Description Details Author(s) References

This package introduces a simple method to select the best model using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). The proposed method is a new forward stepwise-based selection procedure that selects a model containing a subset of variables according to an optimal criterion (obtained by cross-validation) and also takes into account the computational cost. Additionally, bootstrap resampling techniques are used to implement tests capable of detecting whether significant effects of the unselected variables are present in the model.

Package: | FWDselect |

Type: | Package |

Version: | 2.1.0 |

Date: | 2015-12-18 |

License: | MIT + file LICENSE |

`FWDselect`

is just a shortcut for “Forward selection” and is a very
good summary of one of the package's major functionalities, i.e., that of
providing a forward stepwise-based selection procedure. This software helps
the user select relevant variables and evaluate how many of these need to be
included in a regression model. In addition, it enables both numerical and
graphical outputs to be displayed. The package includes several functions
that enable users to select the variables to be included in linear,
generalized linear or generalized additive regression models. Users can
obtain the best combinations of `q`

variables by means of the main
function `selection`

. Additionally, if one wants to obtain the
results for more than one size of subset, it is possible to apply the
`qselection`

function, which returns a summary table showing the
different subsets, selected variables and information criterion values. The
object obtained when using this last function is the argument required for
`plot.qselection`

, which provides a graphical output. Finally, to
determine the number of variables that should be introduced into the model,
only the `test`

function needs to be applied.

Marta Sestelo, Nora M. Villanueva and Javier Roca-Pardinas.

Burnham, K., Anderson, D. (2002). Model selection and multimodel inference: a practical information-theoretic approach. 2nd Edition Springer.

Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7:1-26.

Efron, B. and Tibshirani, R. J. (1993). An introduction to the Bootstrap. Chapman and Hall, London.

Miller, A. (2002). Subset selection in regression. Champman and Hall.

Sestelo, M., Villanueva, N. M. and Roca-Pardinas, J. (2013). FWDselect: Variable selection algorithm in regression models. Discussion Papers in Statistics and Operation Research, University of Vigo, 13/02.

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