lm_mengxin, Yes, it happens because you are not following what Dr. Chongsuvivatwong suggested you in a previous email. Compare
> yourdata$age1 <- factor(yourdata$gender) > result1<-glm(y ~ gender + CD4,family = binomial, data=yourdata) > logistic.display(result1) and > fit <-glm(y ~ as.factor(gender), family = binomial, data=yourdata) > logistic.display(fit) with what you sent. And, please, include an appropriate subject line next time as requested in the Posting Guide. HTH, Jorge.- 2011/12/14 ©sªY <> > Sir: > I find out that for 2 level factor, if I set it to "factor", then I'll get > error reply. > For the instance last mail, if I use: > > result1<-glm(y ~ gender,family = binomial);#gender has 2 levels > logistic.display(result1) > > Error in coeff[, 1] : incorrect number of dimensions > > > if I use: > > result1<-glm(y ~ factor(age),family = binomial); #age has 3 levels > logistic.display(result1) > > It works well. > > So I have a suggestion that no matter the number of levels, if I set it to > factor, logistic.disply can all works well. > > > > > > logistic.display > > > At 2011-12-14 13:53:21,"Virasakdi Chongsuvivatwong" <> wrote: > > logistic.display need a rather tidy model ie all independent variables > must be the original name not any function of a variable in the dataset of > the model. > > If data1 is your dataset containing y, 'gender' and 'age', what you need > to do is > > > data1$age1 <- factor(data1$age) > > result1<-glm(y ~ age1 + gender + CD4,family = binomial, data=data1) > > logistic.display(result1) > > Note that 'gender', like 'y', is already dichotomized (0,1). There is no > need to 'factor' > > Virasakdi C > > > 2011/12/14 ©sªY <> > >> >> Hi sir: >> I follow your suggestion: >> >> result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >> logistic.display(result) >> >> Error in coeff[, 1] : incorrect number of dimensions >> >> >> >> >> >> >> At 2011-12-14 01:59:36,"Jorge I Velez" <> wrote: >> >> Hi there, >> >> Try >> >> require(epicalc) >> logistic.display(result) >> >> HTH, >> Jorge >> >> On Tue, Dec 13, 2011 at 7:16 AM, ©sªY <> wrote: >> >>> Hi all: >>> My data has 3 variables: >>> age(3levels : <30y=1 30-50y=2, >50y=3) >>> gender(Male=0, Female=1) >>> CD4 cell count(raw lab measurement) >>> y(1:death 0:alive) >>> >>> I perform logistic regression to find out the factors that influence y. >>> >>> result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >>> >>> >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of >>> Female, since Male is regarded as reference group and has no result).But >>> how can I compute the 95%CI of OR of gender? >>> >>> >>> Thanks a lot for your help. >>> >>> My best! >>> >>> >>> >>> >>> [[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. >>> >> >> >> >> >> -- >> >> -- NOTE: Prince of Songkla University will NEVER ask for your PSU >> Passport/Email Username or password by e-mail. If you receive such a >> message, please report it to report-ph...@psu.ac.th. >> ------------------------------ >> !!!! NEVER reply to any e-mail asking for your PSU Passport/Email or >> other personal details. !!!! >> ------------------------------ >> For more information, contact the PSU E-Mail Service by dialing 2121 >> >> >> > > > [[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.
