[R-meta] Different outputs by comparing random-effects model with a MLMA without intercept
Dear Rafael, I cannot even attempt an answer to that question without a full understanding of the problem and data that you are working with. Best, Wolfgang -----Original Message----- From: Rafael Rios [mailto:biorafaelrm at gmail.com] Sent: Sunday, 10 March, 2019 15:02 To: Viechtbauer, Wolfgang (SP) Cc: Michael Dewey; r-sig-meta-analysis at r-project.org Subject: Re: [R-meta] Different outputs by comparing random-effects model with a MLMA without intercept Thanks for the answers, Michael and Wolfgang. I suspected some effects of the random variables. Since I want to test whether the average effect size differs from zero in the data without a potential_sce bias (subgroup "no"), which of the two approaches do you recommend? Best wishes, Rafael. Em dom, 10 de mar de 2019 10:40, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> escreveu: Dear Rafael, Let's try this again (instead of sending an empty mail -- sorry about that!). Indeed, the results differ because model2 estimates the variance components only based on the subset, while model1 estimates those variances based on all data. You would have to allow the variance components to differ for the "no" and "yes" levels of 'potential_sce' in 'model1' for the results to be identical. Actually, even then, I don't think you would get the exact same results, since you make use of the 'R' argument. Due to the correlation across species, the estimate (and SE) of 'potential_sceno' and 'potential_sceno' will be influenced by whatever species are included in the dataset. In the subset, certain species are not included (240 instead of 348), which is another reason why there are differences. Best, Wolfgang -----Original Message----- From: Michael Dewey [mailto:lists at dewey.myzen.co.uk] Sent: Thursday, 07 March, 2019 18:06 To: Rafael Rios; Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org Subject: Re: [R-meta] Different outputs by comparing random-effects model with a MLMA without intercept Dear Rafael I think this may be related to the issue outlined by Wolfgang in this section of the web-site http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates Michael
On 07/03/2019 16:46, Rafael Rios wrote:
Dear Wolfgang and All, I am conducting a meta-analysis to evaluate potential bias of a fixed predictor with two subgroups (predictor: yes and no). Because I found a bias, I removed the values of subgroup "yes" and performed a random-effects model. However, when I compared the output of the first model without intercept with the output of the random effects model, I obtained different results, especially in the estimation of confidence intervals. I was expecting to found similar results because the model without intercept tests if the average outcome differs from zero. Can you explain in which case this can happen? Every help is welcome. model1=rma.mv(yi, vi, mods=~predictor-1, random = list (~1|effectsizeID, ~1|studyID, ~1|speciesID), R=list(speciesID=phylogenetic_correlation), data=h) #Multivariate Meta-Analysis Model (k = 1850; method: REML) # #Variance Components: #? ? ? ? ? ? ? ? ? ? estim? ? ?sqrt? ? ? nlvls? fixed? ? ? ? factor? ? ?R #sigma^2.1? 0.0145? 0.1204? ?1850? ? no? effectsizeID? ?no #sigma^2.2? 0.0195? 0.1397? ? 468? ? ?no? ? ? ?studyID? ? ?no #sigma^2.3? 0.2386? 0.4885? ? 348? ? ?no? ? ?speciesID? ?yes # #Test for Residual Heterogeneity: #QE(df = 1848) = 10797.5993, p-val < .0001 # #Test of Moderators (coefficients 1:2): #QM(df = 2) = 17.6736, p-val = 0.0001 # *#Model Results:* *#? ? ? ? ? ? ? ? ? ? ? ? ? estimate? ? ? se? ? ? ? zval? ? ? ?pval ci.lb <http://ci.lb>? ?ci.ub * *#potential_sceno? ? ?0.2843? 0.1659? 1.7141? 0.0865? -0.0408? 0.6095? *. #potential_sceyes? ? 0.3741? 0.1668? 2.2421? 0.0250? ?0.0471? 0.7011? * #--- #Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 model2=rma.mv(zf, vzf, random = list (~1|effectsizeID, ~1|studyID, ~1|speciesID), R=list(speciesID=phylogenetic_correlation), data=subset(h,potential_sce=="no")) #Multivariate Meta-Analysis Model (k = 1072; method: REML) # #Variance Components: #? ? ? ? ? ? estim? ? sqrt? nlvls? fixed? ? ? ? factor? ? R #sigma^2.1? 0.0140? 0.1184? ?1072? ? ?no? effectsizeID? ?no #sigma^2.2? 0.0394? 0.1986? ? 264? ? ?no? ? ? ?studyID? ?no #sigma^2.3? 0.0377? 0.1943? ? 240? ? ?no? ? ?speciesID? yes # #Test for Heterogeneity: #Q(df = 1071) = 4834.5911, p-val < .0001 # *#Model Results:* *#estimate? ? ? se? ? zval? ? pval? ?ci.lb <http://ci.lb>? ?ci.ub * *#? 0.2989? 0.0720? 4.1494? <.0001? 0.1577? 0.4401? *** * #--- #Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 I used another data set to conduct the same approach and obtained similar results: dat <- dat.bangertdrowns2004 rbind(head(dat, 10), tail(dat, 10)) dat <- dat[!apply(dat[,c("length", "wic", "feedback", "info", "pers", "imag", "meta")], 1, anyNA),] head(dat) random.model=rma.mv(yi, vi, random=list(~1|id, ~1|author), structure="UN", data=subset(dat, subject=="Math")) random.model *#Math* *#Model Results:* *#? estimate? ? ? se? ? zval? ? ? ? ?pval? ?ci.lb <http://ci.lb>? ?ci.ub * *#? ? 0.2106? 0.0705? 2.9899? 0.0028? 0.0726? 0.3487? *** mixed.model=rma.mv(yi, vi, mods=~subject-1, random=list(~1|id, ~1|author), structure="UN", data=dat) anova(mixed.model,btt=2) *#Math* *#? estimate? ? ? se? ? ? ?zval? ? pval? ? ? ?ci.lb <http://ci.lb>? ?ci.ub* *#? ? 0.2100? 0.0697? ?3.0122? 0.0026? ?0.0734? 0.3467* Best wishes, Rafael.
__________________________________________________________ Dr. Rafael Rios Moura *scientia amabilis* Behavioral Ecologist, Ph.D. Postdoctoral Researcher Universidade Estadual de Campinas (UNICAMP) Campinas, S?o Paulo, Brazil ORCID: http://orcid.org/0000-0002-7911-4734 Curr?culo Lattes: http://lattes.cnpq.br/4264357546465157 Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2