You have a conceptual problem, as pointed out by previous helpers. You don't have a standard error for the first level of your categorical variable because that level's effect is not estimated. It is being used as a reference level against which the other levels of that categorical variable are being estimated (the default in R). This is one way by which statisticians include categorical predictors into the regression framework, originally meant for relations between continuous quantitative variables. You might want to read about regression, factors, and contrasts. This paper about the issue is available online: M.J. Davis, 2010. Contrast coding in multiple regression analysis: strengths, weaknesses and utility of popular coding structures. Journal of Data Science 8:61-73. HTH Ruben
-----Mensaje original----- De: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] En nombre de D_Tomas Enviado el: martes, 13 de marzo de 2012 14:39 Para: r-help@r-project.org Asunto: [R] Standard errors GLM Dear userRs, when applied the summary function to a glm fit (e.g Poisson) the parameter table provides the categorical variables assuming that the first level estimate (in alphabetical order) is 0. What is the standard error for that variable then? Are the standard errors calculated assuming a normal distribution? Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/Standard-errors-GLM-tp4469086p4469086.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
