Hi all,
I'm trying to see if the rate of change of var X covary with the rate
of change of var Y across time using xtmixed model.
Runnnig this command in STATA11, none of the standard errors for the
random parts were generated. I read in an earlier thread suggesting a
translation of the variables with a mean or minimum value. I have done
that to both BMI and memory but the results were the same. Why? In
fact, can I still trust the estimates in the model anyway?
Origianl command and output
xtmixed varY c.varX##i.agegp1 c.Time##i.agegp1 || StudyID: Time varX,
cov(unstr) var mle
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -4950.0532
Iteration 1: log likelihood = -4946.0478
Iteration 2: log likelihood = -4945.7244
Iteration 3: log likelihood = -4945.0769 (not concave)
Iteration 4: log likelihood = -4945.0712
Iteration 5: log likelihood = -4945.0652
Iteration 6: log likelihood = -4945.0614
Iteration 7: log likelihood = -4945.0608
Iteration 8: log likelihood = -4945.0606
Computing standard errors:
standard-error calculation failed
Mixed-effects ML regression Number of obs = 1301
Group variable: StudyID Number of groups = 482
Obs per group: min = 1
avg = 2.7
max = 3
Wald chi2(5) = 235.11
Log likelihood = -4945.0606 Prob > chi2 = 0.0000
varY Coef. Std. Err. z P>z [95% Conf. Interval]
varX .0710077 .0934028 0.76 0.447 -.1120585 .2540739
1.agegp1 -23.19086 5.407541 -4.29 0.000 -33.78945 -12.59228
agegp1#c.varX
1 .4936027 .19496 2.53 0.011 .1114881 .8757173
Time .8146081 .100225 8.13 0.000 .6181708 1.011045
agegp1#
c.Time
1 -.9569501 .1776534 -5.39 0.000 -1.305144 -.6087557
_cons 77.36584 2.625491 29.47 0.000 72.21997 82.51171
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
StudyID: Unstructured
var(Time) .7128062 . . .
var(varX) .0130577 . . .
var(_cons) 178.6541 . . .
cov(Time,varZ) -.020441 . . .
cov(Time,_cons) -2.190107 . . .
cov(varZ,_cons) -1.40149 . . .
var(Residual) 55.52195 . . .
LR test vs. linear regression: chi2(6) = 407.56 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
***********************************************************
Now, using varZ and varY, in the exact same command as above, I got
the output below. What does Hessian is not negative semidefinite
really mean? What can I do about my data to make the model converge?
Iteration 10: log likelihood = -4787.6438 (backed up)
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
Hessian is not negative semidefinite
Mixed-effects ML regression Number of obs = 1290
Group variable: StudyID Number of groups = 481
Obs per group: min = 1
avg = 2.7
max = 3
Wald chi2(5) = 100.38
Log likelihood = -4797.623 Prob > chi2 = 0.0000
varY Coef. Std. Err. z P>z [95% Conf. Interval]
varZ .1605253 6.491569 0.02 0.980 -12.56272 12.88377
1.agegp1 -12.57239 9.058903 -1.39 0.165 -30.32752 5.182731
agegp1#c.varZ
1 5.441863 11.21308 0.49 0.627 -16.53536 27.41909
Time .0161628 .0922692 0.18 0.861 -.1646814 .1970071
agegp1#
c.Time
1 -.4144446 .1644543 -2.52 0.012 -.7367692 -.0921201
_cons 51.92391 5.174088 10.04 0.000 41.78288 62.06493
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
StudyID: Unstructured
var(Time) .5704303 . . .
var(varZ) 113.3907 . . .
var(_cons) 99.90904 . . .
cov(Time,varZ) -1.132285 . . .
cov(Time,_cons) -.6162719 . . .
cov(varZ,_cons) -53.27241 . . .
var(Residual) 47.8776 . . .
LR test vs. linear regression: chi2(6) = 394.06 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
Warning: convergence not achieved; estimates are based on iterated EM
********************************************************************************************
Your help would be much appreciated.
Regards,
Ada
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