[R-meta] Testing interaction term of categorical moderators in rma.mv
Ju Lee
juhyung2 @end|ng |rom @t@n|ord@edu
Tue Sep 17 18:05:28 CEST 2019
Dear Wolfgang,
I have to throw in another quick question related to your response. I apologize for this inconvenience.
Now I understood that "btt" argument should basically cover whatever number of coefficients shown in the output. So in case there is only one coefficient for interaction term ( in case of 2 moderators with 2 levels each, so 2x2), I will just need to specify "btt=4:4" as I have done below?
> r1<-rma.mv(hedged,VCV, mods=~ factor(Region) * factor(Consumer.habitat.specialization), method="ML", random = ~ region.chs | Study, data=MHF, struct="DIAG", subset=(!is.na(region.chs)))
> r1
Multivariate Meta-Analysis Model (k = 716; method: ML)
Variance Components:
outer factor: Study (nlvls = 160)
inner factor: region.chs (nlvls = 4)
estim sqrt k.lvl fixed level
tau^2.1 0.7810 0.8837 470 no High latitude:Generalist
tau^2.2 1.0199 1.0099 133 no High latitude:Specialist
tau^2.3 0.9985 0.9992 90 no Low latitude:Generalist
tau^2.4 0.3626 0.6022 23 no Low latitude:Specialist
Test for Residual Heterogeneity:
QE(df = 712) = 3194.7722, p-val < .0001
Test of Moderators (coefficient(s) 2:4):
QM(df = 3) = 19.9822, p-val = 0.0002
Model Results:
estimate se zval pval ci.lb
intrcpt 0.7745 0.0905 8.5554 <.0001 0.5971
factor(Region)Low latitude -0.5404 0.2551 -2.1179 0.0342 -1.0405
factor(Consumer.habitat.specialization)Specialist -0.6569 0.2044 -3.2135 0.0013 -1.0575
factor(Region)Low latitude:factor(Consumer.habitat.specialization)Specialist 0.3572 0.3959 0.9022 0.3670 -0.4188
ci.ub
intrcpt 0.9519 ***
factor(Region)Low latitude -0.0403 *
factor(Consumer.habitat.specialization)Specialist -0.2562 **
factor(Region)Low latitude:factor(Consumer.habitat.specialization)Specialist 1.1332
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
> anova(r1,btt=4)
Test of Moderators (coefficient(s) 4):
QM(df = 1) = 0.8139, p-val = 0.3670
Thank you very much for your help!
Best regards,
JU
________________________________
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> on behalf of Ju Lee <juhyung2 using stanford.edu>
Sent: Tuesday, September 17, 2019 8:47 AM
To: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Subject: Re: [R-meta] Testing interaction term of categorical moderators in rma.mv
Dear Wolfgang,
Thank you very much for clarifying this. This is extremely helpful!
I will make sure to cc the mailing list for future correspondence.
Sincerely,
JU
________________________________
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Tuesday, September 17, 2019 8:38 AM
To: Ju Lee <juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Subject: RE: Testing interaction term of categorical moderators in rma.mv
Please always cc the mailing list when replying.
1) There are 6 combinations, but the interaction is given by those 2 model coefficients. Please reread http://www.metafor-project.org/doku.php/tips:multiple_factors_interactions which covers exactly this case (two factors with 2 and 3 levels, respectively). So, yes, btt=5:6 tests whether there is an interaction.
2) One can mix, but it can also lead to inconsistencies. For testing fixed effects, the Wald-type test is often preferred anyway.
Best,
Wolfgang
-----Original Message-----
From: Ju Lee [mailto:juhyung2 using stanford.edu]
Sent: Tuesday, 17 September, 2019 17:33
To: Viechtbauer, Wolfgang (SP)
Subject: Re: Testing interaction term of categorical moderators in rma.mv
Dear Wolfgang,
Thank you very much for your response.
I wanted to follow up from my previous question to clarify:
1) So my model should produce total of 6 categorical groups of interaction term (low:primary, low:secondary, low:tertiary, high:primary, high:secondary, high:tertiary). And what is shown in my mixed model below 5th and 6th coefficient is just subset of these. If I use "btt=5:6" argument, will the model still be testing the main interaction effect of "latitude x trophic level" which produces 6 interaction categories as stated above?
2) It's great to know that it is more advisable not to mix REML and ML estimation in the same study.
I deeply appreciate your time and effort, and everything you've don and are doing for this community.
I sincerely hope to hear from you!
Best wishes,
JU
________________________________________
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Tuesday, September 17, 2019 8:00 AM
To: Ju Lee <juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Subject: RE: Testing interaction term of categorical moderators in rma.mv
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 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)))
>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
[[alternative HTML version deleted]]
_______________________________________________
R-sig-meta-analysis mailing list
R-sig-meta-analysis using r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
[[alternative HTML version deleted]]
More information about the R-sig-meta-analysis
mailing list