similar to: Quantile Regression/(package (quantreg))

Dear all, I want to use the "rearrange" command which is based on Chernozhukov et al paper and is included in the quantreg package. So, I run a quantile regression in which I included dummy variables for state and years in order to estimate the respective fixed effects quantile regression. The problems are the followings: 1. At example that is stated in the help****, I don't

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

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 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 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

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 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) :

Dear all How can I obtain the data from the function "rearrange" in package quantreg More especifically, based on the example below (available in the help of the rearrange function), how can I access the data generated by "rearrange(zp)" ? data(engel) z <- rq(foodexp ~ income, tau = -1,data =engel) zp <-

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

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

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 would like to fit the following model with quantile regression: y ~ alpha + beta where both alpha and beta are factors. The conceptual model I have in my head is that alpha is a constant set of values, that should be independent of the quantile, tau and that all of the variability arises due to beta. If I just fit the model using the quantreg package like so: mdl <- rq( y ~ alpha

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

Quantreg package allows to plot the summary of models derived by quantile regression at different taus. The plot shows the parameters variation by varying taus: intercept and slope (for a linear model). Together with these values even confidence intervals may be plotted, based on the threshold given within the summary (e.g. alpha=0.01 equals 99% CI). However the graphic even plots the mean of

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,

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

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

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 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)

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