Everyone -

What do the NaN's mean here?  Is this analysis a problem?


Linear mixed-effects model fit by maximum likelihood
 Data: tmp.dat
       AIC      BIC    logLik
  1611.251 1638.363 -797.6253

Random effects:
 Formula: ~1 | group_id
         (Intercept) Residual
StdDev: 0.0003077668 9.236715

Fixed effects: AvgTrials ~ time + factor(group_id) + time *
factor(group_id)
                           Value Std.Error  DF   t-value p-value
(Intercept)            18.159722  3.576664 213  5.077279  0.0000
time                    4.192708  1.655674 213  2.532327  0.0121
factor(group_id)2      -6.929563  5.235700   0 -1.323522     NaN
factor(group_id)3      -1.654554  4.189575   0 -0.394922     NaN
time:factor(group_id)2  1.729911  2.423658 213  0.713760  0.4762
time:factor(group_id)3 -2.555111  1.939396 213 -1.317478  0.1891
 Correlation:
                       (Intr) time   fc(_)2 fc(_)3 t:(_)2
time                   -0.926
factor(group_id)2      -0.683  0.632
factor(group_id)3      -0.854  0.790  0.583
time:factor(group_id)2  0.632 -0.683 -0.926 -0.540
time:factor(group_id)3  0.790 -0.854 -0.540 -0.926  0.583

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max
-1.8842754 -0.6979785 -0.3370998  0.5666704  3.0943948

Number of Observations: 219
Number of Groups: 3
Warning message:
In pt(q, df, lower.tail, log.p) : NaNs produced

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