similar to: Mac v Windows Mystery

Thierry, I have this: if (require(MatrixModels) && require(Matrix)) { X <- model.Matrix(Terms, m, contrasts, sparse = TRUE) in my function rqss() I've tried variants of requireNamespace too without success. If I understand properly model.Matrix is from MatrixModels but it calls sparse.model.matrix which is part of Matrix, and it is the latter function that I'm not

Hi, I am trying to use quantile regression to perform weighted-comparisons of the median across groups. This works most of the time, however I am seeing some odd output in summary(rq()): Call: rq(formula = sand ~ method, tau = 0.5, data = x, weights = area_fraction) Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 45.44262 3.64706 12.46007

He folks=) I want to check whether a coefficient has an impact on a quantile regression (by applying the sup-wald test for a given quantile range [0.05,0.95]. Therefore I am doing the following calculations: a=0; for (i in 5:95/100){ fitrestricted=rq(Y~X1+X2,tau=i) tifunrestrited=rq(Y~X1+X2+X3,tau=i) a[i]=anova(fitrestricted,fitunrestricted)$table$Tn) #gives the Test-Value } supW=max(a) As anova

I'm having trouble with R CMD check of my quantreg package. All is well until I get to: checking running R code from vignettes ... ?rq.Rnw? ... failed ERROR Errors in running code in vignettes: when running code in ?rq.Rnw? when I see a snippet from the vignette code and then: Loading required namespace: MatrixModels When sourcing ?rq.R?: Error: could not find function

Dear all I have a question about quantiles standard error, partly practical partly theoretical. I know that x<-rlnorm(100000, log(200), log(2)) quantile(x, c(.10,.5,.99)) computes quantiles but I would like to know if there is any function to find standard error (or any dispersion measure) of these estimated values. And here is a theoretical one. I feel that when I compute median from given

Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns. > object.size(subsetAudit.dat) 450832 bytes > str(subsetAudit.dat) 'data.frame': 11253 obs.

My apologies, the data can now be found at: url <- "http://www.econ.uiuc.edu/~roger/research/ebayes/velo.d" x <- scan(url,skip = 1) If I could get each of the histograms to mimic what is produced by hist(x, 100, freq = FALSE) I?ve experimented with xlim, ylim, without success so far... url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu

Hi Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Tanks [[alternative HTML version deleted]]

Is there a painless way to find the names of all packages on CRAN that "Depend" on a specified package? url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820

John, You can make a local version of bootcov which either: deletes these arguments from the call to fitter, or modify the switch statement to include rq.fit, the latter would need to also modify rq() to return a fitFunction component, so the first option is simpler. One of these days I'll incorporate clustered se's into summary.rq, but meanwhile this seems to be a good alternative.

Quantreggers: I'm trying to run rq() on a dataset I posted at: https://docs.google.com/file/d/0B8Kij67bij_ASUpfcmJ4LTFEUUk/edit?usp=sharing (it's a 1500kb csv file named "singular.csv") and am getting the following error: mydata <- read.csv("singular.csv") fit_spl <- rq(raw_data[,1] ~ bs(raw_data[,i],df=15),tau=1) > Error in rq.fit.br(x, y, tau = tau, ...) :

I'm trying to redo an old plot with: > sessionInfo() R version 2.11.0 Under development (unstable) (2010-02-09 r51113) x86_64-apple-darwin9.8.0 When I do: pdf("lty.pdf",height = 6, width = 8) u <- 1:100/100 y <- matrix(rep(1:10,each = 100),100) matplot(u,y,lwd = 2,type ="l") dev.off() the line types that have dots are difficult to distinguish because the

Hi. I am aware of three different R packages for linear programming: glpk, linprog, lpSolve. From what I can tell, if there are N variables and M constraints, all these solvers require the full NxM constraint matrix. Some linear solvers I know of (not in R) have a sparse matrix input format. Are there any linear solvers in R that have a sparse matrix input format? (including the

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Hi, I was wondering whether there is any Fortran function or associated library for evaluating the quantiles of a set of values (something which the R-function quantile() does). Any help will be much appreciated. Thanks and regards, Dhiman Bhadra [[alternative HTML version deleted]]

Hi Kurt, I have started to look into this, and I need some guidance about how to prioritize my repairs. There are basically 4 categories of warnings from gfortran?s pedantic critique of my packages: 1. Some errant tab characters it doesn?t like, 2. Too many or too few continue statements 3. Horrible (and obsolescent) arithmetic and computed gotos 4. undeclared doubles and dubious

I am trying to do some comparisons of density estimators using lattice. The code below attempts to plot the same histogram in each panel and then overplots a kernel estimate with different bandwidths. Finding packet.number() was a bit arduous, but seems to do what I want. My concern now is that close examination of the 4 histograms reveals that they are different even though they use the same

Dear all, I am having v.4.36 of Quantreg package and I noticed strange behaviour when weights were added. Could anyone please explain me what if the results are really strange or the behavioiur is normal. As an example I am using dataset Engel from the package and my own weights. x<-engel[1:50,1] y<-engel[1:50,2] w<-c(0.00123, 0.00050, 0.00126, 0.00183, 0.00036, 0.00100, 0.00122,

Rers: I installed R 2.9.0 from the Debian package manager on our amd64 system that currently has 6GB of RAM -- my first question is whether this installation is a true 64-bit installation (should R have access to > 4GB of RAM?) I suspect so, because I was running an rqss() (package quantreg, installed via install.packages() -- I noticed it required a compilation of the source) and

quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544