similar to: Warning Messages using rq -quantile regressions

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

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 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

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.

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very

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

Hi, how is it possible to retrieve the corresponding tau value for each observed data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like rq.fit <- rq(y~x,tau=-1). Thank you for your help. Jaci --

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:

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 have a question on plotting the coefficients from a series of mutivariate quantile regressions. The following code plots the coefficients for each RHS variable x1 and x2. Is there a way to plot only the coefficients on x1? In the data I am using, I have a large number of fixed effects and do want to plot the coefficients on these fixed effects. quant.plot <-

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

Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem,

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,

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

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R While this code run perfectly, it does not work for my data providing a warning message: In rq.fit.sfn(D, y, rhs = a) : tiny

Dear R, I have a question concerning quantile regression models. I am using the quantile regression model to test the relationship between abalone and the percentage cover of algae etc at different sites and depths. An example is fit<-rq(abalone~%coversessileinvertebrates+factor(Depth)+factor(Site),tau=0.7) In this model depth is fixed and site is random. My question is, is it possible

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, ...) :

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()

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