Sparse Normal/Adaptive lasso method for finding associated variables. The SNAL method is applied to the linear regression Y= Phi beta + epsilon

Description

For more details please read SNAL.

Usage

1
SNAL.calculation(Y, Phi, s2)

Arguments

Y

Response vector of length N

Phi

Design matrix, with N rows and M columns (number of tested variables)

s2

Variance assumed for the response variable, the tuning parameter of the adaptive lasso problem

Value

gamma.star

Estimates of gamma hyper-parameters

ARD

Posterior estimates of beta coefficients

References

Evangelou, M., Dudbridge, F., Wernisch, L. (2014). Two novel pathway analysis methods based on a hierarchical model. Bioinformatics, 30(5), 690 - 697

Wipf, D. and Nagarajan, S. (2008). A new view of automatic relevance determination. Advances in Neural Information Processing Systems, 20

See Also

SNAL

Examples

1
## Not run: SNAL.calculation(Y,Phi,s2=0.5)