Hello list readers, I am running a set of GLMs on fish spp presence/absence as a function of various habitat characteristics. My response is binomial and I have four predictors, three of which are categorical.
So, R takes one of my predictor-variables away to use as the intercept (the first one alphabetically). However, I want to know the coefficient and SE of this predictor. I tried relevel() and reran the model. Abbreviated summary() results for each run are below. The results seem drastically different. Have I done the wrong thing? (Below is a result from the model with only one predictor, to save space and hassle.) Thanks, Ashley #Default reference level = HH: Estimate Std. Error z value Pr(>|z|) (Intercept) -5.2671 0.2781 -18.942 <2e-16 *** raw.table$SubsComboHS 0.8127 0.6438 1.262 0.207 raw.table$SubsComboSH -0.5736 1.0393 -0.552 0.581 raw.table$SubsComboSS -18.2990 923.6023 -0.020 0.984 #Command used to change reference level: > raw.table$SubsCombo<-relevel(raw.table$SubsCombo, ref="SS") #New reference level = SS: Estimate Std. Error z value Pr(>|z|) (Intercept) -23.57 923.60 -0.026 0.980 raw.table$SubsComboHH 18.30 923.60 0.020 0.984 raw.table$SubsComboHS 19.11 923.60 0.021 0.983 raw.table$SubsComboSH 17.73 923.60 0.019 0.985 -- View this message in context: http://r.789695.n4.nabble.com/Relevel-catagorical-variables-in-a-GLM-tp3558181p3558181.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.