__________________________________________________________
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
Em seg, 11 de mar de 2019 ?s 05:57, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> escreveu:
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