similar to: Piecewise Linear Regression

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

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

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

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, 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 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}}

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

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

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:

Hello everybody!!!! Quick question, if you'd like to throw a little tip: does anyone knows a function that runs piecewise regression models with coefficients estimation and inferences ? Thank you [[alternative HTML version deleted]]

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 all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

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

Dear all, I have two variables, y and x. It seems that the relationship between them is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20, there is a linear relationship between y and x; while x>=20, there is another different linear relationship between them. How can i specify their relationships in R correctly? # glm(y~I(x<20)+I(x>=20),family = binomial, data =

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 all, I am just wondering if anybody has implemented a function that can give a piecewise linear interpolation in more than 2 dimensions? I have looked at the akima package, but I would rather like a piecewise linear interpolation rather than a spline and while it did the job quite satisfactory for 2 dimensions, I need to interpolate in at least three dimensions. If anybody has implemented

Hi All, I want to fit a piecewise spline of degree 1, i.e. a spline consisting of a straight line over each piece. I downloaded the R package pspline, then I have following questions: 1. in the program, the degree of the spline is specified by 2*norder-1. Why do they adopt such scheme that we can only fit a spline with odd degree? 2. norder cannot be set to 1. Is there any specific reason

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:

Hi All, I am trying to fit a piecewise lasso regression, but package Segmented does not work with Lars objects. Does any know of any package or implementation of piecewise lasso regression? Thanks, Lucas