| centeredBasis.gen {DALSM} | R Documentation |
Generation of a recentered cubic B-spline basis matrix in additive models
Description
Generation of a cubic B-spline basis matrix with recentered columns to handle the identifiability constraint in additive models. See Wood (CRC Press 2017, pp. 175-176) for more details.
Usage
centeredBasis.gen(x, knots, cm = NULL, pen.order = 2)
Arguments
x |
vector of values where to compute the "recentered" B-spline basis. |
knots |
vector of knots (that should cover the values in <x>). |
cm |
(Optional) values subtracted from each column of the original B-spline matrix. |
pen.order |
penalty order for the B-spline parameters (Default: 2). |
Value
List containing
B: centered cubic B-spline matrix (with columns recentered to have mean 0 over equi-spaced x values on the range of the knots).Dd: difference matrix (of order <pen.order>) for the associated centered B-spline matrix.Pd: penalty matrix (of order <pen.order>) for the associated centered B-spline matrix.K: number of centered B-splines in the basis.cm: values subtracted from each column of the original B-spline matrix. By default, this is a vector containing the mean of each column in the original B-spline matrix.
Author(s)
Philippe Lambert p.lambert@uliege.be
References
Lambert, P. (2021). Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data. Computational Statistics and Data Analysis, 161: 107250. <doi:10.1016/j.csda.2021.107250>
Examples
x = seq(0,1,by=.01)
knots = seq(0,1,length=5)
obj = centeredBasis.gen(x,knots)
matplot(x,obj$B,type="l",ylab="Centered B-splines")
colMeans(obj$B)