# omp2: Orthogonal matching variable selection In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

## Description

Orthogonal matching variable selection.

## Usage

 `1` ```omp2(y, x, xstand = TRUE, tol = qchisq(0.95, 1), type = "gamma" ) ```

## Arguments

 `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 χ^2 distribution. `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".

## Details

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++.

## Value

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.

## Author(s)

Michail Tsagris

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

## References

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

## See Also

``` mmpc2, pc.sel ```

## Examples

 ```1 2 3 4 5``` ```x <- matrix( rnorm(100 * 50), ncol = 50 ) y <- rgamma(100, 4, 1) a <- omp2(y, x) a x <- NULL ```

Rfast2 documentation built on March 22, 2021, 9:08 a.m.