r - Meta-analysis with metafor package: Strange difference between rma and rma.mv -


i working on meta regression using metafor package. simple trial estimation is:

m1<-rma(yi=coeff, sei=stderr, mods = ~ mt_timeseries + mt_bivariate, method="reml", data=y) 

next estimate same model rma.mv() , use random term rid factor identifying each single observation (no clusters of observations):

m2<-rma.mv(yi=coeff, v=stderr^2, random= ~ 1|rid, mods = ~ mt_timeseries + mt_bivariate, method="reml", data=y) 

estimations m1 , m2 should yield same results (this idea supported note package author on http://www.metafor-project.org/doku.php/tips:rma.uni_vs_rma.mv).

but in fact, don't:

> summary(m1)  mixed-effects model (k = 886; tau^2 estimator: reml)  loglik    deviance         aic         bic        aicc   -4847.7988   9695.5976   9703.5976   9722.7309   9703.6431    tau^2 (estimated amount of residual heterogeneity):     0.0000 (se = 0.0000) tau (square root of estimated tau^2 value):             0.0007 i^2 (residual heterogeneity / unaccounted variability): 1.21% h^2 (unaccounted variability / sampling variability):   1.01 r^2 (amount of heterogeneity accounted for):            87.37%  test residual heterogeneity:  qe(df = 883) = 9083.3858, p-val < .0001  test of moderators (coefficient(s) 2,3):  qm(df = 2) = 104.7561, p-val < .0001  model results:                 estimate      se     zval    pval    ci.lb    ci.ub      intrcpt         -0.0076  0.0009  -8.6343  <.0001  -0.0093  -0.0059  *** mt_timeseries    0.0004  0.0010   0.3669  0.7137  -0.0016   0.0023      mt_bivariate     0.0062  0.0010   6.4595  <.0001   0.0043   0.0081  ***  > summary(m2)  multivariate meta-analysis model (k = 886; method: reml)  loglik    deviance         aic         bic        aicc   -2948.3789   5896.7578   5904.7578   5923.8911   5904.8034    variance components:               estim    sqrt  nlvls  fixed  factor sigma^2    3.2560  1.8044    886     no     rid  test residual heterogeneity:  qe(df = 883) = 9083.3858, p-val < .0001  test of moderators (coefficient(s) 2,3):  qm(df = 2) = 13.7838, p-val = 0.0010  model results:                 estimate      se     zval    pval    ci.lb    ci.ub      intrcpt         -0.5362  0.1262  -4.2475  <.0001  -0.7836  -0.2888  *** mt_timeseries   -0.5021  0.1557  -3.2237  0.0013  -0.8073  -0.1968   ** mt_bivariate    -0.5016  0.1949  -2.5742  0.0100  -0.8835  -0.1197    * 

does have idea why be?

many in advance!

best regards

joachim


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