[R-meta] Multiple comparisons / Tukey test multilevel meta analysis
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Tue Oct 17 14:25:22 CEST 2017
Hello Anne,
Have you seen/worked your way through this?
http://www.metafor-project.org/doku.php/tips:testing_factors_lincoms
It shows how to do all pairwise comparisons (contrasts) between factor levels using glht().
Best,
Wolfgang
--
Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Kranzbuhler, Anne
Sent: Tuesday, 17 October, 2017 11:33
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Multiple comparisons / Tukey test multilevel meta analysis
Hi,
I am doing a multilevel meta analysis with the metafor package (I am new to all this, so please excuse me if this is a stupid question). I want to run a moderator analysis with one categorical moderator that has 15 levels. Is there any way that I could run some sort of post-hoc (Tukey) test to determine which of the levels do or do not differ?
The output of the model is as follows (I now want to know whether the different levels of the variable "emo" differ. Trying the glht() command always produces an error - see below).
Is there anyone who could help me with this?
mods_emos <- rma.mv(EF_FisherZ, variance_FisherZ, W= weight_FisherZ, mods= ~ emo, random = list(~ 1 | Study, ~ 1 | Effectsizecount), data=data_neg, method='ML')
summary(mods_emos, digits=3)
Multivariate Meta-Analysis Model (k = 820; method: ML)
logLik Deviance AIC BIC AICc
-167.024 3189.101 368.048 448.106 368.811
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.038 0.195 85 no Study
sigma^2.2 0.063 0.250 820 no Effectsizecount
Test for Residual Heterogeneity:
QE(df = 805) = 18413.355, p-val < .001
Test of Moderators (coefficient(s) 2:15):
QM(df = 14) = 35.150, p-val = 0.001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -0.286 0.069 -4.129 <.001 -0.422 -0.150 ***
emo2 0.036 0.125 0.292 0.770 -0.208 0.281
emo3 0.032 0.124 0.255 0.798 -0.212 0.275
emo4 0.273 0.120 2.282 0.023 0.039 0.508 *
emo5 0.140 0.080 1.751 0.080 -0.017 0.297 .
emo6 0.234 0.099 2.357 0.018 0.040 0.429 *
emo7 0.210 0.163 1.290 0.197 -0.109 0.529
emo12 0.364 0.105 3.461 <.001 0.158 0.571 ***
emo13 -0.090 0.114 -0.784 0.433 -0.313 0.134
emo14 0.032 0.112 0.289 0.773 -0.187 0.252
emo15 0.125 0.124 1.012 0.312 -0.117 0.368
emo17 0.148 0.142 1.044 0.297 -0.130 0.425
emo18 -0.101 0.128 -0.788 0.431 -0.353 0.150
emo19 0.104 0.141 0.741 0.458 -0.172 0.381
emo21 0.260 0.189 1.372 0.170 -0.111 0.631
glht(mods_emos, linfct = mcp(emo = "Tukey"))
Error in formula.default(object, env = baseenv()) : invalid formula
Error in factor_contrasts(model) :
no 'model.frame' method for 'model' found!
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