Description Details Author(s) References

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.

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:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
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`

.

Mikko Korpela [email protected]

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

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