similar to: how to extract predicted values from a quantreg fit?

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

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

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 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

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

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

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 \ \ /\

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

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:

I'm in the very initial stage of expanding the formula processing in my quantile regression function rq() to handle additive nonparametric components, say qss(x), or qss(x,z). I need some advice about strategy for formula processing. My initial foray was to use: terms(formula,specials="qss") and then modify the components of the resulting terms.object. But in changing formula

Suppose I have a formula like this: f <- y ~ qss(x, lambda = lambdas[1]) + qss(z, lambdas[2]) + s I?d like a function, g(lambdas, f) that would take g(c(2,3), f) and produce the new formula: y ~ qss(x, lambda = 2) + qss(z, 3) + s For only two qss terms I have been using g <- function(lambdas, f){ F <- deparse(f) F <- gsub("lambdas\\[1\\]",lambdas[1],F) F

Recursively walk the formula performing the replacement: g <- function(e, ...) { if (length(e) > 1) { if (identical(e[[2]], as.name(names(list(...))))) { e <- eval(e, list(...)) } if (length(e) > 1) for (i in 1:length(e)) e[[i]] <- Recall(e[[i]], ...) } e } g(f, lambdas = 2:3) ## y ~ qss(x, lambda = 2L) + qss(z, 3L) + s On Fri, Oct

Hi, Can we attach a more descriptive "label" (I may use the wrong terminology, which would explain why I found nothing on the FAQ) to variable names, and later have an easy way to switch to these labels in plots? I fear this is not possible and one must enter this by hand as ylab and xlab when making plots. Thanks in advance, Denis Chabot

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

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, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

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, I'm wondering why 48 %/% 2 gives 24 but 4.8 %/% 0.2 gives 23... I'm not trying to round up here, but to find out how many times something fits into something else, and the answer should have been the same for both examples, no? On a different topic, I like the behavior of NAs better in R than in SAS (at least they are not considered the smallest value for a variable), but at the