Generates the convex regression spline (called C-spline) basis matrix by integrating I-spline basis for a polynomial spline or the corresponding derivatives.

  df = NULL,
  knots = NULL,
  degree = 3L,
  intercept = TRUE,
  Boundary.knots = NULL,
  derivs = 0L,
  scale = TRUE,



The predictor variable. Missing values are allowed and will be returned as they are.


Degree of freedom that equals to the column number of returned matrix. One can specify df rather than knots, then the function chooses df - degree - as.integer(intercept) internal knots at suitable quantiles of x ignoring missing values and those x outside of the boundary. If internal knots are specified via knots, the specified df will be ignored.


The internal breakpoints that define the spline. The default is NULL, which results in a basis for ordinary polynomial regression. Typical values are the mean or median for one knot, quantiles for more knots.


The degree of C-spline defined to be the degree of the associated M-spline instead of actual polynomial degree. For example, C-spline basis of degree 2 is defined as the scaled double integral of associated M-spline basis of degree 2.


If TRUE by default, all spline bases are included. Notice that when using C-Spline for shape-restricted regression, intercept = TRUE should be set even when an intercept term is considered additional to the spline bases in the model.


Boundary points at which to anchor the spline basis. By default, they are the range of the non-NA data. If both knots and Boundary.knots are supplied, the basis parameters do not depend on x. Data can extend beyond Boundary.knots.


A non-negative integer specifying the order of derivatives of C-splines. The default value is 0L for C-spline bases.


Logical value (TRUE by default) indicating whether scaling on C-spline basis is required. If TRUE, C-spline basis is scaled to have unit height at right boundary knot; the corresponding I-spline and M-spline basis matrices shipped in attributes are also scaled to the same extent.


Optional arguments that are not used.


A numeric matrix with length(x) rows and df columns if df is specified or length(knots) + degree + as.integer(intercept) columns if knots are specified instead. Attributes that correspond to the arguments specified are returned for usage of other functions in this package.


It is an implementation of the close form C-spline basis derived from the recursion formula of I-splines and M-splines.


Meyer, M. C. (2008). Inference using shape-restricted regression splines. The Annals of Applied Statistics, 1013--1033. Chicago

See also

iSpline for I-splines; mSpline for M-splines.


library(splines2) x <-, 1, 0.01) knots <- c(0.3, 0.5, 0.6) ### when 'scale = TRUE' (by default) csMat <- cSpline(x, knots = knots, degree = 2) par(mar = c(2.5, 2.5, 0.2, 0.1), mgp = c(1.5, 0.5, 0)) matplot(x, csMat, type = "l", ylab = "C-spline basis")
abline(v = knots, lty = 2, col = "gray")
isMat <- deriv(csMat) msMat <- deriv(csMat, derivs = 2) matplot(x, isMat, type = "l", ylab = "scaled I-spline basis")
matplot(x, msMat, type = "l", ylab = "scaled M-spline basis")
### when 'scale = FALSE' csMat <- cSpline(x, knots = knots, degree = 2, scale = FALSE) ## the corresponding I-splines and M-splines (with same arguments) isMat <- iSpline(x, knots = knots, degree = 2) msMat <- mSpline(x, knots = knots, degree = 2, intercept = TRUE) ## or using deriv methods (more efficient) isMat1 <- deriv(csMat) msMat1 <- deriv(csMat, derivs = 2) ## equivalent stopifnot(all.equal(isMat, isMat1, check.attributes = FALSE)) stopifnot(all.equal(msMat, msMat1, check.attributes = FALSE))