similar to: Need advice about models with ordinal input variables

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

I have a student that I'm encouraging to use R rather than SAS or Stata and within just 2 weeks he has come up with a question that stumps me. What does a person do about endogeneity in generalized linear models? Suppose Y1 and Y2 are 5 category ordinal dependent variables. I see that MASS has polr for estimation of models like that, as long as they are independent. But what if the

Dear R specialists, I have a multivariate statistics question that I want to submit to the R community (which conveys a very good statistical knowledge). I need to perform an optimal scaling based on a discrete variable and an ordinal variable. The discrete variable, Area, defines a geographical area. The ordinal variable, EducationLevel, describes the education level of individuals (the

When I run "lrm" from the Design package, I get a warning about contrasts when I include an ordinal variable: Warning message: Variable ordfac is an ordered factor. You should set options(contrasts=c("contr.treatment","contr.treatment")) or Design will not work properly. in: Design(eval(m, sys.parent())) I don't get this message if I use glm with

Good afternoon, gentlemen! After several days studying and researching on categorical data (various forums with answers from the owner of the library - all incipient) how to interpret the output the function MCMCglmm, come to enlist the help of you, if someone has already worked with MCMCglmm function in the case of variables ordinal dependent. I've read and reread all the pdf's of the

Hello I've been asked to help evaluate a vegetation data set, specifically to examine it for community similarity. The initial problem I see is that the data is ordinal. At best this only captures a relative ranking of abundance and ordinal ranks are assigned after data collection. I've been trying to find a procedure in R that can handle ordinal based classification and so far have

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

Hi all, I have site data with plant species cover and am looking for trends. I'm kind of new to this, but have done lots of reading and can't find an answer. I tried decorana (I know it's been replaced by ca.) and see a trend, but I'm not sure what it means. Is there a way to get the loadings/eigenvectors of the axes (like in PCA)? Is there a way to do this with rda() too? How

Dear R users: I assigned students to make some graphs and I'm having trouble answering some questions that they have. We are all working on R 2.1 on Fedora Core Linux 4 systems. 1. In the plot, the axis is not labeled by "numbers", but rather scientific notation like "-2e+08" or such. We realize that means -200,000,000. We want to beautify the plot. We would rather

Running R 1.1 on RedHat Linux 6.2. I need to write a shell script that goes through a bunch of directories of simulation output, creating summary files that have the mean and standard deviation of the variables found in the data files in each directory. I've got the R code doing almost the right thing. It reads in data, then gets the mean and standard deviation for the numeric variables,

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

I have a consulting client with data containing a mixture of continuous, nominal, and ordinal variables, who wishes to do some correlational analyses. Ignoring for the moment whether this is wise or useful, is there some way to calculate a matrix using, for example, polychoric correlations for pairs of nominal variables, or correlations based on normal scores for ordinal variables? [The PRELIS

Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,

I have a linux system with Fedora Core 2 and R-2.0. I was comparing plots made with plot() and dotplot() and discovered a problem. Although the dots are positioned correctly, the numerical labels in the dotplot y axis are not correct. I put copies here: http://lark.cc.ku.edu/~pauljohn/R/plotTrouble1.jpg That is the "correct" one from plot, with the higest value on y showing at 18.

I've put all ``current'' add-on packages into CRAN's source/contrib tree and created an INDEX file (attached below). As you can see, currently we have acepack bootstrap ctest date e1071 fracdiff gee jpn snns splines survival4 (Yes, e1071 and jpn are new ... more on the latter in a later mail.) In the near future, I am hoping for the following: oz (Bill

Good day: I'm administering 6 linux systems (FC4) in a student lab and worry that users may want packages that are not installed. I get tired of adding them one by one. Then I happened upon this page http://support.stat.ucla.edu/view.php?supportid=30 about installing all R packages from CRAN. That did not run as it was, but after some fiddling I arrived at the following script, which

To whom should bug reports of the R port of acepack be directed? On a SPARC/Solaris 2.6 or 2.7 (SunOS 5.6 or 5.7) system running R-1.0.0 the avas example fails > library(acepack) > example(avas) avas> TWOPI <- 8 * atan(1) avas> x <- runif(200, 0, TWOPI) avas> y <- exp(sin(x) + rnorm(200)/2) avas> a <- avas(x, y) Process R bus error (core dumped) at Mon

This is a follow up to the message I posted 3 days ago about how to estimate mixed ordinal logit models. I hope you don't mind that I am just pasting in the code and comments from an R file for your feedback. Actual estimates are at the end of the post. ### Subject: mixed ordinal logit via "augmented" data setup. ### I've been interested in estimating an ordinal logit model

Today I wanted to experiment with different distributions an to see the hazard rates they imply. So I eventually ended up with this, which uses the hist object's handy $density and the cumsum function in R: x <- c(rweibull(21000,0.5,0.7)) #create "breaks" vector to go into histogram #need last break bigger than max(x) y <- seq(0,max(x)+2) histx <-

Hi! I'm looking for a solution to analyse data, which consists of dichotomous responses (yes/no) for 2 multinomial ordinal variables. I was trying glm() and got hierarhical models treating all variables as nominal, but I can't figure out how to tell glm() to use a model for ordinal data like this: log(Mij) = intercept + X + Y + Z + beta*(x-x')*(y-y') where beta is a