similar to: running median and smoothing splines for robust surface f itting

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

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 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

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 just found this issue: dd1 = c(rep(NaN,82), rep(-1, 144), rep(1, 74)) xx = runmed(dd1, 21) -> R crashes reproducibly in R 3.4.3, R3.4.4 (Ubuntu 14.04/Ubuntu 16.04) With GDB: Program received signal SIGSEGV, Segmentation fault. swap (l=53, r=86, window=window at entry=0xc59308, outlist=outlist at entry=0x12ea2e8, nrlist=nrlist at entry=0x114fdd8, print_level=print_level at

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

Gurus: I have a new version of my quantreg package with minimal changes, mainly to fix some obscure fortran problems. It fails R CMD check ?as-cran with the error: Running examples in ?quantreg-Ex.R? failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: plot.rqss > ### Title: Plot Method for rqss Objects

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

I have a Date x Stock (223 x 520) matrix of "trading volume". I can calculate a 5-day (past) average in about 1 second using: R> apply(vol, 1, filter, filter=c(0, rep(1/5,5)), sides=1) I would like to do the same with a 5-day median, e.g.: R> mymed <- function(x, n=5) { R> r <- rep(NA, length(x)) R> for (i in (n+1):length(x)) r[i] <- median(x[i-(1:n)]) R>

Hi, Lately I run into a problem that my code R code is spending hours performing simple moving window statistical operations. As a result I did searched archives for alternative (faster) ways of performing: mean, max, median and mad operation over moving window (size 81) on a vector with about 30K points. And performed some timing for several ways that were suggested, and few ways I come up

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 \ \ /\

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

Dear Prof. Roger Koenker, On Tue, 22 Oct 2024 09:08:12 +0000 "Koenker, Roger W" <rkoenker at illinois.edu> wrote: > > fN <- rqss(y~qss(x,constraint="N")+z) > > *** caught segfault *** > address 0x0, cause 'invalid permissions? Given a freshly produced quantreg.Rcheck directory, I was able to reproduce this crash by running R -d gdb # make

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

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

Hi, First, let me thank Jaroslaw for making this survey. I find it quite illuminating. Now the questions: * the #1 solution below (based on cumsum) is numerically unstable. Specifically if you do the runmean on a positive vector you can easily get negative numbers due to rounding errors. Does anyone see a modification which is free of this deficiency? * is it possible to optimize the

Hi, Has anyone know about how to get the correct plot? I have use this R script (as below), so I expect the plot is based on x axis, but the result was opposite. Any suggestion will be great. library(IRanges) data <-read.table(file="~/q20snpref/illusmp454merbed",sep="\t",header=F) colnames(data)<-c("Scaffold","sca_position","coverage")

>>>>> Hilmar Berger >>>>> on Fri, 5 Oct 2018 10:17:49 +0200 writes: > Dear all, I just found this issue: > I just found this issue: > dd1 = c(rep(NaN,82), rep(-1, 144), rep(1, 74)) > xx = runmed(dd1, 21) >> R crashes reproducibly in R 3.4.3, R3.4.4 (Ubuntu 14.04/Ubuntu 16.04) and also in the latest development version

Hi, I'm very beginner for R but I think it is a time to start as it is very useful. I have a coverage read file (illusmp454merCbed) for whole genome ~ 450 Mbp. This is head of this file. Scaffold sca_position coverage Scaffold1 1 0 Scaffold1 2 0 Scaffold1 3 0 Scaffold1 4 0 Scaffold1 5 0 Scaffold1 6 0 Scaffold1 7 1 Scaffold1 8 3 Scaffold1 9 3 I would like to plot everage coverage for every 1