srp.c: The Smooth-Rough Partition model fitting

In srp: Smooth-Rough Partitioning of the Regression Coefficients

Description

This function performs the Smooth-Rough Partition linear regression with the input matrix.

Usage

srp.c(x, y, maxq = max(30, ceiling(0.1 * dim(x)[1])), L = 35,
  norder = 4, inisp = 1, plot = T)

Arguments

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])).
L	An integer specifying the dimension of b-spline expansion for the constrained (smoothed) parameters. The default is 35.
norder	An integer specifying the order of b-splines. The default of 4 performs cubic splines.
inisp	An initial value for optimising the tuning parameters and the default is 1.
plot	If true, it gives the plot of estimated regression coefficients.

Details

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.

Value

muhat	The estimator of constant parameter.
bhat	The vector of evaluated constrained 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.
sp	The vector of two tuning parameters estimated by minimising generalised cross validation (GCV).
L	The number of bases used for constrained regression parameters.
norder	The order of b-splines specified.

Author(s)

Hyeyoung Maeng, [e-mail]

See Also

sic.c, predict.srp.c, srp.l

Examples

x <- matrix(rnorm(10000), ncol=100)
srp.c(x)

srp documentation built on May 2, 2019, 9:31 a.m.