This function creates a smooth B-spline basis and provides tools to find the coefficients of functional data in the basis and to recover functional data from basis coefficients.

1 | ```
create.basis(x, p, sp = 1e-09, degree = 3, nderiv = 2)
``` |

`x` |
vector of observation times |

`p` |
number of basis functions |

`sp` |
smoothing parameter |

`degree` |
degree of the B splines |

`nderiv` |
order of the derivative to penalize for smoothing |

The knots of the B-spline basis are taken as regular quantiles of `x`

. The function output is intended for use with functions
`coef2fd`

and `fd2coef`

.

A list with fields

`B` |
matrix of B-splines evaluated at |

`S` |
matrix that maps functional data
to their (smoothed) coefficients of their projection
in the basis set. For the purpose of PCA, the
coefficients are premultiplied by |

`invsqrtM` |
matrix |

`coef2fd`

, `fd2coef`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
n <- 100 # number of curves
d <- 500 # number of observation points
grid <- (1:d)/d # observation points
p <- 50 # number of B-spline basis functions
# Simulate Brownian motion
x <- matrix(rnorm(n*d,sd=1/sqrt(d)),n,d)
x <- t(apply(x,1,cumsum))
# Create B-spline basis
mybasis <- create.basis(grid, p, 1e-4)
# Compute smooth basis coefficients
beta <- fd2coef(x, mybasis)
# Recover smooth functional data
x.smooth <- coef2fd(beta, mybasis)
# Standard PCA and Functional PCA
pca <- prcomp(x)
fpca <- prcomp(beta)
``` |

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