Description Usage Arguments Details Value Author(s) References See Also Examples

Provides a heuristic framework for fitting a set of B-spline basis functions to a set of data points under the constraint of a limited number of degrees of freedom.

1 | ```
fitBspline(dataValues, continuousCovariates, indicators, group, covariate)
``` |

`dataValues` |
A vector of data points to which the spline is to be fit. |

`continuousCovariates` |
Vector that contains all the continuous variables whose coefficients must be estimated in this model. |

`indicators` |
This vector contains all the indicator variables whose coefficients will also be estimated in the model. The number of parameters in |

`group` |
String denoting the subgroup from which the measurements of these |

`covariate` |
String denoting the variable that is being measured by the |

This method is intended for users who do not wish to specify B-spline properties. It adopts the heuristic of modified equipotent arrangement put forth by Yanagihara and Ohtaki (1991) in selecting knot-placement of the splines.

Uses the return format of `bs`

in the `splines`

package, which is a matrix corresponding to the values of each fitted B-spline basis function evaluated at each point in `dataValues`

.

Jonas Mueller

Yanagihara, H. and Ohtaki, M. Knot-Placement to Avoid Over Fitting in *B*-Spline Scedastic Smoothing. *Communications in Statistics*, **32**, 771-85 (1991).

`splines`

for a basic B-spline package on which `fitBspline`

heavily relies.

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