# Computation of Design Matrix induced by Multi-scale SBF's for Ridge Regression

### Description

This function computes design matrix induced by multi-scale SBF's for ridge regression.

### Usage

1 | ```
gg.comp(site1, site2, ssite1, ssite2, snet, seta, lam)
``` |

### Arguments

`site1` |
latitudes of observation sites in radian |

`site2` |
longitudes of observation sites in radian |

`ssite1` |
latitudes of observation sites in radian used in least squares method |

`ssite2` |
longitudes of observation sites in radian used in least squares method |

`snet` |
vector of labels representing sub-networks |

`seta` |
bandwidth parameters for Poisson kernel |

`lam` |
smoothing parameter for ridge regression |

### Value

`gg` |
design matrix induced by multi-scale SBF's for ridge regression. |

### References

Oh, H-S. (1999) Spherical wavelets and their statistical analysis with applications to meteorological data. Ph.D. Thesis, Department of Statistics, Texas A\&M University, College Station.

Li, T-H. (1999) Multiscale representation and analysis of spherical data by spherical wavelets.
*SIAM Journal on Scientific Computing*, **21**, 924–953.

### See Also

`lscoef.comp`

, `gg.comp`

, `ridge.comp`

.

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