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I'm having trouble using lmer beyond a first step.
My data: > some(exp1B) sub ba amplitude a b c d 2 1 1.00 1.5 65 63 4 8 41 4 1.15 0.0 92 41 3 4 43 4 1.15 3.0 88 48 2 2 63 6 1.00 3.0 50 72 9 9 77 8 1.15 0.0 112 25 2 1 89 10 1.15 0.0 37 33 36 34 126 13 1.15 1.5 80 50 6 4 140 14 1.15 4.5 12 115 6 7 145 20 1.00 0.0 73 65 0 2 147 20 1.00 3.0 63 72 2 3 etc.
The output: > summary(exp1B.both.cont.lmer) Linear mixed-effects model fit by maximum likelihood Formula: cbind(b, a) ~ ba + amplitude + (1 | sub) Data: exp1B AIC BIC logLik MLdeviance REMLdeviance 768.0086 783.2579 -379.0043 758.0086 766.3104 Random effects: Groups Name Variance Std.Dev. sub (Intercept) 0.18787 0.43344 Residual 6.36355 2.52261 # of obs: 156, groups: sub, 13
Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 4.118806 0.397780 153 10.3545 < 2.2e-16 ba -4.205518 0.330010 153 -12.7436 < 2.2e-16 amplitude 0.137958 0.023754 153 5.8076 3.547e-08
This is just what I need. But I also need predicted values, plots, etc, and can't figure out how to proceed. Have I overlooked a more extended document than the rather terse (for me at least) help page?
I regret to say no. Methods for generalized linear mixed models fit by lmer are still rather rudimentary.
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