Dear subscribers, I am looking for a function which would allow me to model the dependent variable as the number of successes in a series of Bernoulli trials. My data looks like this
ID TRIALS SUCCESSESS INDEP1 INDEP2 INDEP3 1 4444 0 0.273 0.055 0.156 2 98170 74 0.123 0.456 0.789 3 145486 30 0.124 0.235 0.007 4 147149 49 0.888 0.357 0.321 5 60585 11 0.484 0.235 0.235 6 198953 43 0.295 0.123 0.856 I want to find out how independent variables influence the number of successful trials (dependent variable) I had tried to use glm formula regression.glm <- glm( SUCCESSESS ~ INDEP1 + INDEP2 + INDEP3, data = data, family = binomial, weights= TRIALS) but got the following: Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1 but there y has to be between 0 and 1 (which does make sense, but that's why I am using weights?) I would be grateful for any hints/suggestions on how to proceed. Cheers, Juta -- View this message in context: http://r.789695.n4.nabble.com/binomial-logistic-regression-question-tp3846954p3846954.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.
