| centeredBasis.gen {ordgam} | R Documentation |
Generation of a recentered B-spline basis matrix in additive models
Description
Generation of a 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, verbose = TRUE)
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) |
verbose |
verbose indicator (Default: TRUE) |
Value
List containing
B: centered 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. and Gressani, 0. (2023) Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling. <doi:10.1177/1471082X231181173>. Preprint: <arXiv:2210.01668>.
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)