| Type: | Package |
| Title: | A Fast Algorithm for Sparse Precision Matrix Estimation |
| Version: | 0.1.1 |
| Maintainer: | Juan G. Colonna <juancolonna@icomp.ufam.edu.br> |
| Description: | It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) <doi:10.52933/jdssv.v1i2.11>. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/juancolonna/FastStepGraph |
| Depends: | R (≥ 4.3), |
| Imports: | doParallel (≥ 1.0), foreach (≥ 1.5), MASS (≥ 7.3) |
| Suggests: | knitr, rmarkdown, devtools |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-10-12 15:38:30 UTC; juan |
| Author: | Juan G. Colonna 
[cre, aut],
Marcelo Ruiz [aut] |
| Repository: | CRAN |
| Date/Publication: | 2023-10-12 16:10:02 UTC |
Fast Stepwise Gaussian Graphical Model
Description
Improved and faster implementation of the Stepwise Gaussian Graphical Algorithm.
Usage
FastStepGraph(
x,
alpha_f,
alpha_b = NULL,
nei.max = 5,
data_scale = FALSE,
max.iterations = NULL
)
Arguments
x |
Data matrix (of size n_samples x p_variables). |
alpha_f |
Forward threshold (no default value). |
alpha_b |
Backward threshold. If alpha_b=NULL, then the rule alpha_b <- 0.5*alpha_f is applied. |
nei.max |
Maximum number of variables in every neighborhood (default value 5). |
data_scale |
Boolean parameter (TRUE or FALSE), when to scale data to zero mean and unit variance (default FALSE). |
max.iterations |
Maximum number of iterations (integer), the defaults values is set to p*(p-1). |
Value
A list with the values:
vareps |
Response variables. |
beta |
Regression coefficients. |
Edges |
Estimated set of edges. |
Omega |
Estimated precision matrix. |
Author(s)
Prof. Juan G. Colonna, PhD. juancolonna@icomp.ufam.edu.br
Prof. Marcelo Ruiz, PhD. mruiz@exa.unrc.edu.ar
Examples
data <- FastStepGraph::SigmaAR(30, 50, 0.4) # Simulate Gaussian Data
G <- FastStepGraph::FastStepGraph(data$X, alpha_f = 0.22, alpha_b = 0.14, data_scale=TRUE)
Simulate Covariance Matrix with an Auto-regressive (AR) Model
Description
Helper function to simulate Simulate Gaussian Data with an Autoregressive (AR) Model
Usage
SigmaAR(n_rows, p_columns, phi)
Arguments
n_rows |
Number of samples (rows of X). |
p_columns |
Number of variables (columns of X). |
phi |
Auto-regression coefficient. |
Value
A list with the values:
Sigma |
A covariance matrix. |
Omega |
A precision matrix. |
X |
A normalized data matrix with Gaussian distribution. |
Author(s)
Prof. Juan G. Colonna, PhD. juancolonna@icomp.ufam.edu.br
Prof. Marcelo Ruiz, PhD. mruiz@exa.unrc.edu.ar
Examples
data <- FastStepGraph::SigmaAR(30, 50, 0.4) # Simulate Gaussian Data
Searches for the optimal combination of alpha_f and alpha_b parameters using Cross-Validation
Description
cv.FastStepGraph implements the cross-validation for the Fast Step Graph algorithm.
Usage
cv.FastStepGraph(
x,
n_folds = 5,
alpha_f_min = 0.2,
alpha_f_max = 0.8,
b_coef = 0.5,
n_alpha = 32,
nei.max = 5,
data_scale = FALSE,
data_shuffle = TRUE,
max.iterations = NULL,
return_model = FALSE,
parallel = FALSE,
n_cores = NULL
)
Arguments
x |
Data matrix (of size n x p). |
n_folds |
Number of folds for the cross-validation procedure (default value 5). |
alpha_f_min |
Minimum threshold value for the cross-validation procedure (default value 0.2). |
alpha_f_max |
Minimum threshold value for the cross-validation procedure (default value 0.8). |
b_coef |
This parameter applies the empirical rule alpha_b=b_coef*alpha_f during the initial search for the optimal alpha_f parameter while alpha_b remains fixed, after finding optimal alpha_f, alpha_b is varied to find its optimal value. The default value of b_coef is 0.5. |
n_alpha |
Number of elements in the grid for the cross-validation (default value 32). |
nei.max |
Maximum number of variables in every neighborhood (default value 5). |
data_scale |
Boolean parameter (TRUE or FALSE), when to scale data to zero mean and unit variance (default FALSE). |
data_shuffle |
Boolean parameter (default TRUE), when samples (rows of X) must be randomly shuffled. |
max.iterations |
Maximum number of iterations (integer), the defaults values is set to p*(p-1). |
return_model |
Default FALSE. If set to TRUE, at the end of cross-validation, FastStepGraph is called with the optimal parameters alpha_f and alpha_b, returning |
parallel |
Boolean parameter (TRUE or FALSE), when to run Cross-Validation in parallel using a multicore architecture (default FALSE). |
n_cores |
An 'int' value specifying the number of cores do you want to use if 'parallel=TRUE'. If n_cores is not specified, the maximum number of cores on your machine minus one will be set automatically. |
Value
A list with the values:
alpha_f_opt |
the optimal alpha_f value. |
alpha_f_opt |
the optimal alpha_f value. |
CV.loss |
minimum loss. |
If return_model=TRUE, then also returns:
vareps |
Response variables. |
beta |
Regression coefficients. |
Edges |
Estimated set of edges. |
Omega |
Estimated precision matrix. |
Author(s)
Prof. Juan G. Colonna, PhD. juancolonna@icomp.ufam.edu.br
Prof. Marcelo Ruiz, PhD. mruiz@exa.unrc.edu.ar
Examples
data <- FastStepGraph::SigmaAR(30, 50, 0.4) # Simulate Gaussian Data
res <- FastStepGraph::cv.FastStepGraph(data$X, data_scale=TRUE)