[R-sig-ME] Drop the correlation bet. random effects to find those with small variance

[Simon Harmel]

Dear All,

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?

library(lme4)

dat <- read.csv('
https://raw.githubusercontent.com/WRobertLong/Stackexchange/master/data/singular.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)

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