[R-meta] Testing interaction term of categorical moderators in rma.mv
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Tue Sep 17 17:00:13 CEST 2019
1) Coefficients 5 and 6 make up the interaction terms. So it would be btt=5:6. Instead of refitting the model, you can also do:
2) Yes, you could do that. But I would stick to the Wald-type test that is conducted with the approach above. Then you can do all your analyses with REML.
From: Ju Lee [mailto:juhyung2 using stanford.edu]
Sent: Tuesday, 17 September, 2019 6:39
To: r-sig-meta-analysis using r-project.org
Cc: Viechtbauer, Wolfgang (SP)
Subject: Testing interaction term of categorical moderators in rma.mv
Dear Wolfgang and all,
I am writing to ask a question of how I could perform the test to acquire main interaction effect of two categorical moderators (that have 3 and 2 levels each)
I have been referring to http://www.metafor-project.org/doku.php/tips:multiple_factors_interactions to do this, but have been troubled how to proceed.
1) In the post, Wolfgang suggests:
"To test whether the interaction is significant in general, you can either do a Wald-type test with:
rma(yi, vi, mods = ~ factor(catmod1)*factor(catmod2), data=some.data.frame, btt=X:Y)
where X is the number of the first "interaction coefficient" and Y is the number of the last "interaction coefficient" (so, these are indices to indicate which coefficients should be tested simultaneously). In the output, you will the results of this test under "Test of Moderators"."
However, I am not sure how I can specify the first and last coefficient of interaction terms from a output below. I would deeply appreciate if you could help me figure this out.
> r1<-rma.mv(hedged,VCV, mods=~ Region * Consumption.level, method="ML", random = ~ region.cl | Study, data=MHF, struct="DIAG", subset=(!is.na(region.cl)))
Multivariate Meta-Analysis Model (k = 841; method: ML)
outer factor: Study (nlvls = 176)
inner factor: region.cl (nlvls = 6)
estim sqrt k.lvl fixed level
tau^2.1 0.6893 0.8302 113 no High latitude:Primary
tau^2.2 0.6354 0.7971 380 no High latitude:Secondary
tau^2.3 1.1672 1.0804 211 no High latitude:Tertiary
tau^2.4 1.2214 1.1052 80 no Low latitude:Primary
tau^2.5 0.0000 0.0001 16 no Low latitude:Secondary
tau^2.6 0.3215 0.5670 41 no Low latitude:Tertiary
Test for Residual Heterogeneity:
QE(df = 835) = 3679.1163, p-val < .0001
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 21.9742, p-val = 0.0005
estimate se zval pval ci.lb ci.ub
intrcpt 0.3007 0.1904 1.5788 0.1144 -0.0726 0.6739
RegionLow latitude -0.4096 0.3263 -1.2552 0.2094 -1.0491 0.2300
Consumption.levelSecondary 0.2006 0.2156 0.9303 0.3522 -0.2220 0.6232
Consumption.levelTertiary 0.7136 0.2497 2.8577 0.0043 0.2242 1.2031 **
RegionLow latitude:Consumption.levelSecondary 0.1687 0.3523 0.4787 0.6322 -0.5219 0.8593
RegionLow latitude:Consumption.levelTertiary -0.1613 0.4201 -0.3838 0.7011 -0.9847 0.6622
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
2) Secondly, as an alternative Wolfgang suggests using likelihood-ratio test with full (with interaction term) and reduced (without interaction term) models to test the significant interaction.
However, all my models are based on "REML" method, and apparently I need to fit models with "ML" to do the above test. Is it appropriate if conduct LRT for interaction using "ML", but keep the rest of my analysis using "REML" method?
Thank you for reading, and I sincerely hope to hear back from you.
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