similar to: Question on graphs and simple if statements

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

I would like to create a page of two graphs (2 rows by 1 col) and then draw vertical lines (segments?) on both graphs from the minimum values to the corresponding maximum value. So I have tried # > y <- rnorm(3000) > par(mfrow=c(2,1)) > plot(y,type="l") > plot(cumsum(y),type="l") > segments(1000,min(cumsum(y)),1000,max(cumsum(y))) > par(mfg=c(1,1)) >

Hello, I would like to submit two papers that use LLVM to the Related Publications section. Both papers focus on code isolation applied to perform piecewise compiler optimizations. The code isolation process is performed by CERE, an open source tool based on LLVM. The second paper is an extended version of the first one. 1) Piecewise Holistic Autotuning of Compiler and Runtime Parameters

Dear Mihail, I've added these two publications to the publications page. Please review it and let me know if I need to make any changes. In particular, if you have URLs to use for the papers, having those would be greatly appreciated. Regards, John Criswell On 11/28/17 12:05 PM, Mihail Popov via llvm-dev wrote: > > Hello, > > I would like to submit two papers that use LLVM

To whom it may concern, 1. I would like to know if there is a command in R to choose a function for a given dataset. I am facing a difficult question about how to fit the data [ please attachment ] 2. Are there any function that will allow users to do the piecewise regression? If not, is it going to be provide in the new updated version? What I am using is the piecewise logistic regression,

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

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 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

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

Dear Forum, I have been wracking my head over this problem for the past few days. I have a dataset of (x,y). I have been able to obtain a nonlinear regression line using nls. However, we would like to do some statistical analysis. I would like to obtain a confidence interval for the curve. We thought we could divide up the curve into piecewise linear regressions and compute CIs from those

DMA-related logic is separated from the virtqueue_add_split() to one new function. DMA address will be saved as sg->dma_address if use_dma_api is true, then virtqueue_add_split() will use it directly. Unmap operation will be simpler. The purpose of this is to facilitate subsequent support to receive dma address mapped by drivers. Signed-off-by: Xuan Zhuo <xuanzhuo at linux.alibaba.com>

DMA-related logic is separated from the virtqueue_add_vring_split() to prepare for subsequent support for premapped. DMA address will be saved as sg->dma_address, then virtqueue_add_vring_split() will use it directly. If it is a premapped scene, the transmitted sgs should have saved DMA address in dma_address, and in virtio core, we need to pass virtqueue_map_sgs(). Signed-off-by: Xuan Zhuo

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 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 \ \ /\

Does anyone have any suggestions on perspective plotting of piecewise constant functions? Ideally, I would like something like plot.stepfun for functions that are piecewise constant on polygons. Even pointers to non-R strategies would be welcome at this stage. url: www.econ.uiuc.edu Roger Koenker Dept. of Economics UCL, email rkoenker at uiuc.edu Department of Economics Drayton House, vox:

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 =

On Fri, Nov 3, 2017 at 5:39 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > On 11/03/2017 07:20 PM, Alexandre Isoard via llvm-dev wrote: > > Hello, > > I am not sure about the semantic (if any) of returns_twice and noreturn > attributes. > > int fork() __attribute__((returns_twice)); > void join(int) __attribute__((noreturn)); > > int f(int n) { > int

Dear Rusers, I am now using R and SAS to fit the piecewise linear functions, and what surprised me is that they have a great differrent result. See below. #R code--Knots for distance are 16.13 and 24, respectively, and Knots for y are -0.4357 and -0.3202 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

On Tue, Feb 14, 2023 at 3:27 PM Xuan Zhuo <xuanzhuo at linux.alibaba.com> wrote: > > DMA-related logic is separated from the virtqueue_add_split to prepare > for subsequent support for premapped. The patch seems to do more than what is described here. To simplify reviewers, I'd suggest to split this patch into three: 1) virtqueue_add_split_prepare() (could we have a better