Tool to build the basis matrix and the penalty matrix of natural cubic splines.
ncs builds the basis matrix and the penalty matrix to approximate a smooth function using a natural cubic spline.
ncs(xx, lambda, nknots, all.knots)
xx |
the explanatory variable. |
lambda |
an optional positive value that represents the smoothing parameter value. |
nknots |
an optional positive integer that represents the number of knots of the natural cubic spline. Default is m=[n^{\frac{1}{3}}]+3. The knots are located at the quantiles of order 0/(m-1),1/(m-1),…,(m-1)/(m-1) of xx. |
all.knots |
logical. If |
xx |
the explanatory variable xx with the following attributes: set of knots, basis matrix, penalty matrix, smoothing parameter (if it was specified), and other interest matrices. |
Luis Hernando Vanegas <hvanegasp@gmail.com> and Gilberto A. Paula
Lancaster, P. and Salkauskas, K. (1986) Curve and Surface Fitting: an introduction. Academic Press, London. Green, P.J. and Silverman, B.W. (1994) Nonparametric Regression and Generalized Linear Models, Boca Raton: Chapman and Hall.
n <- 300 t <- sort(round(runif(n),digits=1)) t2 <- ncs(t,all.knots=TRUE) N <- attr(t2, "N") ## Basis Matrix M <- attr(t2, "K") ## Penalty Matrix knots <- attr(t2, "knots") ## Set of knots
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