Well, I have uploaded the data in the public folder of my dropbox. Due 
to data confidentiality, I haved to change the labels. To load the data:

con <- url( "http://dl.dropboxusercontent.com/u/101865137/datEx.rda" )
print(load(con))

# The replicate weights were created according to the jackknife (JK2) 
procedure in the same way as implemented in WesVar.
# According to 100 JK zones, 100 replicate weights result. The replicate 
weights are labelled "totwgtM_1" to "totwgtM_100"
# The regression I want to specify is achievement on group and origin. 
Both predictors are factors.

library(survey)
design   <- svrepdesign(data = datEx[, c("origin", "group", 
"achievement")], weights = datEx[ ,"pweight"],
         type="JKn", scale = 1, rscales = 1, repweights = 
datEx[,grep("^totwgtM_", colnames(datEx))], combined.weights = TRUE, mse 
= TRUE)

# This works
mod1     <- svyglm(formula = achievement ~ origin + group, design = 
design, return.replicates = FALSE, family = gaussian(link="identity"))

# I get the error message when specifying the interaction
mod2     <- svyglm(formula = achievement ~ origin * group, design = 
design, return.replicates = FALSE, family = gaussian(link="identity"))

# The output of the conventional glm() function reports singularities 
for one coefficient of the interaction
mod3     <- glm(formula = achievement ~ origin * group, data = datEx, 
family = gaussian(link = "identity"))

Thanks again,
Sebastian

-- 
Sebastian Weirich, Dipl.-Psych.

Institut zur Qualitätsentwicklung im Bildungswesen
Humboldt-Universität zu Berlin
Sitz: Hannoversche Straße 19, 10115 Berlin
Postadresse: Unter den Linden 6, 10099 Berlin

Tel: +49-(0)30-2093-46512

Am 02.05.2013 22:02, schrieb Thomas Lumley:
> On Fri, May 3, 2013 at 2:27 AM, Sebastian Weirich 
> <sebastian.weirich@iqb.hu-berlin.de 
> <mailto:sebastian.weirich@iqb.hu-berlin.de>> wrote:
>
>     Hello,
>
>     I want to specify a linear regression model in which the metric
>     outcome is predicted by two factors and their interaction. glm()
>     computes effects for each factor level and the levels of the
>     interaction. In the case of singularities glm() displays "NA" for
>     the corresponding coefficients. However, svyglm() aborts with an
>     error message. Is there a possibility that svyglm() provides
>     output for coefficients without singularities like glm()?
>
>
> It's not true that svyglm() aborts with an error message whenever 
> there are singularities, eg
>
> > svyglm(enroll~stype+I(stype),design=dclus1)
> 1 - level Cluster Sampling design
> With (15) clusters.
> svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
>
> Call:  svyglm(formula = enroll ~ stype + I(stype), design = dclus1)
>
> Coefficients:
> (Intercept)       stypeH       stypeM    I(stype)H  I(stype)M
>       432.9        697.4        464.9           NA         NA
>
> Degrees of Freedom: 182 Total (i.e. Null);  12 Residual
> Null Deviance:   24830000
> Residual Deviance: 15120000 AIC: 2599
>
>
> So, perhaps you could show us what you actually did, and what actually 
> happened, as the posting guidelines request.
>
>     -thomas
>
> -- 
> Thomas Lumley
> Professor of Biostatistics
> University of Auckland


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