[R-meta] Effect coding in metafor
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Wed Feb 28 22:33:16 CET 2018
How is the main effect of relevance when there is an interaction?
But ok, leaving this aside, yes, the coefficient for 'testeraware' in model 'res.i1' is indeed the simple effect of the 'tester' factor for level 'none' of the 'weeks' factor.
So you want something different. Can you describe in words what this main effect you are looking for is supposed to reflect?
Best,
Wolfgang
>-----Original Message-----
>From: Yogev Kivity [mailto:yogev_k at yahoo.com]
>Sent: Wednesday, 28 February, 2018 22:14
>To: r-sig-meta-analysis at r-project.org; Viechtbauer Wolfgang (SP)
>Subject: Re: RE: RE: [R-meta] Effect coding in metafor
>
>Thanks!
>
>Sorry - I guess I should have given an example from a model with an
>interaction.
>
>And what if there is an interaction (model 'res.i1') and I am interested
>in the main effect? would I then need to change the coding?
>
>Best,
>Yogev
>
>--
>Yogev Kivity, Ph.D.
>Postdoctoral Fellow
>Department of Psychology
>The Pennsylvania State University
>Bruce V. Moore Building
>University Park, PA 16802
>Office Phone: (814) 867-2330
>
>On Wednesday, February 28, 2018, 3:21:39 PM EST, Viechtbauer Wolfgang
>(SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
>
>Yes, b3 = -.0511 is the main effect of the 'tester' factor. Since there
>is no interaction, the effect is the same for all three levels of the
>'weeks' factor, so it is not just the simple effect for the 'none' level,
>but also for the 'some' and 'high' levels. Look at the line plot again --
>it does not matter at which level of 'weeks' you look, the b3 = -.0511
>effect is always the same.
>
>Best,
>Wolfgang
>
>>-----Original Message-----
>>From: Yogev Kivity [mailto:yogev_k at yahoo.com]
>>Sent: Wednesday, 28 February, 2018 18:08
>>To: r-sig-meta-analysis at r-project.org; Viechtbauer Wolfgang (SP)
>>Subject: Re: RE: [R-meta] Effect coding in metafor
>>
>>Dear Wolfgang,
>>
>>Thanks for the link, this is very helpful in general!
>>However, I am still a bit confused about how to obtain estimates of a
>>main effect. In model ‘res.a1’ in the example in the link, would you
>>interpret the estimate of the ‘testeraware’ coefficient (b3 = -.0511) as
>>a test of the main effect of the ‘tester’ factor? Given that the ‘weeks’
>>factor is dummy coded, I thought that ‘testeraware’ represents the
>simple
>>effect of ‘tester’ within the ‘none’ level. If the latter is correct,
>>than I am looking for a way to test for a main effect (in this example,
>>the effect of ‘tester’ across the levels of ‘weeks’).
>>
>>Best,
>>Yogev
>>
>>--
>>Yogev Kivity, Ph.D.
>>Postdoctoral Fellow
>>Department of Psychology
>>The Pennsylvania State University
>>Bruce V. Moore Building
>>University Park, PA 16802
>>Office Phone: (814) 867-2330
>>
>>On Monday, February 26, 2018, 9:08:23 AM EST, Viechtbauer Wolfgang (SP)
>><wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
>>
>>Dear Yogev,
>>
>>Here is an extensive discussion of an example with 2 categorical
>>moderators and it happens to be the case that it also involves one
>>dichotomous and one 3 level factor:
>>
>>http://www.metafor-
>>project.org/doku.php/tips:multiple_factors_interactions
>>
>>I don't see why you need to change the coding.
>>
>>Best,
>>Wolfgang
>>
>>>-----Original Message-----
>>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>>>project.org] On Behalf Of Yogev Kivity
>>>Sent: Monday, 26 February, 2018 4:38
>>>To: r-sig-meta-analysis at r-project.org
>>>Subject: [R-meta] Effect coding in metafor
>>
>>>
>>>Hi all,
>>>I am running a meta-analysis with 2 categorical moderators, one is
>>>dichotomous and the other has 3 levels (e.g. x1 and x2). For example:
>>>rma.mv(yi, V, mods=~factor(x1)*factor(x2), random = ~ 1 |
>>>StudyID/GroupID/EffectSizeID, data=DataF)
>>>(the specifics of the design of the meta-analysis seemed irrelevant to
>>>me, but if I am mistaken, I am happy to provide further details)
>>>As a first step, I am interested in the main effects of both moderators
>>>as well as the interaction effect. Therefore, to my understanding, I
>>need
>>>both moderators to be set as effect coding factors, rather then dummy
>>>coding factors. However, I could not figure out a way to do it in
>>>metafor. For example, setting the following contrasts did not work, and
>>>essentially the output remained identical to that of the dummy
>variables
>>>(i.e., each estimate expresses the simple effect of the moderator
>within
>>>the reference category of the other moderator):
>>>contrasts(Data$x1)<-c(-.5,.5)
>>>
>>>Obviously, I can get around the problem by creating multiple numeric
>>>variables with weights that correspond to the desired effect coding,
>but
>>>I was wondering whether I am missing something and whether there is a
>>>more elegant way of doing it.
>>>Best,Yogev
>>>--
>>>
>>>Yogev Kivity, Ph.D.
>>>Postdoctoral Fellow
>>>Department of Psychology
>>>The Pennsylvania State University
>>>Bruce V. Moore Building
>>>University Park, PA 16802
>>>Office Phone: (814) 867-2330
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