In #6, there was a wrapper added to perform the extraction. However, we extracted a bit too many terms. In particular, we only want to extract elements corresponding to the random-effects, not all effects (p). c.f.
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T1 <- VarCorr(x)[[group_term]][1:p,1:p] |
Consider revising given:
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# Figure out the class term: |
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term_labels <- attr(mf, "names") |
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# Retrieve the _LAST_ variable (assume it is a group variable.) |
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# To Do: What is the best way to make this approach robust ? |
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group_term <- term_labels[length(term_labels)] |
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T1 <- VarCorr(x)[[group_term]][1:p,1:p] |
Issue arose with:
m <- lmer(DV ~ P + P:H + (1+P||id), data = data)
No data was given, so an equivalent test case should be:
fm1 <- lmer(Reaction ~ Days + (1+Days || Subject), data = sleepstudy)
In #6, there was a wrapper added to perform the extraction. However, we extracted a bit too many terms. In particular, we only want to extract elements corresponding to the random-effects, not all effects (
p). c.f.iccbeta/R/iccbeta.R
Line 152 in f649ced
Consider revising given:
iccbeta/R/iccbeta.R
Lines 145 to 152 in f649ced
Issue arose with:
No data was given, so an equivalent test case should be: