similar to: Problem with tsboot

Dear R-users, tsboot fails when I try to perform a block bootstrap on seaKen (package wq): these commands: require(wq) require(datasets) boot.block.sen <- function(data){seaKen(data)[[1]]} tsboot(sunspot.month, boot.block.sen, R=1999, l=12, sim="fixed") return: Error in seaKen(data) : x must be a 'ts' Any suggestion on how might I change seaKen in order to use it with

Hello everybody, when I create an object of class boot with the function tsboot() from the package boot and try to compute several types of confidence intervals with boot.ci("object of class boot created with tsboot") I obtain the warning message, that "BCa-intervals are not defined for time-series bootstraps". Does that hold in general? Or is it just not defined in

Does anyone know how p-values can be generated if tsboot (stationary bootstrap) for time series is performed? That would be of great help. Thanks a lot for your comments. Markus [[alternative HTML version deleted]]

Fritz, I have slightly adapted (didn't work before) "tsboot" from the "boot" library to the current time series conventions of R. The following patch will do that. I suggest to apply this patch to the file "boot/R/bootfuns.q" of the "boot" library at CRAN. best Adrian --- bootfuns.orig.q Thu Dec 9 10:07:23 1999 +++ bootfuns.q Thu Dec 9 10:06:51 1999

Fritz, I have slightly adapted (didn't work before) "tsboot" from the "boot" library to the current time series conventions of R. The following patch will do that. I suggest to apply this patch to the file "boot/R/bootfuns.q" of the "boot" library at CRAN. best Adrian --- bootfuns.orig.q Thu Dec 9 10:07:23 1999 +++ bootfuns.q Thu Dec 9 10:06:51 1999

Dear R Users, If a stationary bootstrap (Politis & Romano 1994) for time series is performed (e.g. performance difference between trading strategy and benchmark), how can the following data be generated respectively adjusted? 1. p-values 2. smoothing parameter 3. significance levels 4. block lengths Please let me know. Thanks a lot. Best regards, Markus

I have the following R code to create a plot with two y axes. I am essentially trying to plot a price series with a volume series on the same graph. (i.e. to compare price with volume). I can label the first y axis successfully, but the problem is in labeling the 2nd y-axis. Essentially, the label never appears. It seems as though there is not enough margin on the right hand side of

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this

Hi, I just noticed one of my XEN domU''s rebooting by itself. No one was logged in and it wasn''t under load, but just rebooted. It rebooted again about an hour or 2 later Is there any way to see why to see what caused this, either from domU, or dom0 ? /var/log/messages has no indicative reason, but here it is: Aug 1 18:47:06 venus shutdown[13758]: shutting down for system

I'm using zoo to plot multiple data series, however, I am having trouble adjusting the x-axis labeling on a multiple series plot. For example, if I create a zoo object that consists of a date series and a numerical series and then plot it, I can adjust the x axis labeling using axis.Date(1, at=seq(as.Date("1984/01/31"), as.Date("2005/10/31"), by="2

On Monday, April 4, 2016, 14:01:42, Thomas Schmitt via Syslinux wrote: > What software and options did you use to produce the ISO ? Since I'm on Windows, I ended using Microsoft's oscdimg, with the following command line: oscdimg.exe -a -m -o -j1 -bootdata:2#p0,e,bcd\isolinux\boot.bin#pEF,e,bcd\boot\efiboot.ima CD sysresccd.iso I haven't tried booting the resulting ISO in BIOS

Hi all, I hope this is possible. I have a server, with a network switch attached to the serial port, and I want to rescan the serial port (/dev/tty) to see if it picks it up? I'd prefer not to reboot the server if at all possible. dmesg reports ttyS0 as the active port: [root at zabackup01 ~]# dmesg |grep tty Bootdata ok (command line is ro root=/dev/System/root rhgb quiet xencons=tty6)

Good morning, I need to use pciback.hide to hide a pci card because I want to pci passthrough that card to a domU. My hardware neither supports Intel-Vt-d nor AMD's IOMMU. Unfortunately this doesn't seem to work in Centos 54 anymore. I guess it doesn't work because it is not compiled into the kernel, but as a module. I got the following error in dmesg and the pci device is still

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

Folks, I have a time-series of 875 readings of the weekly returns of a stock market index (India's Nifty). I am interested in the AR(1) coefficient. When I do arima(r, order=c(1,0,0)) I get a statistically significant AR1 coefficient. If we apply the ordinary bootstrap to this problem, this involves sampling with replacement, which destroys the time-series structure. Hence, if we do

Hello, I am have fitted GLS models to time series data. Now I wish to bootstrap this data to produce confidence intervals for the model. However, because this is time series data, normal bootstrapping is not applicable. Secondly, 'tsboot' appears to only be useful for ar models - and does not seem to be applicable to GLS models. I have written code in R to randomly sample blocks of

Hello everybody, I have the following problem: I read a multiple time series (along with column names), and then need to manipulate the univariate components separately in order to compute the statistics of interest. Can someone tell me how can I access the univariates separately through their names ? I need to do it on a ts object (and not data frame, where I know how to), for the sake of later

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the