The metafor Package

A Meta-Analysis Package for R

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analyses:gleser2009

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analyses:gleser2009 [2021/09/24 16:34] – Wolfgang Viechtbaueranalyses:gleser2009 [2022/08/03 16:58] (current) – Wolfgang Viechtbauer
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 Variance Components: none Variance Components: none
  
-Test for Residual Heterogeneity: +Test for Residual Heterogeneity:
 QE(df = 4) = 3.945, p-val = 0.414 QE(df = 4) = 3.945, p-val = 0.414
  
-Test of Moderators (coefficient(s) 1,2): +Test of Moderators (coefficient(s) 1,2):
 QM(df = 2) = 252.165, p-val < .001 QM(df = 2) = 252.165, p-val < .001
  
 Model Results: Model Results:
  
-              estimate     se    zval   pval  ci.lb  ci.ub     +              estimate     se    zval   pval  ci.lb  ci.ub
 factor(trt)1     2.374  0.150  15.804  <.001  2.080  2.669  *** factor(trt)1     2.374  0.150  15.804  <.001  2.080  2.669  ***
 factor(trt)2     1.570  0.189   8.330  <.001  1.201  1.940  *** factor(trt)2     1.570  0.189   8.330  <.001  1.201  1.940  ***
Line 315: Line 315:
 factor(trt)2      0.01244      0.03554 factor(trt)2      0.01244      0.03554
 </code> </code>
-The same results are given on page 367. +The same results are given on page 367.
  
 ==== Multiple-Endpoint Studies ==== ==== Multiple-Endpoint Studies ====
Line 336: Line 336:
 dat$yi <- round(with(dat, (m2i-m1i)/sdpi), 3) dat$yi <- round(with(dat, (m2i-m1i)/sdpi), 3)
 dat$vi <- round(with(dat, 1/n1i + 1/n2i + yi^2/(2*(n1i+n2i))), 4) dat$vi <- round(with(dat, 1/n1i + 1/n2i + yi^2/(2*(n1i+n2i))), 4)
-dat$covi <- round(with(dat, (1/n1i + 1/n2i) * ri + (rep(sapply(split(dat$yi, dat$school), prod), each=2) / (2*(n1i+n2i))) * ri^2), 4)+dat$covi <- round(with(dat, (1/n1i + 1/n2i) * ri +  
 +                            (rep(sapply(split(dat$yi, dat$school), prod), each=2) /  
 +                            (2*(n1i+n2i))) * ri^2), 4)
 </code> </code>
 The contents of the resulting dataset are: The contents of the resulting dataset are:
Line 359: Line 361:
 The variance-covariance matrix for the entire dataset can be constructed with: The variance-covariance matrix for the entire dataset can be constructed with:
 <code rsplus> <code rsplus>
-V <- bldiag(lapply(split(dat, dat$school), function(x) matrix(c(x$vi[1], x$covi[1], x$covi[2], x$vi[2]), nrow=2)))+V <- bldiag(lapply(split(dat, dat$school),  
 +            function(x) matrix(c(x$vi[1], x$covi[1], x$covi[2], x$vi[2]), nrow=2)))
 V V
 </code> </code>
Line 391: Line 394:
 Variance Components: none Variance Components: none
  
-Test for Residual Heterogeneity: +Test for Residual Heterogeneity:
 QE(df = 12) = 19.626, p-val = 0.074 QE(df = 12) = 19.626, p-val = 0.074
  
-Test of Moderators (coefficient(s) 1,2): +Test of Moderators (coefficient(s) 1,2):
 QM(df = 2) = 13.005, p-val = 0.001 QM(df = 2) = 13.005, p-val = 0.001
  
 Model Results: Model Results:
  
-                estimate     se   zval   pval  ci.lb  ci.ub     +                estimate     se   zval   pval  ci.lb  ci.ub
 outcomemath        0.362  0.100  3.603  <.001  0.165  0.558  *** outcomemath        0.362  0.100  3.603  <.001  0.165  0.558  ***
 outcomereading     0.205  0.099  2.062  0.039  0.010  0.400    * outcomereading     0.205  0.099  2.062  0.039  0.010  0.400    *
analyses/gleser2009.txt · Last modified: 2022/08/03 16:58 by Wolfgang Viechtbauer