Fitting splines with penalized least squares.
Estimates the control vector for a spline fit by penalized least squares. The penalty being the penalty parameter times the functional inner product of the second derivative of the spline curve.
fitLS(object, x, y, penalty = 0)
The SplineBasis object ot be used to make the fit
The penalty multiplier.
For numeric vector y, and x, and a set of basis functions, represented in
object, defined on the knots (k_0,…,k_m).
The likelihood is defined by
sum_i (y_i-b(x_i)mu) + integral mu^T b''(t)^T b''(t) mu dt
The fucntion estimates μ.
a vector of the control points.
Andrew Redd <aredd at stat.tamu.edu>
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