deviance.bess

Similar to other deviance methods, which returns deviance from a fitted "<code>bess</code>" object.

An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.

Canhong Wen

BeSS

Best Subset Selection in Linear, Logistic and CoxPH Models

Aijun Zhang

Shijie Quan

Xueqin Wang

deviance.bess function

<dl> <dt>object</dt>
<dd>Output from the <code>bess</code> function or the <code>bess.one</code> function.</dd> <dt>...</dt>
<dd>Additional arguments affecting the predictions produced.</dd></dl>

Arguments

Canhong Wen, Aijun Zhang, Shijie Quan, and Xueqin Wang.

Author

Extract the deviance from a "bess" object. — deviance.bess

<dl>

 <dt>object</dt>
<dd>Output from the <code>bess</code> function or the <code>bess.one</code> function.</dd>

 <dt>...</dt>
<dd>Additional arguments affecting the predictions produced.</dd>

</dl>

deviance.bess: Extract the deviance from a "bess" object.

Description

Usage

Value

Arguments

Author

References

See Also

Examples