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.weir...@iqb.hu-berlin.de > <mailto:sebastian.weir...@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 [[alternative HTML version deleted]]
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