| Name : R-mcb
| |
| Version : 0.1.15
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp155.17.1
| Date : 2024-06-08 09:34:18
|
| Group : Development/Libraries/Other
| Source RPM : R-mcb-0.1.15-lp155.17.1.src.rpm
|
| Size : 0.06 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Model Confidence Bounds
|
Description :
When choosing proper variable selection methods, it is important to
consider the uncertainty of a certain method. The model confidence
bound for variable selection identifies two nested models (upper and
lower confidence bound models) containing the true model at a given
confidence level. A good variable selection method is the one of which
the model confidence bound under a certain confidence level has the
shortest width. When visualizing the variability of model selection and
comparing different model selection procedures, model uncertainty curve
is a good graphical tool. A good variable selection method is the one
of whose model uncertainty curve will tend to arch towards the upper
left corner. This function aims to obtain the model confidence bound
and draw the model uncertainty curve of certain single model selection
method under a coverage rate equal or little higher than user-given
confidential level. About what model confidence bound is and how it
work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019)
Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.
< DOI:10.1111/biom.13024>. Besides, \'flare\' is needed only you apply the
SQRT or LAD method (\'mcb\' totally has 8 methods). Although \'flare\' has
been archived by CRAN, you can still get it in
< https://CRAN.R-project.org/package=flare> and the latest version is
useful for \'mcb\'.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
