[R-meta] Multiple comparisons / Tukey test multilevel meta analysis

[Viechtbauer Wolfgang (SP)]

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!