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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.