07-15-2014 09:50 PM
I am recent SAS user and kind of overwhelmed by various options.
Please, suggest right approach for (probably) standard modelling task below.
1. In PROC LOGISTIC, is there way to specify AUC (or c-statistics?) as
criteria for attribute removal/inclusion in forward/backward/step-wise
2. In PROC LOGISTIC, is there way to specify internal k-fold self-split
cross-validation in dataset
3. If these options are not available directly in PROC LOGISTIC,
are there frameworks / macros / code examples for
the following (e.g. backward) attribute selection process:
-*- for given dataset
-*-*- loop until no improvement to AUC
-*-*-*- loop all "current_attribute_set"
-*-*-*-*- remove attribute
-*-*-*-*- loop (e.g.) 10 times with stratified 10-fold train-test split
-*-*-*-*-*- run LOGISTIC(no_selection) in train set and Score test set
-*-*-*-*- combine all 10 test sets and get AUC (or c-statistics?)
-*-*-*- select best AUC and adjust "current_attribute_set" (IF there is an improvement!)
I found several relevant articles, but not a direct example:
Thanks in advance,
07-16-2014 03:14 PM
PROC LOGISTIC does not offer effect selection based on AUC, nor k-fold crossvalidation. However, LOGISTIC uses a 'leave one out' crossvalidation approximation when the CTABLE option is specified. See the "Details: Classification Table" section of the LOGISTIC documentation. Predicted probabilities based on this approximation are available using the PREDPROBS=X option in the OUTPUT statement. Note that k-fold crossvalidation is available in GLIMMIX and ADAPTIVEREG as mentioned in this note:
Note that ADAPTIVEREG can also fit a logistic model (using multivariate adaptive regression splines) and offers effect selection and k-fold crossvalidation. See its documentation for details and examples.
07-01-2016 12:17 PM
I am using SAS 9.4.
Using Proc Logistic, predprobs=crossvalidate, is it possible to output or estimate a single prediction error?
How could I generate such output?