search for: kolenikov

...ound I am stuck... I am trying to compile R from 2.6.2 sources in the meantime, but that's painful for a Linux newbie like me on a brand-new computer that does not necessarily have all the packages. I almost want to switch to Ubuntu as R installation back there was a single click :)) -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: Please do not reply to my Gmail address as I don't check it regularly.

Dear there, I am working on the NHANES survey data, and want to apply quantile regression on these complex survey data. Does anyone know how to do this? Thank you in advance, Yiling Cheng Yiling J. Cheng MD, PhD Epidemiologist CoCHP, Division of Diabetes Translation Centers for Disease Control and Prevention 4770 Buford Highway, N.E. Mailstop K-10 Atlanta, GA 30341 [[alternative HTML

what does R have to compare with , say , proc surveymeans, estimate survey means/proportions with standard errors, using Taylor methods? [[alternative HTML version deleted]]

Dear R users, I am trying to import a table from STATA, a dta file. With a table called "table", this is what I do : library("foreign") read.dta(table) It does not work. What am I doing wrong ? Best Regards, Dwayne [[alternative HTML version deleted]]

Dear R-Users, Is anyone aware of a significance test which allows demonstrating that one distribution dominates another? Let F(t) and G(t) be two distribution functions, the alternative hypothesis would be something like: F(t) >= G(t), for all t null hypothesis: F(t) < G(t), for some t. Best wishes, Matthias PS. This one would be ok, as well: F(t) > G(t), for all t null

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Is it possible to fit a structural equation model with link functions in R? I am trying to build a logistic-regression-like model in sem, because incorporating the dichotomous variables linearly seems inappropriate. Mplus can do something similar by specifying a 'link' parameter, but I would like to be able to do it in R, ofcourse. I have explored the 'sem' package from John Fox,

I'm trying to learn how to calibrate/postStratify/rake survey data in preparation for a large survey effort we're about to embark upon. As a working example, I have results from a small survey of ~650 respondents, ~90 response fields each. I'm trying to learn how to (properly?) apply the aforementioned functions. My data are from a bus on board survey. The expansion in the

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a

G'Day R users! Following an ordination using prcomp, I'd like to test which variables singnificantly contribute to a principal component. There is a method suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called "bootstrapped eigenvector". It was asked for that in this forum in January 2005 by J?r?me Lema?tre: "1) Resample 1000 times with replacement entire

Dear List: I am working to understand some differences between the results of the svymean() function in the survey package and from code I have written myself. The results from svymean() also agree with results I get from SAS proc surveymeans, so, this suggests I am misunderstanding something. I am never comfortable with "I did what the software" does mentality, so I am working to

Dear list useRs, I have to generate a random set of coordinates (x,y) in [-1 ; 1]^2 for say, N points. At each of these points is drawn a circle (later on, an ellipse) of random size, as in: > N <- 100 > > positions <- matrix(rnorm(2 * N, mean = 0 , sd= 0.5), nrow=N) > sizes<-rnorm(N, mean = 0 , sd= 1) > plot(positions,type="p",cex=sizes) My problem is to

Hi All, I am scheduled to teach a graduate course on research methods in health sciences at a university. While drafting the course proposal, I decided to include a brief introduction to R, primarily with an objective to enable the students to do data analysis using R. It is expected that enrolled students of this course have all at least a formal first level introduction to quantitative methods