Last data update: 2014.03.03
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R: Sparse Normal/Adaptive lasso method for finding associated...
SNAL.calculation | R Documentation |
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
SNAL.calculation(Y, Phi, s2)
Arguments
Y |
Response vector of length N
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Phi |
Design matrix, with N rows and M columns (number of tested variables)
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s2 |
Variance assumed for the response variable, the tuning parameter of the adaptive lasso problem
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Value
gamma.star |
Estimates of gamma hyper-parameters
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ARD |
Posterior estimates of beta coefficients
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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
## Not run: SNAL.calculation(Y,Phi,s2=0.5)
Results
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