Converting random effect expression from SAS to R lmer syntax - r

Question

Random effects of my mixed models formula in SAS proc mixed syntax looks like this:
random intercept color size
/type = vc subject = group solution;
I converted it to R lmer syntax as follows:
((1|group) + (0 + color|group)) + ((1|group) + (0 + size|group))
Is it correct?
Can I represent sas random effects formula as follows:
(1|group) + (0 + color|group)) + (0 + size|group) ?
Or is it a wrong implementation in R?

Answer 1

I'm unfamiliar with the SAS syntax, but a quick look at the SAS documentation for the PROC MIXED arguments SUBJECT = and TYPE gives the following info:
identifies the subjects in your mixed model. Complete independence is assumed across subjects; thus, for the RANDOM statement, the SUBJECT= option produces a block-diagonal structure in with identical blocks. The matrix is modified to accommodate this block diagonality. In fact, specifying a subject effect is equivalent to nesting all other effects in the RANDOM statement within the subject effect.
and
specifies standard variance components and is the default structure for both the RANDOM and REPEATED statements. In the RANDOM statement, a distinct variance component is assigned to each effect
Using table 2 (page 7) in the lme4 vignete for fitting mixed effect models, we are looking for the following statement
library(lme4)
fit <- (g)lmer(outcome ~ fixed_effects + (1|subject/color) + (1|subject/size), data = data)
It has been a while, so I don't remember if a parenthesis works out in the random effects such that it can be reduced to (1|subject/(color + size)). This states that "color and size are random intercepts, nested with subject".
Please note that I filled in the entire call. Here one would have to change outcome, fixed_effects and data to suite your data.