[R-sig-ME] Drop the correlation bet. random effects to find those with small variance
@|m@h@rme| @end|ng |rom gm@||@com
Wed Sep 30 21:57:23 CEST 2020
Bates, et al. (2015) <https://arxiv.org/pdf/1506.04967.pdf> mention that to
identify a mixed-model with a singular variance-covariance matrix we can:
Fit a zero correlation parameter which will identify random effects with
zero, or very small, variance
That is, going from `m0` to `m1` (see below). BUT, how come dropping all
correlations between slopes and intercepts can lead to identifying random
effects with zero, or very small, variance?
dat <- read.csv('
m0 <- lmer(y ~ A * B * C + (A * B * C | group), data = dat)
m1 <- lmer(y ~ A * B * C + (A * B * C || group), data = dat)
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models