Hi there,
I take advantage of this chat to ask other question related to logistic regression. This is my first time as well. I have data that I want to model but Im not sure if glm() is the correct function to use. My problem is as follow, I used Oxford Instability Score of the shoulder (OIS, independent variable) as indicative of the outcome ( 12-20 best, to 41-60 worst outcome, 5 possible results). Looking for many independent variables like categorical and numerical I want to see their prognostic impact on the outcome. There is a function which I can use to model my problem? I heard about multinomial logistic regression but I did not able to find nothing related to it. Any help would be much appreciated. Marlene. 2009/7/31 G. Jay Kerns <gke...@ysu.edu> > Dear Carlos, > > On Thu, Jul 30, 2009 at 6:11 PM, Carlos López<nato...@fisica.unam.mx> > wrote: > > Hello everybody :-) > > > > I have some data that I want to model with a logistic regression, most of > > the independent variables are numeric and the only dependent is > categorical, > > I was thinking that I could apply a logistic regression using glm but I > > wanted to deepen my knowledge of this so I tried to do some reading and > > found the "iris" dataset, now I would like to ask two things, first if > you > > know of any bibliography to read more about the logistic regression and R > so > > I could understand and interpret better the output, > > > See the following > > https://home.comcast.net/~lthompson221/<https://home.comcast.net/%7Elthompson221/> > > and the following specific link on that page: > > https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf<https://home.comcast.net/%7Elthompson221/Splusdiscrete2.pdf> > > which is a manual to accompany Agresti's _Categorical Data Analysis_. > In particular, you may want to check out Chapter 5 (and also some of > 4). > > > >and second, what could I > > do when I have some independent variables that are not only numerical but > > categorical too, i.e. mixed (categorical and numerical), can I still use > a > > logistic regression? > > Easy peasy, lemon squeezy. See page 78. > > Hope this helps, > Jay > > > > > > > > > > > > > *************************************************** > G. Jay Kerns, Ph.D. > Associate Professor > Department of Mathematics & Statistics > Youngstown State University > Youngstown, OH 44555-0002 USA > Office: 1035 Cushwa Hall > Phone: (330) 941-3310 Office (voice mail) > -3302 Department > -3170 FAX > VoIP: gjke...@ekiga.net > E-mail: gke...@ysu.edu > http://www.cc.ysu.edu/~gjkerns/ <http://www.cc.ysu.edu/%7Egjkerns/> > > ______________________________________________ > 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. > [[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.
