Re: [R] Help Metafor

2011-02-21 Thread Viechtbauer Wolfgang (STAT)
Dear Mario,

If you could tell me how to define and interpret I^2 and H^2 for models with 
moderators, I may consider implementing these measures for such models. 
However, I am not aware of any work that has extended these measures to 
meta-regression models.

Best,

-- 
Wolfgang Viechtbauer
Department of Psychiatry and Neuropsychology
School for Mental Health and Neuroscience
Maastricht University, P.O. Box 616
6200 MD Maastricht, The Netherlands
Tel: +31 (43) 368-5248
Fax: +31 (43) 368-8689
Web: http://www.wvbauer.com


 -Original Message-
 From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
 On Behalf Of petre...@unina.it
 Sent: Sunday, February 20, 2011 13:59
 To: r-help@r-project.org
 Subject: [R] Help Metafor
 
 Dear Sir, I'm using the Metafor package;
 
 however, introducing moderators, I'm unable to obtain both I^2 (%
 of total variability due to heterogeneity) and H^2 (total variability
 / within-study variance). It is possible to obtain I^2 and H^2 also
 with moderators?
 
As example
 
_Dataset - sqlQuery(channel = 1, select * from [Foglio1$])
names(Dataset) - make.names(names(Dataset))
alloc.a - ifelse(dat$alloc == a, 1, 0)
alloc.b - ifelse(dat$alloc == b, 1, 0)
alloc.c - ifelse(dat$alloc == c, 1, 0)
 
res-rma.uni(yi, vi, mods=cbind(alloc.a, alloc.b, Year),
 measure=GEN, intercept=TRUE, data=dat, slab=Author, subset, add=1/2,
 to=only0, vtype=LS, method=REML, weighted=TRUE, knha=FALSE,
 btt=c(2,3))
 
 print (res)
 
Mixed-Effects Model (k = 22; tau^2 estimator: REML)
tau^2 (estimate of residual amount of heterogeneity): 0.7810 (SE = 0.2924)
tau (sqrt of the estimate of residual heterogeneity): 0.8838
 
Test for Residual Heterogeneity:
QE(df = 18) = 223.3159, p-val  .0001
 
Test of Moderators (coefficient(s) 2,3):
QM(df = 2) = 0.8251, p-val = 0.6620
 
Model Results:
 
  estimate   se zval    pval  ci.lb ci.ub
intrcpt  -135.3524  54.4487  -2.4859  0.0129  -242.0700  -28.6349  *
alloc.a    -0.3836   0.4529  -0.8469  0.3971    -1.2713    0.5042
alloc.b    -0.3656   0.5529  -0.6613  0.5084    -1.4493    0.7180
Year    0.0673   0.0273   2.4677  0.0136 0.0139    0.1208  *
 
Many thanks
 
 _
 
 Mario Petretta
 Dipartimento di Medicina Clinica Scienze Cardiovascolari e Immunologiche
 Facoltà di Medicina e Chirurgia
 Università di Napoli Federico II
 081 - 7462233

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[R] Help Metafor

2011-02-20 Thread petretta


   Dear Sir, I'm using the Metafor package;

   however, introducing moderators, I'm unable to obtain both I^2 (%  
of total variability due to heterogeneity) and H^2 (total variability  
/ within-study variance). It is possible to obtain I^2 and H^2 also  
with moderators?

   As example

   _Dataset - sqlQuery(channel = 1, select * from [Foglio1$])

   names(Dataset) - make.names(names(Dataset))

   alloc.a - ifelse(dat$alloc == a, 1, 0)

   alloc.b - ifelse(dat$alloc == b, 1, 0)

   alloc.c - ifelse(dat$alloc == c, 1, 0)

   res-rma.uni(yi, vi, mods=cbind(alloc.a, alloc.b, Year),  
measure=GEN, intercept=TRUE, data=dat, slab=Author, subset, add=1/2,  
to=only0, vtype=LS, method=REML, weighted=TRUE, knha=FALSE,  
btt=c(2,3))

    print (res)

   Mixed-Effects Model (k = 22; tau^2 estimator: REML)

   tau^2 (estimate of residual amount of heterogeneity): 0.7810 (SE = 0.2924)

   tau (sqrt of the estimate of residual heterogeneity): 0.8838

   Test for Residual Heterogeneity:

   QE(df = 18) = 223.3159, p-val  .0001

   Test of Moderators (coefficient(s) 2,3):

   QM(df = 2) = 0.8251, p-val = 0.6620

   Model Results:

     estimate   se zval    pval  ci.lb ci.ub  

   intrcpt  -135.3524  54.4487  -2.4859  0.0129  -242.0700  -28.6349  *

   alloc.a    -0.3836   0.4529  -0.8469  0.3971    -1.2713    0.5042  

   alloc.b    -0.3656   0.5529  -0.6613  0.5084    -1.4493    0.7180  

   Year    0.0673   0.0273   2.4677  0.0136 0.0139    0.1208  *

   Many thanks

   _

   Mario Petretta
Dipartimento di Medicina Clinica Scienze Cardiovascolari e Immunologiche
Facoltà di Medicina e Chirurgia
Università di Napoli Federico II
081 - 7462233



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