[R-meta] Different outputs by comparing random-effects model with a MLMA without intercept

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.