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Basic random effect structures and non-convergence in lmer()
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library(lme4) | |
library(nlme) | |
library(ggplot2) | |
data(Oxboys) | |
head(Oxboys, 12) | |
ggplot(aes(y=height, x=age), data=Oxboys) + | |
geom_point() + geom_path(aes(group=Subject)) | |
## random intercepts only | |
m1 <- lmer(height ~ age + (1|Subject), data=Oxboys) | |
## uncorrelated random intercepts and random slopes | |
m2 <- lmer(height ~ age + (1|Subject) + (0+age|Subject), data=Oxboys) | |
## m2 <- lmer(height ~ age + (1+age||Subject), data=Oxboys) ## alt syntax for uncorr. slope/int. Note double "|". | |
## random intercepts, random slopes, and their correlation | |
m3 <- lmer(height ~ age + (1+age|Subject), data=Oxboys) | |
summary(m1) | |
summary(m2) | |
summary(m3) | |
## In our work, a major reason for using less than a maximal random effect structure is | |
## the presence of convergence issues in models that include rich random effects. So, if | |
## if a model with maximal raneff does not converge, our typical first step is to retry with | |
## orthogonal slopes and intercepts (e.g., m2 above). If that fails, then we will try | |
## different optimizers as proposed here | |
## https://rstudio-pubs-static.s3.amazonaws.com/33653_57fc7b8e5d484c909b615d8633c01d51.html | |
## and here (pp14-15) | |
## https://rstudio-pubs-static.s3.amazonaws.com/33653_57fc7b8e5d484c909b615d8633c01d51.html | |
## | |
## In the second case, pay particular attention to this advice: | |
## "try all available optimizers (e.g. several different implementations of BOBYQA and | |
## NelderMead, L-BFGS-B from optim, nlminb, . . . ) via the allFit() function, see ‘5.’ in | |
## the examples. While this will of course be slow for large fits, we consider it the gold | |
## standard; if all optimizers converge to values that are practically equivalent, then we | |
## would consider the convergence warnings to be false positives." | |
## ** Sub-optimal random effects | |
## In LMMs, sub-optimal handling of random effects can lead to shrinking of SEs | |
## for fixed effects with the end result being increased liklihood of type 1 | |
## error (Barr et al., 2013; Mirman, 2014, p62). |
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