similar to: vignette checking woes

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

> On Mar 26, 2015, at 8:50 AM, Martyn Plummer <plummerM at iarc.fr> wrote: > > On Wed, 2015-03-25 at 15:12 -0500, Roger Koenker wrote: >> Thierry, >> >> I have this: >> >> if (require(MatrixModels) && require(Matrix)) { >> X <- model.Matrix(Terms, m, contrasts, sparse = TRUE) > > You have this in the current release, which

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

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

I?d like to solicit some advice on a debugging problem I have in the quantreg package. Kurt and Brian have reported to me that on Debian machines with gfortran 9 library(quantreg) f = summary(rq(foodexp ~ income, data = engel, tau = 1:4/5)) plot(f) fails because summary() produces bogus estimates of the coefficient bounds. This example has been around in my R package from the earliest days of R,

Apologies, forgot to copy R-help on this response. Begin forwarded message: From: Roger Koenker <rkoenker at illinois.edu> Subject: Re: [R] effects() extractor for a quantile reqression object: error message Date: September 6, 2024 at 8:38:47?AM GMT+1 To: "Christopher W. Ryan" <cryan at binghamton.edu> Chris, This was intended to emulate the effects component of lm()

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.

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.

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

I discovered recently that the phrase "Oja median" produces no hits in Jonathan Baron's very valuable R search engine. I found this surprising since I've long regarded this idea as one of the more interesting notions in the multivariate robustness literature. To begin to remedy this oversight I wrote a bivariate version and then decided that writing a general p-variate version

Hello all, I would like to compute a quantile regression using rq (from the quantreg package), while keeping one of the coefficients fixed. Is it possible to set an offset for rq in quantreg? (I wasn't able to make it to work) Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili at gmail.com |? 972-52-7275845 Read me:

Hello, I'm seeing three different vignette-related errors with recent versions of R-3.0.0 alpha. First, with the package BitSeq (http://bioconductor.org/packages/2.12/bioc/html/BitSeq.html), I get the following when trying to build the package: Error: processing vignette ?BitSeq.Rnw' failed with diagnostics: Failed to locate the ?weave? output file (by engine ?utils::Sweave?) for

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

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]]

Hi All, I'm still having significantly difficulty getting the quantreg library running in R. I'm running R on MEPIS using the debs created by Dirk Eddelbuettel and placed in apt testing. When I try to install quantreg using the install.packages() function it fails with: /usr/bin/ld: cannot find -lblas-3 Dirk was nice enough to send me a .deb for quantreg which installs without

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

I was trying "quantreg" package, lm1 <- lm(y~x) rq1 <- rq(y~x) plot(summary(rq1)) #then got a warning says singular value, etc. but this line can be omited plot(lm1) #crash here It happened every time on my PC, Windows XP Pro Serv. Pack 1, Pentium(4) 3.00G. [[alternative HTML version deleted]]

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,

I have spent hours on this ---looked through the quantreg manual and r-help site--- still couldn't figure out the answer. Can someone please help me on this? I plot the result from quantile regression and want to change the variable labels: temp<-rq(dep~inc+age50, data=newdata, tau=1:9/10) temp2<-plot(summary(temp)) dimnames(temp2)[[1]]<-c("Intercept", "Per Capita