similar to: plot(summary) within quantreg package

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

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've been trying to draw quantile linear regression lines across a scatterplot of my data using attach(forrq) plot(PREGNANT,DAY8,xlab="pregnant EPDS",ylab="postnatal EPDS",cex=.5) taus <- c(.05,.1,.25,.75,.9,.95) xx <- seq(min(PREGNANT),max(PREGNANT),100) for(tau in taus){ f <- coef(rq(DAY8~PREGNANT,tau=tau)) yy <-

Dear all, I am using the "rq.fit.hogg" function from the "quantreg" package. I have two problems with it. First (less importantly), it gives an error at its default values with error message "Error in if (n2 != length(r)) stop("R and r of incompatible dimension") : argument is of length zero". I solve this by commenting four lines in the code. I.e. I

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

Hi Folks, I am currently trying to present some results I obtained by using the quantreg package developed by Roger Koenker. After calculating fit<-summary(rq(Y~X1+X2, tau=2:98/100) ) the function plot(fit) presents a really nice the results by showing the values for all "regressors" in the given interval tau. But in my case, I only need the output of a single variable, say X1 and I

Hello dear R help members, I wish to get a nice LaTeX table for a rq object. Trying to use the functions I found so far wouldn't work. I can start opening the functions up, but I am wondering if I had missed some function which is the one I should be using. Here is an example session for a bunch of possible errors: (Thanks) data(stackloss) y <- stack.loss x <- stack.x rq_object

Dear All, I'm trying to construct confidence interval for an additive quantile regression model. In the quantreg package, vignettes section: Additive Models for Conditional Quantiles http://cran.r-project.org/web/packages/quantreg/index.html It describes how to construct the intervals, it gives the covariance matrix for the full set of parameters, \theta is given by the sandwich formula

Dear R-mailing list, I would like to overlay to two quantreg coefficients plots. I have plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata))) plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata1))) Is there a possibility to display the two in the same graph? Thank you so much!!! Christian [[alternative HTML version deleted]]

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

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]]

Rers: I installed R 2.9.0 from the Debian package manager on our amd64 system that currently has 6GB of RAM -- my first question is whether this installation is a true 64-bit installation (should R have access to > 4GB of RAM?) I suspect so, because I was running an rqss() (package quantreg, installed via install.packages() -- I noticed it required a compilation of the source) and

Dear all, I sent an email on Friday asking about nlrq {quantreg}, but I haven't received any answer. I need to estimate the quantile regression estimators of a model as: y = exp(b0+x'b1+u). The model is nonlinear in parameters, although I can linearise it by using log.When I write: fitnl <- nlrq(y ~ exp(x), tau=0.5) I have the following error: Error in match.call(func, call = cll) :

Full_Name: Edward Huang Version: 2.6.1 OS: Mac OS 10.5.1 Leopard Submission from: (NULL) (71.198.106.232) I'm trying to run quantile regression on my data. I just couldn't make it work. The same dataset ran okay on STATA 10, tho. Would you please take a look at it? Here is the error message: *** caught segfault *** address 0x3ff00008, cause 'memory not mapped' Traceback:

Hi I am using the quantreg package for median regression for a large series of subsets of data. It works fabulously for all but one subset. When it reaches this subset, R takes the command and never responds. I end up having to kill R and restart it. It appears to be something with the particular data subset, but I can't pinpoint the problem. Here are some details Operating system:

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

Dear all, I have just sent an email with my problem, but I think no one can see the red part, beacuse it is black. So, i am writing again the codes: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N <- 200 I <- 5 taus <- c(0.480:0.520) h <-

Hi, I have used package quantreg to estimate a non-linear fit to the lowest part of my data points. It works great, by the way. But I'd like to extract the predicted values. The help for predict.qss1 indicates this: predict.qss1(object, newdata, ...) and states that newdata is a data frame describing the observations at which prediction is to be made. I used the same technique I used

Hey, My dataset has 1 dependent variable(Logloss) and 7 independent dummy variables(AS,AM,CB,CF,RB,RBR,TS) , it's attached in this email. The problem is I cant finish Khmaladze test because there's an error "Error in switch(mode(x), "NULL" = structure(NULL, class = "formula"), : invalid formula" which I really dont know how to fix. My R version is 2.7.2.

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,