sisal-package: sisal: Sequential input selection algorithm

Description Details Author(s) References

Description

Implements the SISAL algorithm by Tikka and Hollmén. It is a sequential backward selection algorithm which uses a linear model in a cross-validation setting. Starting from the full model, one variable at a time is removed based on the regression coefficients. From this set of models, a parsimonious (sparse) model is found by choosing the model with the smallest number of variables among those models where the validation error is smaller than a threshold. Also implements extensions which explore larger parts of the search space and/or use ridge regression instead of ordinary least squares.

Details

Package: sisal
Depends: R (>= 3.1.2)
Imports: graphics, grDevices, grid, methods, stats, utils,
boot, lattice, mgcv, digest, R.matlab, R.methodsS3
Suggests: graph, Rgraphviz, testthat (>= 0.8)
License: GPL (>= 2)
LazyData: yes

Index:

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bootMSE                 Bootstrap Estimate of Mean Squared Error Using
                        SISAL Object
dynTextGrob             Create Text with Changing Size
laggedData              Create Input Matrix and Output Vector for Time
                        Series Prediction
plot.sisal              Plotting Sequential Input Selection Results
plotSelected.sisal      Plotting Sets of Inputs Produced by Sequential
                        Input Selection
print.sisal             Printing Sequential Input Selection Objects
sisal                   Sequential Input Selection Algorithm (SISAL)
sisal-package           sisal: Sequential input selection algorithm in
                        R
sisalData               Download External Datasets for SISAL
sisalTable              Draw Table with Equally Sized Cells
summary.sisal           Summarizing Sequential Input Selection Results
testSisal               Testing the Sequential Input Selection
                        Algorithm
toy.learn               Toy Data for SISAL (Learning Set)
toy.test                Toy Data for SISAL (Test Set)
tsToy.learn             Toy Time Series Data for SISAL (Learning Set)
tsToy.test              Toy Time Series Data for SISAL (Test Set)

Run input selection on your own data with sisal. For demo purposes, use testSisal to run the algorithm on example data sets. After input selection, compute bootstrap MSE in test data with bootMSE.

Author(s)

Mikko Korpela [email protected]

References

Tikka, J. and Hollmén, J. (2008) Sequential input selection algorithm for long-term prediction of time series. Neurocomputing, 71(13–15):2604–2615.


sisal documentation built on May 29, 2017, 9:09 a.m.