This function performs the Smooth-Rough Partition linear regression with the input matrix.
srp.c(x, y, maxq = max(30, ceiling(0.1 * dim(x)[1])), L = 35,
norder = 4, inisp = 1, plot = T)
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.
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
.
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])).
An integer specifying the dimension of b-spline expansion for the constrained (smoothed) parameters. The default is 35.
An integer specifying the order of b-splines. The default of 4 performs cubic splines.
An initial value for optimising the tuning parameters and the default is 1.
If true, it gives the plot of estimated regression coefficients.
The estimator of constant parameter.
The vector of evaluated constrained functional regression coefficient.
The vector of unconstrained regression coefficient estimators.
The vector containing both bhat
and ahat
with unevaluated form.
The vector of estimated response variable.
The vector of Schwarz criterion with length maxq
which is computed for the different number of unconstrained parameters.
The optimal number of unconstrained parameters selected in the model.
The vector of two tuning parameters estimated by minimising generalised cross validation (GCV).
The number of bases used for constrained regression parameters.
The order of b-splines specified.
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.
# NOT RUN {
x <- matrix(rnorm(10000), ncol=100)
srp.c(x)
# }
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