Compute the functional PCA from a set of curves.

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`x` |
The set of curves. |

`nbasisInit` |
The number of initial spline coefficients. |

`propVar` |
The proportion of explained variance. |

`reconstruct` |
Should the data be reconstruct after dimension reduction ? |

`varName` |
The name of the current functional variable. |

`verbose` |
Should the details be printed. |

The Functional PCA is performed in two steps. First we express each discretized curves as a linear combination of ‘nbasisInit’ spline basis functions. Then a multivariate PCA is computed on the spline coefficients. The final number of principal components is chosen such that the proportion of explained variance is larger than ‘propVar’.

A list with two components:

`design` |
The matrix of the principal components ; |

`smoothData` |
The reconstructed data if ‘reconstruct == TRUE’. |

Baptiste Gregorutti

Ramsay, J. O., and Silverman, B. W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

`hardThresholding`

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