[R-meta] AICc or variance components, which one matters more?
Luke Martinez
m@rt|nez|ukerm @end|ng |rom gm@||@com
Sat Nov 13 05:24:47 CET 2021
Hello Colleagues,
I've fit two candidate models (g1 and g2).
With g1, I get two non-zero variance components for 'lab' and 'study'.
With g2, I get a zero variance component for 'lab' and a non-zero one
for 'study'.
So, from the perspective of variance components, g1 seems like a better model.
But when I compare the models using AICc, g2 seems like a better model.
I wonder, then, which criterion should I use to choose the model (AICc
or the variance components)?
Thanks,
Luke
For reproducibility, I'm showing a somewhat similar situation with a
small section of my data below.
m="
lab study yi vi es_id
1 1 1.04 0.48503768 1
1 1 0.96 0.51076604 2
1 2 1.71 0.05767389 3
2 2 1.52 0.07539841 4
1 3 1.91 0.31349510 5
2 4 3.01 0.67910095 6
2 4 3.62 0.50670360 7
9 5 0.99 0.18297170 8
9 5 0.43 0.37225851 9
9 6 1.68 0.39072390 10
9 6 1.25 0.02879550 11"
dd <- read.table(text=m,h=T)
(g1=rma.mv(yi, vi, random = ~1|lab/study, data = dd))
estim sqrt nlvls fixed factor
sigma^2.1 0.1245 0.3529 3 no lab
sigma^2.2 0.2535 0.5035 7 no lab/study
(g2=rma.mv(yi, vi, random = list(~1|lab, ~1|study), data = dd))
estim sqrt nlvls fixed factor
sigma^2.1 0.0000 0.0000 3 no lab
sigma^2.2 0.5127 0.7160 6 no study
fitstats(g1,g2)[5,]
g1 g2
AICc: 30.85992 29.73897
(for full data, AICc of g2 is more noticeably smaller than that of g1)
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