# st: standard errors failed to be calculated in xtmixed

 From Ada Lo <[email protected]> To [email protected] Subject st: standard errors failed to be calculated in xtmixed Date Wed, 6 Jan 2010 11:48:16 +1000

```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:

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,