similar to: Estimation in a changepoint regression with R

Hi, all, I'd like to find one or more changepoints in a dataset eg for data like 0.9 1 0.8 0.8 0.85 0.3 0.9 0.9 0.9 1.1 0.3 is unlikely a changepoint but for data like 0.9 1 0.8 0.8 0.85 0.3 0.2 0.2 0.1 0.2 0.3 0.3 is a change point I am wondering is there any function can do changepoint analysis? Ying Fan

Hello Does anyone know how to get the p values for changepoints in the "changepoint" package? There is guidance in the CRAN vignette https://cran.r-project.org/web/packages/changepoint/changepoint.pdf but I can't make it work to get the p value out... Thanks Nick Wray [[alternative HTML version deleted]]

I am trying to understand my population abundance data and am looking into analyses of change point to try and determine, at approximately what point do populations begin to change (either decline or increasing). Can anyone offer suggestions on ways to go about this? I have looked into bcp and strucchange packages but am not completely convinced that these are appropriate for my data. Here is

Hello, I would like to evaluate the relationship between flows and phytoplankton abundance (or Chlorophyll a concentrations) using a changepoint analysis.? Specifically, I have two study questions: Study Question 1: Are there certain flow thresholds that result in spikes in phytoplankton abundance? Study Question 2: Are the duration of certain flows important for phytoplankton abundance (e.g.,

Dear All, I was looking for the color index in?image function,?such as from topo.colors(n) and etc. but still never found it. For instance, from the help menu. ########################################### # Volcano data visualized as matrix. Need to transpose and flip # matrix horizontally. image(t(volcano)[ncol(volcano):1,]) # A prettier display of the volcano x <- 10*(1:nrow(volcano)) y

Dear All, Let: dp: depth of the?river tp: temperature with respect to depth We can?have a simple scatter plot, between depth as y-axis and temperature as x-axis,?by using a?plot function as shown?below. #####################? dp <- c(1,4,3,2,5,7,9,8,9,2) tp <- 1:10 plot(tp,dp, type= 'l') ##################### ? Could someone advice me on?the way to?plot the same pair of

Dear All, I have a series of data in which the first column consist of a combination of date and time, for instance 17 April 2008 at 4.01pm, such data is recorded as: 4/17/2008 16:01 I'd like to seperate it into four different columns which consist of Day, Month,Year and Time, respectively. Could someone please advice me on this mater? Thank you, Fir

Dear All, Let a, b and c are?three matrices with same no. of column but different no. of row. a <- matrix(1, 1, 2) b <- matrix(2, 2, 2) c <- matrix(3, 3, 2) Could someone help me to combine these matrices together as a single matrix? Thank you Fir

Hello The other say I posted a question about altering confidence limits in the seqMK() function in the pheno package. I didn't get any bites but in any case I've been trying to use this function. It seems to me to give very odd results - sometimes identifying 8 or 9 changepoints in a relatively short vector of maybe 40 elements, and at other times not giving any changepoints at all,

Dear All,   Could someone please give some advice the way to do linear modelling via best subset regression in R? I'd really appreciate for your kindness.   Thanks, Kagba [[alternative HTML version deleted]]

Hi, I have some data that have this behaviour: | |******* | * | * | * | * |---------------- What is the best and simpler way to fit this in R? Thanks Ronaldo -- Ela pilotava um Continenal 2001 com igni????o autom??tica Magiclic... -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior ] |>

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Dear All, Could anyone?give me a hand?to suggest few packages in R to running Kalman prediction and filtration ? Thanks Fir

Hi,   Could any one please help how I can transform data based on Box-Cox Transformations in R.   Any helps will be much appreciated.   thanks, Kagba [[alternative HTML version deleted]]

Dear All, I'm sorry if my question does not suit with this R group. I have recently installed?R software with version 2.9.2, but i found the program took almost 1 minute as soon as it was opened, before it can be used. However,?the previous version 2.9.1 only take few seconds?after the menu bar was clicked. This circumstance has caused me to wait for couple of minutes as several R windows

Dear All, ? Let: dp: depth of the river tp: temperature with respect to depth These pair of observations are in 3 different groups i.e: Obs. 1,3,5,7?from the first group Obs. 2,4 and?10?from second group Obs 6,8 and 9?from third group. We can have a simple scatter plot, between depth as y-axis and temperature as x-axis, with each pairs are denoted by a red dot, by using a plot function shown

Dear friends. I would like to link an object file made in C. Dyn.load rather wants to link a whole library and I only wanted to link this small object file. I followed instructions given for S - by Clive Loader in his software for changepoint handling and it is his object file I made from http://cm.bell-labs.com/cm/ms/departments/sia/clive/smcp.shar Any dirty tricks or easy oversights ? Yours

I am looking for R code to be able to fit a linear-linear piecewise model with person-specific changepoint. I have searched the web, but have not been able to locate any code. Below is my attempt at some code: chgpt = function(a1,a2,a3,gam,wave){ yht=numeric(10) y1=(wave <= gam)*(a1+(a2*wave)) y2=(wave > gam)*((a1+(a2-a3)*gam)+a3*wave) yhat=y1+y2 return(yht) } nl.dat <- nlme(y ~

Dear All, I have small samples of data (between 6 and 15) for numerious time series points. I am assuming the data for each time point is normally distributed. The problem is that the data arrvies sporadically and I would like to detect the number of outliers after I have six data points for any time period. Essentially, I would like to detect the number of outliers when I have 6 data points then

Hi to all, I am trying to run breakpoints() on a fairly large sample (>10.000 observations). The process is very slow, any idea on how to speed this up? I have tried the hpc="foreach" parameter, but this didn't work at all when I tried to run it on a smaller sample. breakpoints(x ~ x.l1 + x.l2 + X.l3 + x.l4 + x.l5 + x.l6 + x.l7 + x.l8 + y.l1 + y.l2 + y.l3 + y.l4 + y.l5 + y.l6