search for: rqss

...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 simple spline smoothing gives the basic shape of the expected curve: plot(x,y) lines(smooth.spline(x, y), lty=2, col = "red") Now I tried to do rqss. But what I got was only a straight line and nothing appropriate. No parameter change did help. Can anyone tell me what's wrong with this approach? library(quantreg) fit <- rqss(y ~ qss(x)) plot(fit) points(x,y) lines(smooth.spline(x, y), lty=2, col = "red") Thank You in advance....

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

Hello, I am a new user and I am looking for the description of the output of rqss function (Additive Quantile Regression Smoothing). It is supposed to be in rqss.object but I could not find any reference to rqss.object anywhere. thanks a lot. Julia [[alternative HTML version deleted]]

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,...

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 watched the memory usage spike to 4.9GB (my input data contains > 500,000 samples). With this said, after 30 mins or so of processing, I got the following error: tahoe_rq &lt...

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 .GlobalEnv, or at least this is how I understand it. Now, I'd like to have a function say, h <- function(lam) AIC(rqss(y ~ qss(x, lambda = lam), data = d)) but now,...

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 both 17664. I tried and found that the same command ran with x[1:16008] and y[1:16008]. So, it looks to me a memory related problem, but I...

...then create such a dataframe like this MyX <- data.frame(x=seq(-1,60)) This works fine with GAM (mgcv) but not with quantreg: > y <- rnorm(500, 10, 5) > x <- rep(seq(1,50,1), 10) > My.data <- data.frame(x, y) > My.x <- data.frame(x=seq(5,45)) > > fit <- rqss(y ~ qss(x, lambda=5), tau=0.05) > pred <- predict.qss1(fit, My.x) Could someone please help me creating a dataframe "newdata" that would satisfy predict.qss1? Thanks in advance, Denis Chabot

...,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,5 0.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) y = c (0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186 ,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) library(quantreg) plot(x,y) fit <- rqss(y ~ qss(x)) plot(fit) it gives 5 segments not 3, but this can be controlled by the choice of lambda in the qss function, for example, try: fit <- rqss(y ~ qss(x,lambda=3) plot(fit,col="red") which gives a fit like you suggest might be reasonable with only three segments. ... s...

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 output is: Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 1.46561...

R-ers: I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion? I'm trying to derive a model of a "hull" of points which are likely to require higher-order polynomial fitting (e.g. splines)-- would quantreg be sufficient, if the response and predictors are all continuous? Thanks! --j

Hi, I need to fit a piecewise linear regression. x = c(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,50.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) y = c(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) there are two change points. so the fitted curve should look like \ \ /\

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 >

Thierry, I have this: if (require(MatrixModels) && require(Matrix)) { X <- model.Matrix(Terms, m, contrasts, sparse = TRUE) in my function rqss() I've tried variants of requireNamespace too without success. If I understand properly model.Matrix is from MatrixModels but it calls sparse.model.matrix which is part of Matrix, and it is the latter function that I'm not finding. Maybe I should go back to the requireNamespace strategy...

Hello all, I wish to fit a loess smother to a plot of Y`X, but in predicting the 95% quantile. Something that will be a combination of what rq (package quantreg} does, with loess. Is there a function/method for doing this? Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili@gmail.com | 972-52-7275845 Read me:

...bit of evidence for this is fact that R CMD build gives me: * creating vignettes ... ERROR Rscript execution error: No such file or directory so I've been building with the --no-build-vignettes flag... Many thanks, for your suggestions, Roger > > Martyn > >> in my function rqss() I've tried variants of requireNamespace too without success. >> If I understand properly model.Matrix is from MatrixModels but it calls >> sparse.model.matrix which is part of Matrix, and it is the latter function that I'm >> not finding. Maybe I should go back to the...

I appreciate to Roger Koenker, Achim Zeileis and Vito Muggeo for their informative answers. Listed below is unedited replies I got followed by the question I posted. Kyong 1. Roger Koenker: You might try rqss() in the quantreg package. It gives piecewise linear fits for a nonparametric form of median regression using total variation of the derivative of the fitted function as a penalty term. A tuning parameter (lambda) controls the number of distinct segments. More details are available in the vi...

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:

Dear list, I have a series of data points which I want to approximate with exactly two linear functions. I would like to choose the intervals so that the total deviation from my fitted lines is minimal. How do I best do this? Thanks! Kamila The information transmitted in this electronic communication...{{dropped}}

Hello, R users, I applied segmented regression method contributed by Muggeo and got different slope estimates depending on the initial break points. The results are listed below and I'd like to know what is a reasonable approach handling this kinds of problem. I think applying various initial break points is certainly not a efficient approach. Is there any other methods to deal with segmented