> -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of Jerome Myers > Sent: Wednesday, January 18, 2012 7:27 AM > To: r-help@r-project.org > Subject: [R] confint function in MASS package for logistic regression > analysis > > I have the following binary data set: > Sex > Response 0 1 > 0 159 162 > 1 4 37 > My commands > library(MASS) > sib.glm=glm(sib~sex,family=binomial,data=sib.data) > summary(sib.glm) > The coefficients in the output are > Estimate Std. Error z value Pr(>|z|) > (Intercept) -3.6826 0.5062 -7.274 3.48e-13 *** > sex 2.2059 0.5380 4.100 4.13e-05 *** > I have calculated the .95 confidencce interval for sex two ways: > (1) confint(sib.glm) The result is > 2.5 % 97.5 % > (Intercept) -4.861153 -2.823206 > sex 1.263976 3.428764 > > Using the usual confidence interval formula, > (2) 2.2059 +/- 1.96*.538 = 1.15142. 3.26038 > The results from (2) are identical to those from SPSS but do not agree > with those from the confint function. > > I have reviewed the MASS pdf file and, seeing no solution there, > have tried to get the Venables & Ripley book from the local college > libraries but the only copies are out on loan. I suspect there is a > simple explanation of the discrepancy, perhaps a modification to > account > for pre-asymptotic distribution. Or perhaps I misunderstand the > application of the confint fuuction in the MASS package. If someone > knows the explanation, I'd appreciate it. > > -- > Jerome L. Myers >
Jerome, I suspect that the difference you are seeing is due to your "usual confidence interval formula" being based on asymptotic normal methods, while the confint.glm method from MASS uses profile likelihoods. Dan Daniel J. Nordlund Washington State Department of Social and Health Services Planning, Performance, and Accountability Research and Data Analysis Division Olympia, WA 98504-5204 ______________________________________________ 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.
