similar to: glm binomial logit

I would like to know how R computes the probability of an event in a logistic model (P(y=1)) from the score s, linear combination of x and beta. I noticed that there are differences (small, less than e-16) between the fitting values automatically computed in the glm procedure by R, and the values "manually" computed by me applying the reverse formula p=e^s/(1+e^s); moreover I noticed

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,

Hi, I am playing with clogit(case~spontaneous+induced+strata(stratum),data=infert) from clogit help file. This line works. 1. But, why strata(stratum) doesn't have a coefficient like spontaneous and induced? 2. When I remove strata(stratum) from the command, this function seems to keep running forever. Why? 3. I think the equation for clogit looks like P=1/(1+

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

Dear R Helpers, First, I apologize for asking for help on the first of my topics. I have been looking at the posts and pages for apply, tapply etc, and I know that the solution to this must be ridiculously easy, but I just can't seem to get my brain around it. If I want to produce a set of tables for all the variables in my data, how can I do that without having to type them into the table

--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

Dear R-help, I'm doing conditional logit regression for a discrete choice model. I want to know whether there's a way to get the fitted probabilities. In Stata, "predict" works for clogit, but it seems that in R "predict" does not. Thank you very much! Best wishes. Sincerely, Min -- Min Chen Graduate Student Department of Agricultural,

I am encountering a make error on an AIX 5.2 with R-2.3.0 and R-patched_2006-05-10 after successfully configuring them using gcc-4.1.0, gnu make, and the following configure options CC=/usr/local/bin/gcc F77=/usr/local/bin/gfortran CXX=/usr/local/bin/g++ MAIN_LDFLAGS=-Wl,-brtl SHLIB_LDFLAGS=-Wl,-G CFLAGS='-g -O' FFLAGS='-O' CXXFLAGS='-g -O' bash-3.00$ ./configure

I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many

Hi R-users, I have the following problem with CrossTable function within ?gmodels? package: the output of the function (format ?spss? and asresid=T) can not be stored within another object. For example: >library(gmodels) >data(infert, package = "datasets") > CrossTable(infert$education, infert$induced)->aa # the function prints everything ok on the screen > aa # works

After loading the RODBC package version 1.1-7, Ctrl-C changes its behaviour and is quitting R and returning to the (unix-)command prompt on the solaris2.8 platform here. Here's what happened before and after loading RODBC > for (i in 1:10^5) rnorm(10) ^C > library(RODBC) > for (i in 1:10^5) rnorm(10) ^C bash-3.00$ platform sparc-sun-solaris2.8 arch

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Using AIX 5.3 and gcc-4.2.1 for building R-2.7.1 with the following configure flags OBJECT_MODE=64 CC="gcc -maix64" CXX="g++ -maix64" F77="gfortran -maix64" FC="gfortran -maix64" CFLAGS="-g -O3" CXXFLAGS="-g -O3" FFLAGS="-g -O3" FCFLAGS="-g -O3" LDFLAGS="-L/$HOME/usr/local/lib/ppc64

I am trying to get R-2.2.1 to pass make check on an AIX 5.2 at work (I know R-2.2.1 is not the latest release, but I encountered make errors in my attempts to install either R-2.3.0 or the R-patched_2006-05-10 on the version of AIX I am on. I will post those errors in a separate posting from this.) So far, configure and make finish without error for R-2.2.1 using gnu make, gcc-4.1.0, and

Per the request to test the latest tarball referenced below, I have built R on AIX 5.3. There is a memory issue, please see 3) below. 1) Build with --enable-BLAS-shlib option. Builds and passes "make check". 2) GNU libiconv was installed; R configured *without* the --without- iconv option. Builds and passes "make check." 3) Memory issue: a)

Hello to all, I recently downloaded R to my PC and am enjoying getting acquainted with it. Thank you to everyone involved in the R-project! I am interested in doing a log-linear analysis with R on a data set with dichotomous variables. There are 11 variables (columns) and around 1000 subjects (rows). How do I aggregate my data, i.e. how do I make a new dataset that includes the variable giving

Hello all, I was wondering if the following behaviour of margin.table in R 1.3.0 is normal. > mm <- data.frame(x=c(0,0,1), y=c(1,1,0), z=c(0,1,0)) > margin.table(mm,1) [1] 1 2 3 <0 rows> (or 0-length row.names) while using 'apply(x,margin,sum)' I get > apply(mm,1,sum) 1 2 3 1 2 1 which appears to be normal. Similar behaviour when margin=2. On the other hand, the

i have a data frame(dat) which has many variables.and i use the following script to get the crosstable. >danx2<-c("x1.1","x1.2","x1.3","x1.4","x1.5","x2","x4","x5","x6","x7","x8.1","x8.2","x8.3","x8.4","x11",

I asked a question that might help me track down what changed between 2.3.1 that did build and the 2.4.0 version that will not build. Some of the undefined names the linker was complaining about looked like #define symbols that were not picked up by configure. The first thing I was hoping for was to find out if this problem looked familiar? Whether or not, I'm willing to try to work on

I need help with (hopefully) just one more thing. I have been fussing with this for quite some time and have decided just to give up and ask! I want to match a column in a matrix to a vector. I found a "which" command that I thought would be helpful as it does the following: > g=c(1,5,3,2,7) > which(g==5) [1] 2 As the above gave which placement in the g vector corresponded to