Description Usage Arguments Details Value Author(s) References See Also Examples

Orthogonal matching variable selection.

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`y` |
The response variable, a numeric vector. For "omp" this can be either a vector with discrete (count) data, 0 and 1, non negative values, strictly positive or a factor (categorical) variable. |

`x` |
A matrix with the data, where the rows denote the observations and the columns are the variables. |

`xstand` |
If this is TRUE the independent variables are standardised. |

`tol` |
The tolerance value to terminate the algorithm. This is the change in the criterion value
between two successive steps. For "ompr" the default value is 2 because the default method
is "BIC". The default value is the 95% quantile of the |

`type` |
This denotes the parametric model to be used each time. It depends upon the nature of y. The possible values are "gamma", "negbin", or "multinomial". |

This is the continuation of the "omp" function of the Rfast. We added some more regression models. The "gamma" and the "multinomial" models have now been implemented in C++.

A list including:

`runtime` |
The runtime of the algorithm. |

`info` |
A matrix with two columns. The selected variable(s) and the criterion value at every step. |

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr>.

Pati Y. C., Rezaiifar R. and Krishnaprasad P. S. (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Signals, Systems and Computers. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on. IEEE.

Mazin Abdulrasool Hameed (2012). Comparative analysis of orthogonal matching pursuit and least angle regression. MSc thesis, Michigan State University. https://www.google.gr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwik9P3Yto7XAhUiCZoKHQ8XDr8QFgglMAA&url=https

Lozano A., Swirszcz G. and Abe N. (2011). Group orthogonal matching pursuit for logistic regression. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics.

The *γ*-OMP algorithm for feature selection with application to gene expression data.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (Accepted for publication)
https://arxiv.org/pdf/2004.00281.pdf

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