similar to: rqss.object

Hello, i have the following data: x=c(0,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.21,0.22,0.23,0.25,0.26,0.27,0.46,0.47,0.48,0.49) y=c(0.48,0.46,0.41,0.36,0.32,0.35,0.48,0.47,0.55,0.56,0.54,0.67,0.61,0.60,0.54,0.51,0.45,0.42,0.44,0.46,0.41,0.43,0.43,0.48,0.48,0.47,0.39,0.37,0.32,0.29) and tried to get piecewise linear regression. Doing a

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 list, I used rqss() in quantreg package for a piecewise linear regression. Can someone tell me how to find the x values corresponding to the breakpoints and the slopes for the phases before and after the breakpoints? I searched the list and gather that there is another package "segmented" that does that, but my stat is pathetic, and I have difficulty setting the parameters right

Hi, everyone. I have some questions about quantile regression in R. I am running an additive quantile regression first for a complete matrix and then with some selected rows. I am doing the following: datos <-read.table("Regresion multiple.txt",header=T) Fit<-rqss(datos$campings ~datos$Cobarbogrupo+datos$CobSDgrupo+datos$Areadecultivosgrupo, tau=0.9) summary.rq(Fit) #The

I'm having difficulty with an environmental issue: I have an additive model fitting function with a typical call that looks like this: require(quantreg) n <- 100 x <- runif(n,0,10) y <- sin(x) + rnorm(n)/5 d <- data.frame(x,y) lam <- 2 f <- rqss(y ~ qss(x, lambda = lam), data = d) this is fine when invoked as is; x and y are found in d, and lam is found the

Dear all, My machine is SUN Java Workstation 2100 with 2 AMD Opteron CPUs and 16GB RAM. R is compiled as 64bit by using SUN compilers. I trying to fit quantile smoothing on my data and I got an message as below. > fit1<-rqss(z1~qss(cbind(x,y),lambda=la1),tau=t1) Error in as.matrix.csr(diag(n)) : cannot allocate memory block of size 2496135168 The lengths of vector x and y are

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

Hi Paul, Take a look at gam() from package mgcv (gam = generalized additive models), maybe this will help you. GAMs can work with other distributions as well. Generalized additive models consist of a random component, an additive component, and a link function relating these two components. The response Y, the random component, is assumed to have a density in the exponential family. I am not sure

Hi, Let me pick up this old thread. How does one extract the locations of the knots (ends of the segments) from the fit object below? Thanks, Vadim >From : roger koenker < roger_at_ysidro.econ.uiuc.edu > Date : Tue 31 May 2005 - 10:23:19 EST It is conventional to fit piecewise linear models by assuming Gaussian error and using least squares methods, but one can argue that

loess() should be able to do robust 2D smoothing. There's no natural ordering in 2D, so defining running medians can be tricky. I seem to recall Prof. Koenker talked about some robust 2D smoothing method at useR! 2004, but can't remember if it's available in some packages. Andy From: Vladislav Petyuk > > Hi, > Are there any multidimenstional versions of runmed() and >

We are announcing a short course, which is scheduled to take place in June 2007 at The University of Reading, UK. Summary information is given below. For more detailed information and registration forms please see http://www.ssc.rdg.ac.uk providing your address and/or fax number, or email statistics-courses@lists.reading.ac.uk. Statistical Modelling and Graphics using R

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

Folks, We are contemplating holding a Birds of a Feather session titled "Julia and LLVM: Implementing a fast dynamic language for technical computing" at the LLVM 2012 European Conference on April 12-13 in London. http://llvm.org/devmtg/2012-04-12/ Would this be of interest to the LLVM developer and user community? It would be great if you could drop me a line. It will help us gauge

Dear LLVM contributors, my name is Matthias, I am a graduate student majoring in computer science at the Vienna University of Technology, and I am glad to be one of this year's GSoC students who work for the Julia foundation. Julia is a relatively young programming language that uses LLVM for just-in-time compilation and the goal of my project is to integrate Polly in this environment to

Dear Community, in an earlier post, students working on LLVM were asked to provide a short report on their GSoC project. in the following I want to give an overview on the current status of my GSoC project and outline my next planned activities. Since my mentoring organization is Julia, I also send this to the according mailing list. *1. Activities so far:* As described in my proposal [1], I

hello everyone,recently I write a package for R call Julia through Julia C API https://github.com/armgong/RJulia now the package can do 1 finish basic typemapping, now int boolean double R vector to julia 1d-array is ok,and julia int32 int64 float64 bool 1D array to R vector is also ok. 2 R STRSXP to julia string 1D array and Julia string array to STRSXP is written but not sure it is correct or

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

Actually it only link the shlib fine. Linking any executable against it fails: llvm[2]: Linking Release+Asserts executable llvm-lto (without symbols) /home/kfischer/julia/deps/llvm-svn/build_Release+Asserts/Release+Asserts/bin/clang++ -fsanitize=address -O3 -Wl,-R -Wl,'$ORIGIN/../lib' -L/home/kfischer/julia/deps/llvm-svn/build_Release+Asserts+Sanitize/Release+Asserts/lib

Hello, I'm trying to fix a long standing issue we are having in Julia where when the file information switched, we weren't recording that correctly, so line information showed up in the wrong file. Basically we would always create a scope with the DISubprogram and the given line number. What I tried was to change the scope to be a DIFile instead so we'd get the correct file

Sorry, I didn't have a small IR example and I was sure I was just doing something stupid. Thanks for the help, I'll try it out and report back. Maybe it would be good to add an assertion or something that tells people what's wrong in this case, since the generated DWARF seems to be invalid? On Wed, Aug 13, 2014 at 5:53 PM, David Blaikie <dblaikie at gmail.com> wrote: > Use