srp.l: The (simpler) Smooth-Rough Partition linear regression model...
In srp: Smooth-Rough Partitioning of the Regression Coefficients

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

This function performs same as srp.c except that constrained functional coefficient is estimated as a linear function.

1	srp.l(x, y, maxq = max(30, ceiling(0.1 * dim(x)[1])), plot = T)

`x`	A matrix you wish to fit Smooth-Rough Partition model. The dimension of row is the number of variables which are pre-ordered in terms of their importance in prediction.
`y`	A vector you wish to use as a response variable in case of regressing `y` on `x`. If `y` is missing, the response variable is obtained from the last row of `x`.
`maxq`	An integer specifying the maximum number of unconstrained parameters which the model can have. The default is max(30, ceiling(0.1*dim(x)[1])).
`plot`	If true, it gives the plot of estimated regression coefficients.

The estimation procedure of Smooth-Rough Partition model is described in "Regularised forecasting via smooth-rough partitioning of the regression coefficients", H. Maeng and P. Fryzlewicz (2018), preprint.

`muhat`	The estimator of constant parameter.
`bhat`	The vector of evaluated constrained (linear) functional regression coefficient.
`ahat`	The vector of unconstrained regression coefficient estimators.
`etahat`	The vector containing both `bhat` and `ahat` with unevaluated form.
`yhat`	The vector of estimated response variable.
`SIC`	The vector of Schwarz criterion with length `maxq` which is computed for the different number of unconstrained parameters.
`qhat`	The optimal number of unconstrained parameters selected in the model.