Hello R folks, I have three questions. I am trying to run a logistic regression (binomial family) where the response variable is a proportion. According to R Documentation in "a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes." However when I run my code I get the following error message:
Error in model.frame.default(formula = PER_ELA ~ A_EX + COMM + ENG + S_R + : variable lengths differ (found for '(weights)') I'm not sure what I am doing wrong. My response variable is Y/M, which is the proportion of 1's (successes) among M binary responses. My prior weight is a variable indicating the number of trials for each observation. This is an abbreviated version of the code that I ran: glm1<-glm(PER_ELA~A_EX .... PER_LEA, family=binomial(link="logit"),data=data2,weights="REG") Question 1 and 2: Does the number of trials for each observation in my dataset have to be the same? What am I doing wrong here? Question 3: Is it OK for me to use percentages as predictor variables in a logistic regression? -- View this message in context: http://r.789695.n4.nabble.com/Specifying-Prior-Weights-in-a-GLM-tp3083480p3083480.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.
