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

 Orthogonal matching pursuit variable selection R Documentation

## Orthogonal matching variable selection

### Description

Orthogonal matching variable selection.

### Usage

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 \chi^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 \gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(2): 1214-1224. https://arxiv.org/pdf/2004.00281.pdf

 mmpc2, pc.sel 

### Examples

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


Rfast2 documentation built on May 29, 2024, 8:45 a.m.