Orthogonal matching variable selection
Orthogonal matching variable selection.
omp2(y, x, xstand = TRUE, tol = qchisq(0.95, 1), type = "gamma" )
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". |
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
x <- matrix( rnorm(100 * 50), ncol = 50 ) y <- rgamma(100, 4, 1) a <- omp2(y, x) a x <- NULL
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