similar to: effects() extractor for a quantile reqression object: error message

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 All, I am running quantile regression in a "for loop" starting with 1 variable and adding a variable at a time reaching a maximum of 20 variables. I get the following warning messages after my "for" loop runs. Should I be concerned about these messages? I am building predictive models and am not interested in inference. Warning messages: 1: In

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

All - Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Specifically, I'm wondering if anyone has implemented the goodness-of-fit process developed by Koenker and Machado (1999) for R? Though I have used package quantreg in the past, I may have overlooked this function, if it is included. Citation: Koenker, R. and

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:

R-ers: I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion? I'm trying to derive a model of a "hull" of points which are likely to require higher-order polynomial fitting (e.g. splines)-- would quantreg be sufficient, if the response and predictors are all continuous? Thanks! --j

Hi, I'm using metafor to perform a mixed-effects meta-analysis. I'd like to test whether the effect is different for animals and plants/whether "group" (animal/plant) influences the effect size, but am having trouble interpreting the results I get. I've read previous posts about QM in metafor, but I'm still a bit confused. I've dummy-coded the factors:

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)

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.

I've been looking for ways to calculate a large number (100) of non-crossing Nonparametric quantile regressions on large populations (1000+). Can the quantreg package in R ensure the non-crossing property? If not, do you know any alternative? Thank you, Paulbegc -- View this message in context:

I attempt install and use qmtest for run LLVM testsuit at FreeBSD, but without success. I have installed qm-2.2 (latest and single version accessable from http://www.codesourcery.com/qmtest/download.html in sources) When I start qmtest for LLVM ("make qmtest" in <llvm-obj-dir>/test directory - right?) error printed: ---8X---------------

Am 03.01.19 um 16:19 schrieb Rowland Penny via samba: > On Thu, 3 Jan 2019 15:46:24 +0100 "Stefan G. Weichinger via samba" > <samba at lists.samba.org> wrote: >> observation, maybe important: > > Oh, it's more than important, guess where the Windows ACLs are stored > ;-) hmm ... ? ;) >> (share "projekte" works fine, share "QM"

Please, I have a problem with nonlinear quantile regression. My data shows a large variability and the quantile regression seemed perfect to relate two given variables. I got to run the linear quantile regression analysis and to build the graph in the R (with quantreg package). However, the up part of my data dispersion seems a positive exponential curve, while the down part seems a negative

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 list, I'm using the quantreg package for quantile regression. Although it's fine, there're is some weird behavior a little bit difficult to understant. In some occasions, the regression results table shows coefficients, t-statistics, standard errors and p-values. However, in other occasions it shows only coefficients and confidence intervals. Therefore, the question is... Is

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:

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

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

I used R and the quantreg package in a manuscript that is currently in the proofs stage. I cited both R and quantreg as suggested by citation() and noted the version of R and quantreg that I used in the main text as "All tests were computed with the R v2.9.0 statistical programming language (R Development Core 2008). Quantile regressions were conducted with the quantreg v4.27 package

Am 03.01.19 um 15:29 schrieb Rowland Penny via samba: > On Thu, 3 Jan 2019 15:08:46 +0100 > "Stefan G. Weichinger via samba" <samba at lists.samba.org> wrote: > >> >> We are in the process of switching over shares from the old way of >> doing this to Windows ACLs: >> >> disable "valid users" "write list" etc >>