similar to: Peak Over Threshold values

Hi, I am trying to find m breakpoints of a linear regression model. I used the segmented package. It works fine for small number of predicators and breakpoints.(3 r.v. 3 points). However, my model has 14 variables it even would not work even for just one breakpoints!. The error message is always estimated breakpoints are out of range. Since my problem is time related problem. So I

Dear List, I have a data frame with approximately 500000 rows that looks like this: ?Date??? time??? value ? 19.07.1956????????? 12:00:00?????????????? 4.84 19.07.1956????????? 13:00:00?????????????? 4.85 19.07.1956????????? 14:00:00?????????????? 4.89 19.07.1956????????? 15:00:00?????????????? 4.94 19.07.1956????????? 16:00:00?????????????? 4.99 19.07.1956????????? 17:00:00?????????????? 5.01

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04

Hi, I have a set of x, y points where x represents dates and y actual values. I am trying to plot a line graph of the data with points on top, but R is connecting the wrong points with lines. Does anyone know how I can rectify this. Please see sample below: x= 24/09/2009 09:13 16/10/2009 11:17 24/10/2009 21:43 11/09/2009 18:34 22/08/2009 15:45 10/08/2009 00:30 14/08/2009 14:52 24/09/2009

Hello, and please advise regarding any errors/omissions on my part. However, the documentation for R's tcplot function in the evd package appears to contain an error. I am using evd version 2.2-4 in R 2.14.0 with Windows XP. > data(portpirie) > mrlplot(portpirie) ## No Error > tlim <- c(3.6, 4.2) > tcplot(portpirie, tlim) ## Line from documentation Error in fpot(data, u[1],

Hi all, Hope you people do not feel irritated for repeatedly sending mail on Time series. Here I got another problem on the same, and hope I would get some answer from you. I have following dataset: data[,1] [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 4.98 4.99 4.99 5.00 5.01 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03

I just saw this thread in the OLM forum. Possibly of interest, since they say it includes CentOS 5, if the update hasn't been released yet. Always good to keep boxes updated, for security and stability reasons. <http://forums.olm.net/showthread.php?t=2137>

Hi all, I wrote a function that actually does what I want it to do, but it tends to be very slow for large amount of data. On my computer it takes 5.37 seconds for 16000 data points and 21.95 seconds for 32000 data points. As my real data consists of 18000000 data points it would take ages to use the function as it is now. Could someone help me to speed up the calculation? Thank you, Tonja

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine >

Hi all, I used contour() to add contour lines to a plot. Now I?m wondering if there is a way to get an output of the calculated x- and y- coordinates of the contour lines? Tonja

Hi all! I'm sure this is a stupid question but I can't find an answer. How can I fit the Gumbel distribution to my data using The Method of Moments in R? Thank you for helping me, Tonja

Dear all, I have a question concerning the ks.test() function. I tryed to calculate the example given on the German wikipedia page. xi <- c(9.41,9.92,11.55,11.6,11.73,12,12.06,13.3) I get the right results when I calculate: ks.test(xi,pnorm,11,1) Now the question: shouldn't I obtain the same or a very similar result if I commpare the sample and a calculated sample from the distribution?

Dear List, I am creating a plot and I want to insert the tabular data below the X axis. I mean for every value of X I want to show the value in Y as a table below the plot. I think the attached image gives an idea of what I mean by this. Below is the code i am using now... but as you see the Y values don't have the right location. Maybe I should insert them as a table? Any ideas on that.

I tried toy problems and there doesn't seem to be a basic problem between lm() and ts objects: X = data.frame(x=c(1,2,7,9), y=c(7,2,3,1)) lm(y ~ x, X) X <- lapply(X, function(x) ts(x, frequency=12, start=c(1994,7))) lm(y ~ x, X) and this works fine - whether you do an lm() before or after making ts objects, it's okay. But I have a situation where things aren't okay.

Dear R-helpers ? I have 7 data points that I want to fit a continuous curve to, that should look similar to a sine wave My data points would mark the local minima and maxima respectively. This is what I?ve got so far. And I would keep doing so, but sadly nls() then says that it has reached the maximum number of Iterations? ?

Dear R-users, I am trying to understand how the 'segmented'-package works to determine breakpoints and slopes of regression lines in broken-line regression models. However, I am not able to repeat the example on the "plant"-dataset, which was reported in the accompanying paper of the package. (V.M.R Muggeo, "Segmented: an R package to fit regression models with