Re: [R] Differences between SPSS and R on probit analysis

Hi Bianca, I hope you’ve solved your problem with SPSS and R probit analysis, but if you haven’t, I have your solution: Based on the output you’ve given, I see that your residual deviance is under-dispersed (that the ratio of residual deviance to residual deviance df does is less than 1). How

Re: [R] Differences between SPSS and R on probit analysis

24 de febrero de 2017 14:29 Para: Biank M Cc: r-help@r-project.org Asunto: Re: [R] Differences between SPSS and R on probit analysis Another model specification equivalent to cbind(afflicted, total-afflicted) ~ ... is the ratio you had accompanied by the total as the 'weights

Re: [R] Differences between SPSS and R on probit analysis

Another model specification equivalent to cbind(afflicted, total-afflicted) ~ ... is the ratio you had accompanied by the total as the 'weights' argument afflicted/total ~ ..., weights=total Bill Dunlap TIBCO Software wdunlap tibco.com On Fri, Feb 24, 2017 at 12:01 PM, William Dunlap wro

Re: [R] Differences between SPSS and R on probit analysis

Did you not get a warning from glm, such as the following one? > fm1 <- glm(affected/total ~ log(dose), family=binomial(link = probit), > data=finney71[finney71$dose != 0, ]) Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! Do not ignore warnings. The left