similar to: vignette checking woes

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

> 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

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, ...) :

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

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.

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

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

Dear All, I'm trying to track down a problem with my quantreg package reported by a user doing censored quantile regression. When he runs the test4.R file attached below (which reads the csv file also attached) on his windows machine he gets an error like this: > Error in dimnames(B) <- list(c("tau", dimnames(x)[[2]], "Qhat"), NULL) : > length of

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

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]]

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

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

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

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'm having difficulty with an environmental issue: I have an additive model fitting function with a typical call that looks like this: require(quantreg) n <- 100 x <- runif(n,0,10) y <- sin(x) + rnorm(n)/5 d <- data.frame(x,y) lam <- 2 f <- rqss(y ~ qss(x, lambda = lam), data = d) this is fine when invoked as is; x and y are found in d, and lam is found the

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, has anybody an example of an Engel curve analysis in R or does there exist a package to estimate and plot Engel curves from expenditure / income data in R? Thanks a million for your hints, Werner

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