polyn.eval {sfsmisc} | R Documentation |
Evaluate one or several univariate polynomials at several locations,
i.e. compute coef[1] + coef[2]*x + ... + coef[p+1]* x^p
(in the simplest case where x
is scalar and coef
a vector).
polyn.eval(coef, x)
coef |
numeric vector or matrix. If a vector, x can be an
array and the result matches x .If coef is a matrix it specifies several polynomials of the
same degree as rows, x must be a vector, coef[,k] is
for x^(k-1) and the result
is a matrix of dimension length(x) * nrow(coef) . |
x |
numeric vector or array. Either x or coef must
be a vector. |
The stable “Horner rule” is used for evaluation in any case.
numeric vector or array, depending on input dimensionalities, see above.
Martin Maechler, ages ago.
For much more sophisticated handling of polynomials, use the
polynom
package, e.g. predict.polynomial
.
polyn.eval(c(1,-2,1), x = 0:3)# (x - 1)^2 polyn.eval(c(0, 24, -50, 35, -10, 1), x = matrix(0:5, 2,3))# 5 zeros! (cf <- rbind(diag(3), c(1,-2,1))) polyn.eval(cf, 0:5)