similar to: tsboot fails on Seasonal Mann-Kendall (seaKen function, wq package)

I'm trying to bootstrap some regression coefficients so that I can estimate confidence intervals, but boot is not producing results. Can anybody suggest what I'm doing wrong here? > SpecPress <- ts(rnorm(501)) > Returns <- ts(rnorm(501)) > BootData <- data.frame(cbind(SpecPress, Returns)) > boot.specpress <- function(data, indices, maxit=20){ + data <-

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

Dear useRs, I've been trying to run a Mann-Kendall test in my data in order to detect trends. I studied the examples given at the Kendall package and I can understand pretty well how it works on time-series data. However, my data consists of values in different sites per year, as I display below;              Year 1 | Year 2 | Year 3 | ... Site 1        x           x          x       ...

> R.version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 8.1 year 2008

Hi there, I'm trying to apply the Mann Kendall test for trend analysis of a time series. I have downloaded and installed the package Kendall and subsequently loaded it into the software. My time series is a .txt file with 2 columns - column 1 is the year (1985 - 2009) and column 2 is the corresponding entry variable. According to the R guidelines, the call should be: MannKendall(x)

Hello - does anyone know whether there are any packages for Mann-Kendall mutation tests in R available? The only one I could find online is this MK_mut_test: Mann-Kendall mutation test in Sibada/sibadaR: Sibada's accumulated R scripts for next probably use to avoid reinventing the wheel. (rdrr.io) <https://rdrr.io/github/Sibada/sibadaR/man/MK_mut_test.html> but there doesn't seem to

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

I configure as follows: ./configure --with-zlib=/usr/local/include cc -o sshd sshd.o auth-rhosts.o auth-passwd.o auth-rsa.o auth-rh-rsa.o sshpty.o sshlogin.o servconf.o serverloop.o auth.o auth1.o auth2.o auth-options.o session.o auth-chall.o auth2-chall.o groupaccess.o auth-skey.o auth-bsdauth.o auth2-hostbased.o auth2-kbdint.o auth2-none.o auth2-passwd.o auth2-pubkey.o monitor_mm.o

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

Dear R Fundation, I am a post-doc researcher at the University of Pisa, Italy. I apologize for my english and I have to tell you in advance that I am a very beginner with R. I used R for fitting dose-response curves (drc package) and for an ordinary ANOVA (one, two or three factors), including the post-hoc mean comparison (I used the LSD test...). Now I have to process some simple data on

May I recommend my new book on mathematical modeling to you, which is based on R as a main software tool: Kai Velten: Mathematical Modeling and Simulation, Wiley-VCH, 2009, ISBN 978-3527407583. See also: http://www.wiley.com/WileyCDA/WileyTitle/productCd-3527407588.html http://books.google.com/books?id=Czp1N5UWpyEC List of reviews below. The book covers a broad range of mathematical models

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