Description Usage Arguments Value References See Also Examples
This function implements the same model selection technique extensively
described in stabilityGLM
. The sole difference is the use
of a different elastic net solver. In this function, we make use of
biglasso
. Thanks to its parallel
backend, biglasso
scales well to
high-dimensional GWAS datasets. However, in our case, because of the use of
additional backend files, a slight decrease in runtime is to be expected,
compared with stabilityGLM
.
1 2 3 | stabilityBIG(X, Y, family = "gaussian", n_subsample = 20,
n_lambda = 100, lambda_min_ratio = 0.01, eps = 1e-05,
short = TRUE, ncores = 2)
|
X |
design matrix formatted as a
|
Y |
response vector |
family |
response type. Either 'gaussian' or 'binomial' |
n_subsample |
number of subsamples for stability selection |
n_lambda |
total number of lambda values |
lambda_min_ratio |
the minimum value of the regularization parameter lambda as a fraction of the maximum lambda, the first value for which the elastic net support is not empty. |
eps |
elastic net mixing parameter (see |
short |
whether to compute the aucs only on the first half of the stability path. We observed better performance with thresholded paths |
ncores |
number of cores for the
|
a vector grouping the aucs of all covariates within X
Slim, L., Chatelain, C., Azencott, C.-A., & Vert, J.-P. (2018). Novel Methods for Epistasis Detection in Genome-Wide Association Studies. BioRxiv.
Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417–473.
Haury, A. C., Mordelet, F., Vera-Licona, P., & Vert, J. P. (2012). TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Systems Biology, 6.
Other support estimation functions: stabilityGLM
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