Hi: In your example, you made the response zero in every case which is going to cause problems. In glm's, I think they call it the donsker effect. I'm not sure what it's called in OLS. probably a lack of identifiability. Note that you probably shouldn't be using zeros and 1's as the response in a regression anyway.
If you change the response to below, you get what you'd expect. y <- c(rep(0, 15), rep(1,15)) On Fri, Sep 27, 2019 at 1:48 PM David J. Birke <djbi...@berkeley.edu> wrote: > Dear R community, > > I just stumbled upon the following behavior in R version 3.6.0: > > set.seed(42) > y <- rep(0, 30) > x <- rbinom(30, 1, prob = 0.91) > # The following will not show any t-statistic or p-value > summary(lm(y~x)) > # The following will show t-statistic and p-value > summary(lm(1+y~x)) > > My expected output is that the first case should report t-statistic and > p-value. My intuition might be tricking me, but I think that a constant > shift of the data should be fully absorbed by the constant and not > affect inference about the slope. > > Is this a bug or is there a reason why there should be a discrepancy > between the two outputs? > > Best, > David > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
