Hi, Not sure how your data looks like. With the sample data below, the code works.
Try this: set.seed(1) dat1<-data.frame(MIGRATION=sample(c(0,1),100,replace=TRUE),distance=sample(40:80,100,replace=TRUE)) RR.rebuild<-glm(MIGRATION~distance,data=dat1,subset=!(1:100 %in% c(56,23,20,9,19)),family=binomial(link="logit")) RR.rebuild #Call: glm(formula = MIGRATION ~ distance, family = binomial(link = "logit"), # data = dat1, subset = !(1:100 %in% c(56, 23, 20, 9, 19))) #Coefficients: #(Intercept) distance #-0.0781611 -0.0004483 #Degrees of Freedom: 94 Total (i.e. Null); 93 Residual #Null Deviance: 131.4 #Residual Deviance: 131.4 AIC: 135.4 A.K. ----- Original Message ----- From: Marcus Tullius <tull...@europe.com> To: r-help@r-project.org Cc: Sent: Wednesday, September 5, 2012 3:42 PM Subject: [R] Outliers in Binary Logistic Regressions Hallo folks, I know I should not ask the same question again. But I have a problem I cannot solve and without the solution I am stuck and lost, unable to get along with my work! Someone suggested I should try the code below in order to eliminate the outliers from my data. I did as I was told, but I got a negative reply. The code did not function. I am including it here so that, if possible, someone may correct it for me. That would really be very much appreciated! My data has 1439 rows. *RR.rebuild <- glm(RR, subset=remove) glm(RR, subset=!(1:1439 %in% c(56,303,365,391,512,746,859,940,1037,1042,1138,1355)) influence(RR.rebuild) influence.measures(RR.rebuild)* Many thanks in advance for any help and sorry for being annoyingly persistent! Francisco [[alternative HTML version deleted]] ______________________________________________ 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.
