This function performs same as srp.c
except that constrained functional coefficient is estimated as a linear function.
srp.l(x, y, maxq = max(30, ceiling(0.1 * dim(x)[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])).
If true, it gives the plot of estimated regression coefficients.
The estimator of constant parameter.
The vector of evaluated constrained (linear) 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 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.l(x)
# }
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