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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