similar to: warning message when running quantile regression

Hello. I am a beginer in R and I can not implement the KhmaladzeTest in the following command. Please help me!!!!!!!!!!! PD: I attach thw results and the messages of the R program R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) ISBN 3-900051-07-0 R es un software libre y viene sin GARANTIA ALGUNA. Usted puede redistribuirlo bajo ciertas

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

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 using quantreg package version 5.98 of 24 May 2024, in R 4.4.1 on Linux Mint. The online documentation for quantreg says, in part, under the description of the rq.object, "The coefficients, residuals, and effects may be extracted by the generic functions of the same name, rather than by the $ operator." I create an rq object for the 0.9 quantile, called qm.9 effects(qm.9)

Hi, Could you please explain what is non-positive fis error? I have been trying to use quantile regression (rq) procedure and I keep ending up with this error. I haven't been able to find an explanation for the same. Best Regards, Arti Arti Mann Ph.D. Student Department of Information Systems W.P. Carey School of Business Arizona State University Email : Arti.Mann at asu.edu

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

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()

Hi, I am trying to read a SAS version 9.1.3 SAS dataset into R (to preserve the SAS labels), but am unable to do so (I have read in a CSV version). I first created a transport file using the SAS code: libname ces2 'D:\CES Analysis\Data'; filename transp 'D:\CES Analysis\Data\fadata.xpt'; /* create a transport file - R cannot read file created by proc cport */ proc

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.

Hello all, I wish to fit a loess smother to a plot of Y`X, but in predicting the 95% quantile. Something that will be a combination of what rq (package quantreg} does, with loess. Is there a function/method for doing this? Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili@gmail.com | 972-52-7275845 Read me:

The help page and vignette for summary.rq(quantreg) mention that there are three different bootstrap methods available for the se="bootstrap" argument, but I can't figure out how to select a particular method. For example, if I want to use the "xy-pair bootstrap" how do I indicate this in summary.rq? Tom -- View this message in context:

I am using the quantreg package to build a quantile regression model and wish to generate confidence intervals for the fitted values. After fitting the model, I have tried running predict() and predict.rq(), but in each case I obtain a vector of the fitted values only. For example: library(quantreg) y<-rnorm(50,10,2) x<-seq(1,50,1)

Dear Users, I loaded my dataset as following: presu <- read.table("C:/_Ricardo/Paty/qtdata_f.txt", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dep<-presu[,3]; exo<-presu[,4:92]; When I try: rq(dep ~ exo, ...) or mle.stepwise(dep ~ exo, ...) I got the same error: > rq(dep ~ exo) Error in model.frame(formula, rownames,

Hi all, I'm having some troubles with the Quantreg package. I am using R version 2.10.0, and have downloaded the most recent version of Quantreg (4.44) and SparseM (0.83 - required package). However, when I try to run an analysis (e.g. fit1<-rq(y~x, tau=0.5)) I get an error message saying that the function "rq" could not be found. I get the same message when I try to search

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

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

Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

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

Dear all, I would like to know whether any specification test for linear against nonlinear model hypothesis has been implemented in R using the quantreg package. I could read papers concerning this issue, but they haven't been implemented at R. As far as I know, we only have two specification tests in this line: anova.rq and Khmaladze.test. The first one test equality and significance of