# cSplineDes: Evaluate cyclic B spline basis In mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

 cSplineDes R Documentation

## Evaluate cyclic B spline basis

### Description

Uses `splineDesign` to set up the model matrix for a cyclic B-spline basis.

### Usage

``````cSplineDes(x, knots, ord = 4, derivs=0)
``````

### Arguments

 `x` covariate values for smooth. `knots` The knot locations: the range of these must include all the data. `ord` order of the basis. 4 is a cubic spline basis. Must be >1. `derivs` order of derivative of the spline to evaluate, between 0 and `ord`-1. Recycled to length of `x`.

### Details

The routine is a wrapper that sets up a B-spline basis, where the basis functions wrap at the first and last knot locations.

### Value

A matrix with `length(x)` rows and `length(knots)-1` columns.

### Author(s)

Simon N. Wood simon.wood@r-project.org

`cyclic.p.spline`

### Examples

`````` require(mgcv)
## create some x's and knots...
n <- 200
x <- 0:(n-1)/(n-1);k<- 0:5/5
X <- cSplineDes(x,k) ## cyclic spline design matrix
## plot evaluated basis functions...
plot(x,X[,1],type="l"); for (i in 2:5) lines(x,X[,i],col=i)
## check that the ends match up....
ee <- X[1,]-X[n,];ee
tol <- .Machine\$double.eps^.75
if (all.equal(ee,ee*0,tolerance=tol)!=TRUE)
stop("cyclic spline ends don't match!")

## similar with uneven data spacing...
x <- sort(runif(n)) + 1 ## sorting just makes end checking easy
k <- seq(min(x),max(x),length=8) ## create knots
X <- cSplineDes(x,k) ## get cyclic spline model matrix
plot(x,X[,1],type="l"); for (i in 2:ncol(X)) lines(x,X[,i],col=i)
ee <- X[1,]-X[n,];ee ## do ends match??
tol <- .Machine\$double.eps^.75
if (all.equal(ee,ee*0,tolerance=tol)!=TRUE)
stop("cyclic spline ends don't match!")
``````

mgcv documentation built on July 26, 2023, 5:38 p.m.