Hello all! So I'm doing a negative binomial regression, for my DV (timecz) and the interaction of two IVs (predation and age). I added in a random factor, spkr, and I get no values for the Wald chi2 or prob. 'spkr' has values 1, 2, 3, 4 so it is a categorical variable, and every one of my data points has a 'spkr' value to it.
I've also used a different random factor, family, and I get the Wald chi2 and prob values. 'family' is what family the subject came from, so again it is categorical and every data point has a value, but there are about 20 different family values.
I would appreciate any insight into this problem. Thank you!
menbreg timecz predation##age  spkr:, vce(robust)
Fitting fixedeffects model:
Iteration 0: log likelihood = 610.48561
Iteration 1: log likelihood = 585.81128
Iteration 2: log likelihood = 584.98962
Iteration 3: log likelihood = 584.9823
Iteration 4: log likelihood = 584.9823
Refining starting values:
Grid node 0: log likelihood = 586.93141
Fitting full model:
Iteration 0: log pseudolikelihood = 586.93141 (not concave)
Iteration 1: log pseudolikelihood = 584.50204
Iteration 2: log pseudolikelihood = 584.30355
Iteration 3: log pseudolikelihood = 584.24794
Iteration 4: log pseudolikelihood = 584.24786
Iteration 5: log pseudolikelihood = 584.24786
Mixedeffects nbinomial regression Number of obs = 117
Overdispersion: mean
Group variable: spkr Number of groups = 4
Obs per group: min = 27
avg = 29.2
max = 32
Integration method: mvaghermite Integration points = 7
Wald chi2(3) = .
Log pseudolikelihood = 584.24786 Prob > chi2 = .
(Std. Err. adjusted for clustering on spkr)

 Robust
timecz  Coef. Std. Err. z P>z [95% Conf. Interval]
+

2.predation  .0265594 .6956106 0.04 0.970 1.336812 1.389931

age 
2  .4622833 .2973759 1.55 0.120 .1205628 1.045129
3  .5894719 .5789841 1.02 0.309 .545316 1.72426

predation#age 
2 2  .6903936 .5020987 1.38 0.169 .2937017 1.674489
2 3  .0129458 1.045617 0.01 0.990 2.036425 2.062317

_cons  3.627404 .3742223 9.69 0.000 2.893942 4.360866
+
/lnalpha  .6274604 .1765955 3.55 0.000 .2813397 .9735812
+
spkr 
var(_cons) .0659578 .0592337 .0113459 .3834348

I've also used a different random factor, family, and I get the Wald chi2 and prob values. 'family' is what family the subject came from, so again it is categorical and every data point has a value, but there are about 20 different family values.
I would appreciate any insight into this problem. Thank you!
menbreg timecz predation##age  spkr:, vce(robust)
Fitting fixedeffects model:
Iteration 0: log likelihood = 610.48561
Iteration 1: log likelihood = 585.81128
Iteration 2: log likelihood = 584.98962
Iteration 3: log likelihood = 584.9823
Iteration 4: log likelihood = 584.9823
Refining starting values:
Grid node 0: log likelihood = 586.93141
Fitting full model:
Iteration 0: log pseudolikelihood = 586.93141 (not concave)
Iteration 1: log pseudolikelihood = 584.50204
Iteration 2: log pseudolikelihood = 584.30355
Iteration 3: log pseudolikelihood = 584.24794
Iteration 4: log pseudolikelihood = 584.24786
Iteration 5: log pseudolikelihood = 584.24786
Mixedeffects nbinomial regression Number of obs = 117
Overdispersion: mean
Group variable: spkr Number of groups = 4
Obs per group: min = 27
avg = 29.2
max = 32
Integration method: mvaghermite Integration points = 7
Wald chi2(3) = .
Log pseudolikelihood = 584.24786 Prob > chi2 = .
(Std. Err. adjusted for clustering on spkr)

 Robust
timecz  Coef. Std. Err. z P>z [95% Conf. Interval]
+

2.predation  .0265594 .6956106 0.04 0.970 1.336812 1.389931

age 
2  .4622833 .2973759 1.55 0.120 .1205628 1.045129
3  .5894719 .5789841 1.02 0.309 .545316 1.72426

predation#age 
2 2  .6903936 .5020987 1.38 0.169 .2937017 1.674489
2 3  .0129458 1.045617 0.01 0.990 2.036425 2.062317

_cons  3.627404 .3742223 9.69 0.000 2.893942 4.360866
+
/lnalpha  .6274604 .1765955 3.55 0.000 .2813397 .9735812
+
spkr 
var(_cons) .0659578 .0592337 .0113459 .3834348
