| Type: | Package |
| Title: | The 'epilogi' Variable Selection Algorithm for Continuous Data |
| Version: | 1.2 |
| Date: | 2024-12-20 |
| Author: | Michail Tsagris [aut, cre] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | Rfast, stats |
| Suggests: | Rfast2 |
| Description: | The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. <doi:10.1093/bioinformatics/btad545>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2024-12-20 15:18:24 UTC; mtsag |
| Repository: | CRAN |
| Date/Publication: | 2024-12-23 14:10:01 UTC |
The 'epilogi' Variable Selection Algorithm for Continuous Data.
Description
The '\epsilonpilogi' Variable Selection Algorithm for Continuous Data.
Details
| Package: | epilogi |
| Type: | Package |
| Version: | 1.2 |
| Date: | 2024-12-20 |
| License: | GPL-2 |
Maintainers
Michail Tsagris mtsagris@uoc.gr.
Author(s)
Michail Tsagris mtsagris@uoc.gr.
References
Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). Automated machine learning for Genome Wide Association Studies. Bioinformatics, 39(9): btad545.
Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). 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.
The epilogi Variable Selection Algorithm for Continuous Data.
Description
The \epsilonpilogi Variable Selection Algorithm for Continuous Data.
Usage
epilogi(y, x, tol = 0.01, alpha = 0.05, parallel = FALSE)
Arguments
y |
A vector with the continuous response variable. |
x |
A matrix with the continuous predictor variables. |
tol |
The tolerance value for the algortihm to terminate. This takes values greater than 0 and it refers to the change between two successive |
alpha |
The significance level to deem a predictor variable is statistically equivalent to a selected variable. |
parallel |
If set to TRUE, some of the computations take place in parallel (in C++). |
Details
The \epsilonpilogi variable selection algorithm (Lakiotaki et al., 2023) is a generalisation of the \gamma-OMP algorithm (Tsagris et al. 2022). It applies the aforementioned algorithm with the addition that it returns possible statistically equivalent predictor(s) for each selected predictor. Once a variable is selected the algorithm searches for possible equivalent predictors using the partial correlation between the residuals.
The heuristic method to consider two predictors R and C informationally equivalent given the current selected predictor S is determined as follows: first, the residuals r of the model using S are computed. Then, if the following two conditions hold R and C are considered equivalent: Ind(R; r | C) and Ind(r ; C | R), where Ind(R; r | C) denotes the conditional independence of R with r given C. When linearity is assumed, the test can be implemented by testing for significance the corresponding partial correlation. The tests Ind return a p-value and independence is accepted when it is larger than a threshold (significance value, argument alpha). Intuitively, R and C are heuristically considered equivalent, if C is known, then R provides no additional information for the residuals r, and if R is known, then C provides no additional information for r.
Value
A list including:
runtime |
The runtime of the algorithm. |
result |
A matrix with two columns. The selected predictor(s) and the adjusted |
equiv |
A list with the equivalent predictors (if any) corresponding to each selected predictor. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). Automated machine learning for Genome Wide Association Studies. Bioinformatics, 39(9): btad545
Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). 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.
See Also
Examples
#simulate a dataset with continuous data
set.seed(1234)
n <- 500
x <- matrix( rnorm(n * 50, 0, 30), ncol = 50 )
#define a simulated class variable
y <- 2 * x[, 1] - 1.5 * x[, 2] + x[, 3] + rnorm(n, 0, 15)
# define some simulated equivalences
x[, 4] <- x[, 1] + rnorm(n, 0, 1)
x[, 5] <- x[, 2] + rnorm(n, 0, 1)
epilogi(y, x, tol = 0.05)
Equivalence test using partial correlation
Description
Equivalence test using partial correlation.
Usage
pcor.equiv(res, y, x, alpha = 0.05)
Arguments
res |
A vector with the residuals of the linear model. |
y |
A vector with a selected predictor. |
x |
A matrix with other predictors. |
alpha |
The significance level to check for predictors from x that are equivalent to y. |
Value
A vector with 0s and 1s. 0s indicate that the predictors are not equivalent, while 1s indicate the equivalent predictors.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
#simulate a dataset with continuous data
set.seed(1234)
n <- 500
x <- matrix( rnorm(n * 50, 0, 30), ncol = 50 )
#define a simulated class variable
y <- 2 * x[, 1] - 1.5 * x[, 2] + x[, 3] + rnorm(n, 0, 15)
# define some simulated equivalences
x[, 4] <- x[, 1] + rnorm(n, 0, 1)
x[, 5] <- x[, 2] + rnorm(n, 0, 1)
b <- epilogi(y, x, tol = 0.05)
sel <- b$result[2, 1]
## standardise the y and x first
y <- (y - mean(y)) / Rfast::Var(y, std = TRUE)
x <- Rfast::standardise(x)
res <- resid( lm(y ~ x[, sel] ) )
sela <- b$result[2:3, 1]
pcor.equiv(res, x[, sela[2]], x[, -sela] )
## bear in mind that this gives the third variable after removing the first two,
## so this is essentially the 5th variable in the "x" matrix.