Turn on suggestions

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

Showing results for

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Problem with 'second order optimality condition' in proc nlmixed

Options

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

🔒 This topic is **solved** and **locked**.
Need further help from the community? Please
sign in and ask a **new** question.

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Posted 08-24-2012 04:13 PM
(5783 views)

Hi everyone;

Am working with 9.3 version. I submit the following code :

proc nlmixed data=x;

lambda=exp(b0+blogdisage*logdisage+bloglos*loglos+bpows*powsYES

++b1mar*marMARRIED+b2mar*marPREVIOUSLY_MARRIED

+bseq*seq+e);

ll=-lambda*rtime**(alpha+1)+rstatus*(LOG(alpha+1)+alpha*LOG(rtime)+LOG(lambda));

MODEL rtime~GENERAL(ll);

RANDOM e~NORMAL(0,s2) SUBJECT=id;

PARMS b0=1 blogdisage=0 bloglos=0 bpows=0 b1mar=0 b2mar=0 bseq=0 s2=1 alpha=0;

run;

and got this notice in log:

NOTE: FCONV convergence criterion satisfied.

NOTE: At least one element of the (projected) gradient is greater than 1e-3.

WARNING: The final Hessian matrix is full rank but has at least one negative eigenvalue.

Second-order optimality condition violated.

I could not understand what's happened but I have got the following parameter estimates that has nothing for S2 (Random Variance).

b0 | 0.7987 | 0.6933 | 2443 | 1.15 | 0.2494 | 0.05 | -0.5608 | 2.1582 | 717.9996 |
---|---|---|---|---|---|---|---|---|---|

blogdisage | -0.7512 | 0.1124 | 2443 | -6.68 | <.0001 | 0.05 | -0.9717 | -0.5308 | 2882.034 |

bloglos | -0.1244 | 0.03542 | 2443 | -3.51 | 0.0005 | 0.05 | -0.1939 | -0.05496 | 834.6029 |

bpows | 0.2024 | 0.4954 | 2443 | 0.41 | 0.6829 | 0.05 | -0.7691 | 1.1740 | -717.951 |

b1mar | 0.03810 | 0.04843 | 2443 | 0.79 | 0.4315 | 0.05 | -0.05687 | 0.1331 | 34.79886 |

b2mar | 0.06841 | 0.04097 | 2443 | 1.67 | 0.0951 | 0.05 | -0.01194 | 0.1488 | 190.9215 |

bseq | -0.1507 | 0.03239 | 2443 | -4.65 | <.0001 | 0.05 | -0.2142 | -0.08721 | 829.1515 |

s2 | -111E-14 | . | 2443 | . | . | 0.05 | . | . | 234.112 |

alpha | -0.4764 | 0.02492 | 2443 | -19.12 | <.0001 | 0.05 | -0.5252 | -0.4275 | 2446.752 |

Appreciate your comments on this.

Thanks!

Issac

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Check out this excellent article from the most recent SAS Global Forum.

http://support.sas.com/resources/papers/proceedings12/332-2012.pdf

This paper deals with most of the common errors and warnings one can receive using the mixed-model procedures. Your Warning is discussed on page 14 (with good corrective hints).

4 REPLIES 4

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

This is all a guess, without seeing the data structure. I think there is a "complete solution" to the existing data, given the fixed effects, leaving no variability from individual to individual after accounting for all fixed effects. I certainly would not trust any of the standard errors or tests, given that the Hessian is non-positive definite. It may be that the model is over-specified. What happens if you drop some of the fixed effects?

Steve Denham

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Steve;

Thanks for your response. When I drop 'e' (random effect) from the model new error named "Floating Point Zero Divide" has shown up. Also when I exclude some of categorical columns, it did'n change anything. I also check the over specification condition and almost sure that it is not the case in the model.

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Check out this excellent article from the most recent SAS Global Forum.

http://support.sas.com/resources/papers/proceedings12/332-2012.pdf

This paper deals with most of the common errors and warnings one can receive using the mixed-model procedures. Your Warning is discussed on page 14 (with good corrective hints).

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Ivm;

Thanks so much for bring this good paper into my attention. That's great!

Issac

**Available on demand!**

Missed SAS Innovate Las Vegas? Watch all the action for free! View the keynotes, general sessions and 22 breakouts on demand.

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.