From: Gregor Gorjanc <gregor.gorjanc_at_gmail.com>

Date: Sat 11 Feb 2006 - 10:27:02 EST

# of obs: 8, groups: school, 8

# of obs: 8, groups: school, 8

Date: Sat 11 Feb 2006 - 10:27:02 EST

Hello!

I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible.

For example we can take schools data by Gelman from R2WinBUGS package. As you can see bellow use of weights argument did not had influence on results.

I do not know if my specification of weights i.e. 1 / sd^2 is ok. Under least squares one minimizes sum(e^2_i) or sum(w_i * e^2_i) with weighted LS. If I consider that \sigma_i represents uncertainty in my "data" then e'_i = e_i / \sigma_i and we minimize sum(e'^2_i) = sum((e_i / \sigma_i)^2) = sum(e_i * \sigma^-2_i). Therefore weights i.e. w_i are equal to 1 / \sigma^2_i.

Can anyone help me with this issue?

Thank you very much!

* > library("R2WinBUGS")
** > data(schools)
** > schools
** > attach(schools)
** >
** > ## Fit simple model without "weights"
** > lmer(estimate ~ 1 + (1 | school))
*

Linear mixed-effects model fit by REML

Formula: estimate ~ 1 + (1 | school)

AIC BIC logLik MLdeviance REMLdeviance 58.882 59.041 -27.441 59.278 54.882 Random effects: Groups Name Variance Std.Dev. school (Intercept) 80.4 8.97 Residual 30.1 5.49

# of obs: 8, groups: school, 8

Fixed effects:

Estimate Std. Error t value (Intercept) 8.82 3.72 2.37

* > ## Fit simple model with "weights"
** > lmer(estimate ~ 1 + (1 | school), weights = ~ 1 / (sd^2))
*

Linear mixed-effects model fit by REML

Formula: estimate ~ 1 + (1 | school)

AIC BIC logLik MLdeviance REMLdeviance 58.882 59.041 -27.441 59.278 54.882 Random effects: Groups Name Variance Std.Dev. school (Intercept) 80.4 8.97 Residual 30.1 5.49

# of obs: 8, groups: school, 8

Fixed effects:

Estimate Std. Error t value (Intercept) 8.82 3.72 2.37

-- Lep pozdrav / With regards, Gregor Gorjanc ---------------------------------------------------------------------- University of Ljubljana PhD student Biotechnical Faculty Zootechnical Department URI: http://www.bfro.uni-lj.si/MR/ggorjan Groblje 3 mail: gregor.gorjanc <at> bfro.uni-lj.si SI-1230 Domzale tel: +386 (0)1 72 17 861 Slovenia, Europe fax: +386 (0)1 72 17 888 ---------------------------------------------------------------------- "One must learn by doing the thing; for though you think you know it, you have no certainty until you try." Sophocles ~ 450 B.C. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Sat Feb 11 10:31:28 2006

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