similar to: breakpoints and nonlinear regression

Can anyone please tell me how to get the r-squared output from a piecewise (segmented) regression using the strucchange package? Here is the R code I have tried thus far. library(lmtest) library(strucchange) data <- ts(c(rnorm(30), runif(30)), frequency = 12, start = c(2005, 01)) bpts <- breakpoints(data ~ 1) print(bpts) summary(bpts) coeftest(bpts) [[alternative HTML version

All, I currently have a dataset with a variety of different columns, let's say, A,B,C, and D. I'd like to run an R script that graphs only certain rows of the dataset based on what's in column A. In awk, it'd be something like: awk ' { if(A==5 && B ==6) print $0 } ' datafile | command or file Since I have just begun using R, I am unsure if awk is compatible in R

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 \ \ /\

On Apr 5, 2011, at 2:56 AM, Viktor Pavlu wrote: > On Mon, Apr 4, 2011 at 9:50 PM, Eric Christopher <echristo at apple.com> wrote: >> >> On Apr 1, 2011, at 6:53 AM, Viktor Pavlu wrote: >> >>> [...] Although most optimizations are turned off >>> already and the FastISel instruction selector is used, the "fast" path >>> for first-time

We're seeing OMPT tests fail on AArch64: libomp :: ompt/misc/control_tool.c libomp :: ompt/synchronization/master.c libomp :: ompt/synchronization/taskwait.c The failure mode is similar for all of them: openmp/runtime/test/ompt/misc/control_tool.c:26:17: error: CHECK-NEXT: expected string not found in input // CHECK-NEXT: {{^}}[[MASTER_ID]]:

Curious. I removed -fno-experimental-isel and all of the tests *except* control_tool.c passed. I would have expected all of them to pass if blockaddress works. I'll try to look at some asm and see what's going on. -David Jonas Hahnfeld <hahnjo at hahnjo.de> writes: > Hi David, > > I was the one who originally added the flag to fix failures

On Mon, Apr 4, 2011 at 9:50 PM, Eric Christopher <echristo at apple.com> wrote: > > On Apr 1, 2011, at 6:53 AM, Viktor Pavlu wrote: > >> [...] Although most optimizations are turned off >> already and the FastISel instruction selector is used, the "fast" path >> for first-time code generation is still the bottleneck [...] > > This is effectively

HI i'm trying to detect breaks points in various flow time series, they all contains seasonality and trend my question is : i have to remove this seasonality and trend before apply the function breakpoints du package strucchange?? another question, the function breakpoints is similar to de Pettit tests ? or how does it realy works? THANKS!!!! DENISSE -- View this message in context:

Hi, I am new to R. I am using strucchange to get the breakpoints in time series dataset. So the problem I am facing is: I want to link the result generated by the breakpoints to further analysis (for eg. generating volatility for each group). The result is in following form: --------------------------------------- > res <- gbreakpoints(GDP.new ~ 1,data=a,h=2,breaks=6) > res

If I try the breakpoints() function (strucchange package) with a minimum segment size = the number of regressors, there appears the following error message: "minimum segment size must be greater than the number of regressors" According to the documentation: "breakpoints implements the algorithm described in Bai & Perron (2003) for simultaneous estimation of multiple

Dear R-Team, Am I right supposing that the "breakpoints()" function in the strucchange package is an implementation of the pure structural change model proposed by Bai and Perron (1997, 2003)? My question relates to a partial structural change model that Bai and Perron formulate in their 2003 paper, e.g. formulated as y = x' beta + z' delta_j + epsilon, where beta and delta

Hello, we want to identify breakpoints (different phases) in environmental data, algae cell counts of three years with intervals between 7 and 30 days (N=40). We found that breakpoints(cells ~1) works great and identifies 5 very good breaks, however we are uncertain about these, because the data are unequally spaced. Is there a way to include the information about the measurement intervals,

Hi, I have recently use the strucchange package in R with a single time series observation. I found it extremely useful in the testing of change points. Now, I am thinking of using the strucchange package with panel data (about 500 firms, with 73 monthly time series observations each). For each firm, I have to conduct the Fstats and breakpoints tests. Based on the test of each firm, I have to

Hola! I am using package strucchange, and encounters the following: > bp <- breakpoints(diesel90 ~ regress -1, h=NULL) Error in La.chol2inv(x, size) : element (14, 14) is zero, so the inverse cannot be computed The obvious problems have been checked, that is, the model matrix is of full rank. What can be causing this? I can send some data if that can be of help. Kjetil Halvorsen

Hi All, I'd like to propose a fast path through code generation for x86-84 in the JIT execution engine as part of 2011's Google Summer of Code program. While the LLVM-JIT is very popular as a first try at jitting programming languages, projects have abandoned the LLVM-JIT when disappointed with the overall runtime. The problem is, that the benefit of faster execution is traded for longer

On Apr 1, 2011, at 6:53 AM, Viktor Pavlu wrote: > I currently work on generating fast cycle-accurate simulators[2]. For > this, our institute has implemented a two-part adaptive compilation > scheme using the LLVM-JIT. Although most optimizations are turned off > already and the FastISel instruction selector is used, the "fast" path > for first-time code generation is

Hello, I have done some research about breakpoints (I am not a statistician) and I found out about the breakpoint, strucchange and segmented packages in R allowing to find breakpoints assuming linear model. However, I would like to fit a periodic time series with a non linear (periodic) model, and I was wondering how I could find breakpoints for this model in R. Is it even possible ? My model

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 trying to a strucutural change analysis on a certain data set. Attached here, with variable name "xcd" http://old.nabble.com/file/p26226190/xcd.rda xcd.rda Am using the following command: bp.inrz<-breakpoints(INR~SPX+WPI,data=xcd,h=26) But keep on getting this error whatever variables i put in on the x side as regressors: Error in chol2inv(qr.R(qr(X))) : element (3,

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,