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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