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.
|Author||Mikko Korpela [aut, cre]|
|Date of publication||2015-10-10 15:45:32|
|Maintainer||Mikko Korpela <email@example.com>|
|License||GPL (>= 2)|
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...
plotSelected.sisal: Plotting Sets of Inputs Produced by Sequential Input...
plot.sisal: Plotting Sequential Input Selection Results
print.sisal: Printing Sequential Input Selection Objects
sisal: Sequential Input Selection Algorithm (SISAL)
sisalData: Download External Datasets for SISAL
sisal-package: sisal: Sequential input selection algorithm
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)