similar to: Piecewise Linear Regression

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, 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

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

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

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

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 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}}

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

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

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

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

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]]

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

Hi everybody, I installed Rglpk package from local zip files. Then I tried to load the package in a R session. But R shows following message. Loading required package: slam Error: package 'slam' could not be loaded In addition: Warning message: In library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'slam' Hence, the

Hi, I am a biologist (relatively new to R) analyzing data which we predict to fit a power function. I was wondering if anyone knew a way to model piecewise functions in R, where across a range of values (0-x) the data is modeled as a power function, and across another range (x-inf) it is a linear function. This would be predicted by one of our hypotheses, and we would like to find the AICs

Dear friends, I am building an R package called *mypackage*. I followed every possible steps (to my understanding) for the same. I got following problem while doing *R CMD check mypackage*. * installing *source* package 'mypackage' ... ** libs cygwin warning: MS-DOS style path detected: C:/PROGRA~1/R/R-213~1.0/etc/i386/Makeconf Preferred POSIX equivalent is:

I am a novice R user. I would like to be able to graph some simple piecewise functions/functions with domain restrictions in R, but I'm having trouble defining such functions. For example, I would like to define the following function: f(x)={x^2 if -1<x<x; 1 if 2<x<3} Notably, the function is undefined outside of domain (-1,1)U(2,3). My best attempt in R is something like

Which function implements the piecewise cubic Hermite interpolation? I am looking for equivalent of matlab's interp1 with the method = 'pchip' Here is the reference http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/techdoc/ref/interp1.html& -- View this message in context: