similar to: Piecewise nls w/ boundary as a fitting parameter

Dear all, Here I come with another stupid question. Suppose I want to use nls() to fit a series of data (here modelled by generated points), then plot the points and the fitting curve. I figured out some way of doing it: x <- runif(1:20, 0, 10) y <- 0.1*x^2 - rep(3, length(x)) + rnorm(length(x), sd = 0.5) yfit <- nls(y ~ a*x^2 + b*x + c, start = list(a = 1, b = 1, c = 1),

Hello everybody, I am sure this is a beginners' problem which is being asked recurrently every few months, but nevertheless I wasn't able to find the answer searching through the r-help list. So here is my problem: I would like to plot a set of points (y vs. x), a (linear) regression line through them, and on the second graph underneath the first one I would want to plot residuals vs. x.

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

Hello all, I am trying to estimate the cumulative distribution function for a single stock return time series. A piecewise estimation is composed of three parts: parametric generalized Pareto (GP) for the lower tail (10% of the observation), non-parametric kernel-smoothed interior (80% of the observations), and GP for the upper tail (10%). I wonder if anyone has clue about this in R. The

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:

Is a 95% CI on a breakpoint fixed effect legitimate when the nonlinear equation is continuous but not differentiable at the breakpoint? I used the nlme to generate a non-linear mixed-effects piecewise model with initial slope zero prior to an unknown breakpoint. It did give a 95% CI for the breakpoint. Thanks Erik whole model is complex, but here is the simple equation used:

Dear friends, I used the B-spline (degree=1) method to fit the piecewise linear function and the results are listed below. m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24)) +bs(y,degree=1,knots=c(-0.4357,-0.3202 )),family=binomial(logit),data=point) summary(m.glm) Coefficients: Estimate Std.

I am looking into fitting a so-called double von Bertalanffy function to fish length-at-age data. Attempting to simplify the situation, the model looks like this ... Y ~ f(X; a,b,c) if x < Z Y ~ g(X; a,d,e) if x >= Z where * f and g are non-linear functions (the "traditional" "single" von Bertalanffy growth function), * Y (length) and X (age) are observed variables,

How can I fit a piecewise continuous logistic regression with a single free knot (i.e. the knot is not specified; the model produce an estimate of the value of the knot). Thank you, John John David Sorkin M.D., Ph.D. Professor of Medicine Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine Baltimore VA Medical Center

Dear R-helpers, a S-code for piecewise regressions was provided by Toms & Lesperance (2003) Ecology, 84, 2034-2041 (paper can be found on the web).The code is quite complete with different types of transitions around breakpoints and model selection fonctions. It doesn't work directly under R due to some "translation" problems I guess. However I reckon that it would be a

Hi there, I´m a newbie in R and I´m looking for some advice how to teste Piecewise (Broken Stick) models. I´have another simple question: how can I compute descriptive statistics for data grouped for two or more variables? See below, please. Year Site Repetition rainfall 1980 1 1 ... 1980 1 2 ... 1980 1 3 ... 1980 2

Hi everyone, I'm trying to fit a of piecewise regression model on a time series. The idea is to divide the series into segments and then to apply linear regression models on each segment but in a "global way" and considering heteroskedasticity between the segments. For example, I build a time series y with 3 segments: segment1=1:20+rnorm(20,0,2) segment2=20-2*1:30+rnorm(30,0,5)

Dear all, I want to do piecewise CAPM linear regression in R: RRiskArb−Rf = (1−δ)[αMktLow+βMktLow(RMkt−Rf)] + δ[αMkt High +βMkt High(RMkt −Rf )] where δ is a dummy variable if the excess return on the value-weighted CRSP index is above a threshold level and zero otherwise. and at the same time add the restriction: αMkt Low + βMkt Low · Threshold = αMkt High + βMkt High · Threshold to ensure

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

I'm looking for software that makes plots such as fig 4 (a)-(e), fig 5 anf fig 7 of Gray, Robert, "Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis," 1992, J Am Stat Assoc, pp 942-51. In other words, I'm looking for software that takes survival data and a continuous covariate as input and computes a curve giving log hazard

We sent questionnaires to lots of folks. The questionnaires differed in word count (wc). For each questionnaire, I know wc and whether the questionnaire was returned or not (status). It looks like there is a fixed rate of return when wc is below a break point, and the rate of return falls off linearly after that. I want to do a piecewise linear regression with two pieces meeting at a common

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

On Oct 27, 2013 2:16 PM, "David Nadlinger" <code at klickverbot.at> wrote: > > The following piece of IR is a fixed point for opt -std-compile-opts/-O3: > > --- > target datalayout = > "e-p:64:64:64-S128-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f16:16:16-f32:32:32-f64:64:64-f128:128:128-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64"

Dear All, I am trying to simulate data for a spline/piecewise regression model. I am missing something fundamental in my simulation procedure because when I try to fit my simulated data using the Gauss-Newton method in SAS, I am getting some wacky parameter estimates. Can anyone please check my simulation code and tell me what mistake I am making in generating data for spline model? Thank you

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