the orthogonal polynomials. The matrix
has given class c("poly", "matrix")
.
Other cases of poly
and polym
, and predict.poly
:
a matrix.
This routine is intended for statistical purposes such as
contr.poly
: it does not attempt to orthogonalize to
machine accuracy.
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
Kennedy, W. J. Jr and Gentle, J. E. (1980) Statistical Computing Marcel Dekker.
cars
for an example of polynomial regression.
(z < poly(1:10, 3)) predict(z, seq(2, 4, 0.5)) poly(seq(4, 6, 0.5), 3, coefs = attr(z, "coefs")) polym(1:4, c(1, 4:6), degree=3) # or just poly() poly(cbind(1:4, c(1, 4:6)), degree=3)