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Dear R Users, Did someone implemented the R1 (Pseudo R-2) and likelihood ratio statistics for quantile regressions, which are some of the inference procedures for quantile regression found in Koenker and Machado (1999)? I tried the Ox version, but my dataset is too large (> 50.000) and the algorith breaks. ________________________________________ Ricardo Gonçalves Silva, M. Sc. Apoio aos

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

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 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

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

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 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

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 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 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

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()

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

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

Hi, I am trying to add a function in a linear quantile regresion to find a breakpoint. The function I want to add is: y=(k+ax)(x&lt;B)+(k+(a-d)B+dx)(x&gt;B) How do I write it in the rq() function? Do I need to define the parameters in any way and how do I do that? I'm a biologist new to R. Thanks! -- View this message in context:

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. 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

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:

Hi, I was recently plowing through the docs of the quantreg package by Roger Koenker and came across the total variation penalty approach to 1-dimensional spline fitting. I googled around a bit and have found some papers originated in the image processing community, but (apart from Roger's papers) no paper that would discuss its statistical aspects. I have a couple of questions in this

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Hi, I have two sets of data that I would like to put into a data frame. But since they have different length, I am not sure how to do this. Here is an example of my data: data set one: date growth 1/1/2007 10 1/2/2007 10.2 1/3/2007 10.4 1/4/2007 10.6 data set two: date growth 1/1/2007 22 1/2/2007 22.5 1/4/2007 22.4 I would like to combine the two data sets and