fitBspline: Compute B-Spline Basis for Data
In plmDE: Additive partially linear models for differential gene expression analysis

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 `indicators` and `continuousCovariates` together with the length of `dataValues` determine the available number of degrees of freedom for the fitting of the spline.
`group`	String denoting the subgroup from which the measurements of these `dataValues` are coming.
`covariate`	String denoting the variable that is being measured by the `dataValues`.

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.

data = runif(n = 30, min = 50, max = 100)
variables = c("height", "weight", "insulin")
indicators = c("sex", "diseased")
subgroup = "diabetes"
covariate = "height"
BsplineFit = fitBspline(data, variables, indicators, subgroup, covariate)