Message-ID: <e522ef0b34bf49beb3bd9aa5996f1a11@UM-MAIL3214.unimaas.nl>
Date: 2019-09-17T15:00:13Z
From: Wolfgang Viechtbauer
Subject: [R-meta] Testing interaction term of categorical moderators in rma.mv
In-Reply-To: <SN6PR02MB4336D66142A8970150EEF0D8F78F0@SN6PR02MB4336.namprd02.prod.outlook.com>
Hello Ju,
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:
anova(r1, btt=5:6)
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.
Best,
Wolfgang
-----Original Message-----
From: Ju Lee [mailto:juhyung2 at stanford.edu]
Sent: Tuesday, 17 September, 2019 6:39
To: r-sig-meta-analysis at 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)))
>r1
Multivariate Meta-Analysis Model (k = 841; method: ML)
Variance Components:?
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
Model Results:
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?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.
Best regards,
JU