predict.bess

Similar to other predict methods, which returns predictions 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

predict.bess function

<dl> <dt>object</dt>
<dd>Output from the <code>bess</code> function or the <code>bess.one</code> function.</dd> <dt>newdata</dt>
<dd>New data used for prediction.</dd> <dt>type</dt>
<dd>Types of coefficients returned. <code> type = "AIC"</code> cooresponds to the predictor with optimal AIC value; <code> type = "BIC"</code> cooresponds to the predictor with optimal BIC value;
<code> type = "EBIC"</code> cooresponds to the predictor with optimal EBIC value;<code> type = "ALL"</code> cooresponds to all predictors in the <code>bess</code> object; <code> type = "opt"</code> cooresponds to predictors in best model. Default is <code>ALL</code>.</dd> <dt>...</dt>
<dd>Additional arguments affecting the predictions produced.</dd></dl>

Arguments

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

Author

make predictions from a "bess" object. — predict.bess

<dl>

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

 <dt>newdata</dt>
<dd>New data used for prediction.</dd>

 <dt>type</dt>
<dd>Types of coefficients returned. <code> type = "AIC"</code> cooresponds to the predictor with optimal AIC value; <code> type = "BIC"</code> cooresponds to the predictor with optimal BIC value;
<code> type = "EBIC"</code> cooresponds to the predictor with optimal EBIC value;<code> type = "ALL"</code> cooresponds to all predictors in the <code>bess</code> object; <code> type = "opt"</code> cooresponds to predictors in best model. Default is <code>ALL</code>.</dd>

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

</dl>

predict.bess: make predictions from a "bess" object.

Description

Usage

Value

Arguments

Author

References

See Also

Examples