我正在使用metafor包进行元回归。我的简单试验估计是：
m1<-rma(yi=COEFF, sei=STDERR, mods = ~ MT_TIMESERIES + MT_BIVARIATE, method="REML", data=y)
接下来，我使用rma.mv（）估计相同的模型，并使用随机项RID，这是识别每个单一观察的因素（没有观察群集）：
m2<-rma.mv(yi=COEFF, V=STDERR^2, random= ~ 1|RID, mods = ~ MT_TIMESERIES + MT_BIVARIATE, method="REML", data=y)
估计m1和m2实际上应该产生相同的结果（这个想法得到包裹作者在http://www.metafor-project.org/doku.php/tips:rma.uni_vs_rma.mv上的注释支持）。
但事实上，他们没有：
> 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 for 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 for 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 *
有谁知道为什么会这样？
非常感谢提前！
祝你好运
勒夫