similar to: non-parametric repeated measures anova using Proportional Odds Model - examples?!

Hello Prof. Harrell and dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

Hi I have data from an experiment that used a repeated-measures factorial 2x2 design (i.e. each participant contributed data to both levels of both factors). I need a non-parametric version of the repeated-measures factorial ANOVA to analyse the data. SPSS only has non-parametric tests for one-way ANOVAs but I have been told that the test I need can be implemented using the R software.

Hello dear R help group, I have the following setup to analyse: We have about 150 subjects, and for each subject we performed a pair of tests (under different conditions) 18 times. The 18 different conditions of the test are complementary, in such a way so that if we where to average over the tests (for each subject), we would get no correlation between the tests (between subjects). What we wish

I have a data set w/ an ordinal response taking on one of 10 categories. I am considering using polr to fit a cumulative logits model. I previously fit the model in SAS (using proc logistic) which provides a test for the proportional odds assumption (p < 0.001 for the test). Are there simple diagnostic plots that can be used to look at the validity of this assumption and possibly help w/

Simple example: # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start, method="class", data=kyphosis) Now I would like to know how can I measure the "importance" of each of my three explanatory variables (Age, Number, Start) in the model? If this was a regression model, I could have looked at p values from the

Some attribute x from 17 individuals was recorded repeatedly on 6 time points using a Likert scale with 7 distractors. Which statistical test(s) can I apply to check whether the changes along the 6 time points were significant? set.seed( 123 ) x <- matrix( sample( 1:7, 17*6, repl=T ), nrow = 17, byrow = TRUE, dimnames = list(1:17, paste( 'T', 1:6, sep='' )) ) I found

Hello dear R help group, I would like to download the tRNA data on: http://gtrnadb.ucsc.edu/download.html And then import it into R. Can anyone direct me as to how to do so? Thanks, Tal ---------------------------------------------- My contact information: Tal Galili E-mail: Tal.Galili@gmail.com Phone number: 972-52-7275845 FaceBook: Tal Galili My Blogs: http://www.talgalili.com (Web and

Hello dear R help list, I wish to compile a check-list for diagnostic measures for OLS regression. My question: Can you offer more (or newer) tests/measures for the validity of a linear model then what is given here: http://www.statmethods.net/stats/rdiagnostics.html This resource gives a list of measures to test for: OUTLIERS, INFLUENTIAL OBSERVATIONS, NON-NORMALITY, NON-CONSTANT ERROR

Hello dear R-devel, I am allowing myself to bump this issue again (half a year later) - I think that the www.r-project.org website could benefit from having a description meta-tag. Could anyone please let me know who to contact regarding this? With much respect, Tal On Sat, Apr 2, 2011 at 4:24 PM, Tal Galili <tal.galili@gmail.com> wrote: > Hello Peter, > > Here is what google

We need a two-way non-parametric ANOVA in order to analysis properly some ecological data, do you know any reference in R? or how to do it? Thank you very much All the best diana -- View this message in context: http://www.nabble.com/Two-way-non-parametric-ANOVA--tp15039308p15039308.html Sent from the R help mailing list archive at Nabble.com.

Hello all, I imported a txt file with Hebrew column names, and wish to use them for my plots. I found that the Hebrew text is jumbled (Example: .áéèçåï.òöîé.) How do I fix it so to be able to see the characters in a correct way ? Thanks, Tal ---------------------------------------------- My contact information: Tal Galili E-mail: Tal.Galili@gmail.com Phone number: 972-52-7275845

Dear Dr. Fieberg, you used a regression tree approach to explore ordinal data set in addition to the proportinal odds model. I find this very interesting. I would like to know, how good the results of the regression tree approach turned out in comparison to the proportional odds model. Since people very often treat ordinal data as continuous, I would like to know how successfull this strategy

Dear R-team, As an extension of my previous work ( http://www.ncbi.nlm.nih.gov/pubmed/21377324) I am now about to handle new obtained ordinal data. Disabled students had to rank their top 5 incentives for participating in sport activities according to their personal (subjective) importance. Individual hierarchies of incentives were transformed so that now each dependent variable contains 6

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base

Hello dear R help members. I am trying to understand the anova.rq, and I am finding something which I can not explain (is it a bug?!): The example is for when we have 3 nested models. I run the anova once on the two models, and again on the three models. I expect that the p.value for the comparison of model 1 and model 2 would remain the same, whether or not I add a third model to be compared

Dear list members, I am estimating an ordinal generalized estimating equations (GEE) regression model on repeated measurements data. Is there any way (preferably in R) to test the proportional odds assumption of this model? Thanks in advance. Andreas [[alternative HTML version deleted]]

Hello all, When building a CART model (specifically classification tree) using rpart, it is sometimes interesting to know what is the importance of the various variables introduced to the model. Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas