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08-24-2012 04:13 PM

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

Accepted Solutions

Solution

08-30-2012
09:20 AM

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Posted in reply to issac

08-30-2012 09:20 AM

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).

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Posted in reply to issac

08-27-2012 07:31 AM

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

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Posted in reply to SteveDenham

08-30-2012 08:07 AM

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.

Solution

08-30-2012
09:20 AM

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Posted in reply to issac

08-30-2012 09:20 AM

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).

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Posted in reply to issac

08-30-2012 11:34 AM

Ivm;

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

Issac