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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