[R] converting proc mixed to lme for a random effectsmeta-analysis

[Viechtbauer Wolfgang (STAT)]

That was going to be my suggestion =)

By the way, lme does not give you the right results because the residual variance is not constrained to 1 (and it is not possible to do so).

Best,

-- 
Wolfgang Viechtbauer 
 Department of Methodology and Statistics 
 University of Maastricht, The Netherlands 
 http://www.wvbauer.com/ 



-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Bernd Weiss
Sent: Tuesday, June 19, 2007 14:37
To: Lucia Costanzo
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] converting proc mixed to lme for a random effectsmeta-analysis


On 19 Jun 2007 at 8:13, Lucia Costanzo wrote:

Date sent:      	Tue, 19 Jun 2007 08:13:30 -0400
From:           	Lucia Costanzo <lcostanz at uoguelph.ca>
To:             	r-help at stat.math.ethz.ch
Subject:        	[R] converting proc mixed to lme for a random 
effects meta-analysis

> I would like to convert the following SAS code for a Random Effects 
> meta-analysis model for use in R but, I am running into difficulties.
> The results are not similar, R should be reporting 0.017 for the 
> between-study variance component, 0.478 for the estimated parameter
> and 
> 0.130 for the standard error of the estimated parameter.  I think it
> is 
> the weighting causing problems. Would anyone have any suggestions or
> tips?
> 
> Thank you,
> Lucia
> 
> *** R CODE ***
> studynum <-c(1, 2, 3, 4, 5)
> y <-c(0.284, 0.224, 0.360, 0.785, 0.492)
> w <-c(14.63, 17.02, 9.08, 33.03, 5.63)
> genData2 <-data.frame(cbind(studynum, y, w,v))
> 
> re.teo<-lme(y~1, data=genData2, random =~1, method="ML",
> weights=varFixed(~w))
> 
> 


What about using MiMa <http://www.wvbauer.com/downloads.html>? 

studynum <-c(1, 2, 3, 4, 5)
y <-c(0.284, 0.224, 0.360, 0.785, 0.492)
w <-c(14.63, 17.02, 9.08, 33.03, 5.63)
## without cbind(...)
genData2 <-data.frame(studynum, y, w)
mima(genData2$y, 1/genData2$w, mods = c(), method = "ML")


Some output:

- Estimate of (Residual) Heterogeneity: 0.0173

-         estimate     SE   zval  pval   CI_L   CI_U
intrcpt   0.4779 0.1304 3.6657 2e-04 0.2224 0.7334

Looks like what you are looking for...

HTH,

Bernd

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