Note 
====

Please contact Dabao Zhang (zhangdb@stat.purdue.edu) for any
relevant questions.


How to Use gPOCRE
================

1. To use cross-validation method to choose the tuning parameter,
   read and try Ex4CVGPOCRE.m and try
     > res = Ex4CVGPOCRE(‘l1’);

2. To use EBIC to choose the tuning parameter, read and try
   Ex4gPOCREPath.m and try
     > res = Ex4gPOCREPath(‘l1’);

3. Code a similar .m file to analyze your own data. You may want
   to change the settings in option according to your data
   analysis.

4. You can also use the function gPOCRE(...) if you want to try a
   particular lambda value.

Note
====

1. The algorithm is described in the following papers,

   Y. Lin, M. Zhang, and D. Zhang (2014). Fitting high-dimensional generalized linear models with penalized orthogonal-
components regression. In Preparation.

2. The optimal tuning parameter can be determined using either
cross-validation or EBIC (by Chen & Chen, 2010) or BIC or AIC or
AICC.

3. Note that gPOCRE(...) always assumes a fixed weight matrix W. If W is not provided, an identity matrix is used. So, it is recommended to run gPOCRE(...) to get an initial beta, call CalcOptW(...) to update W, then run gPOCRE(...) with the updated W. gPOCREPath(...) follows this strategy.