turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Proc logistics warning: Ridging has failed

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

11-16-2009 04:44 PM

Hi,

I am running proc logistics with backward selection. I am getting warning that Ridging has failed.

Can anyone explain what the warning means and some solution.

I appreciate all your help.

Thanks,

Amit

**CODE:**

proc logistic data=out.train1 NAMELEN=32;

model del123_target (event = '1') = &VarName

/ selection=backward fast sls=0.1;

run;

**LOG:**

NOTE: PROC LOGISTIC is modeling the probability that del123_target=1.

WARNING: Ridging has failed to improve the loglikelihood in Step 0. You may want to

use a different ridging technique (RIDGING= option), or switch to using

linesearch to reduce the step size (RIDGING=NONE), or specify a new set of

initial estimates (INEST= option).

WARNING: The LOGISTIC procedure continues in spite of the above warning. Results

shown are based on the last maximum likelihood iteration. Validity of the

model fit is questionable.

NOTE: Convergence criterion (GCONV=1E-8) satisfied in Step 1.

NOTE: There were 1315176 observations read from the data set OUT.TRAIN1.

NOTE: PROCEDURE LOGISTIC used (Total process time):

real time 8:19.57

cpu time 4:52.89

I am running proc logistics with backward selection. I am getting warning that Ridging has failed.

Can anyone explain what the warning means and some solution.

I appreciate all your help.

Thanks,

Amit

proc logistic data=out.train1 NAMELEN=32;

model del123_target (event = '1') = &VarName

/ selection=backward fast sls=0.1;

run;

NOTE: PROC LOGISTIC is modeling the probability that del123_target=1.

WARNING: Ridging has failed to improve the loglikelihood in Step 0. You may want to

use a different ridging technique (RIDGING= option), or switch to using

linesearch to reduce the step size (RIDGING=NONE), or specify a new set of

initial estimates (INEST= option).

WARNING: The LOGISTIC procedure continues in spite of the above warning. Results

shown are based on the last maximum likelihood iteration. Validity of the

model fit is questionable.

NOTE: Convergence criterion (GCONV=1E-8) satisfied in Step 1.

NOTE: There were 1315176 observations read from the data set OUT.TRAIN1.

NOTE: PROCEDURE LOGISTIC used (Total process time):

real time 8:19.57

cpu time 4:52.89

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

11-16-2009 11:33 PM

Amit,

The warning in the log is just indicating that the iterative maximum likelihood optimization method for fitting the model failed to find a solution. As with any optimization program, this can happen for some models on some data sets. The message suggests some ways you can change the optimizer which *may* allow the procedure to find a solution. In fact, if the procedure does not converge, it presents the results based on the last iteration. This is not the maximum likelihood solution, so you may not even want to use the results. To fix the problem, you can try the suggested options. If they don't work, you may have to change your model... probably by reducing the number of parameters in it.

The warning in the log is just indicating that the iterative maximum likelihood optimization method for fitting the model failed to find a solution. As with any optimization program, this can happen for some models on some data sets. The message suggests some ways you can change the optimizer which *may* allow the procedure to find a solution. In fact, if the procedure does not converge, it presents the results based on the last iteration. This is not the maximum likelihood solution, so you may not even want to use the results. To fix the problem, you can try the suggested options. If they don't work, you may have to change your model... probably by reducing the number of parameters in it.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

11-23-2009 03:28 PM

SELECTION=BACKWARD is usually a bad idea when there are many candidate effects in the model. The reason it is a bad idea is that the method starts with the most complex model which includes all of the candidate effects. This model is usually so complex and makes the data so sparse that the maximum likelihood solution does not exist. Better to use SELECTION=FORWARD or STEPWISE which starts with no effects (or only those forced by the INCLUDE= option) and built up a model.