| 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 three-term 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)