search for: 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(rnorm(501)) > BootData <- data.frame(cbind(SpecPress, Returns)) > boot.specpress <- function(data, indices, maxit=20){ + data <- data[indices,] + mod <- dyn$lm(SpecPress~lag(Returns,-1)+lag(SpecPress,-1)) + coefficients(mod) + } > > system.time(SpecPress.boot <- tsboot(BootData, boot.specpress, R=1000, maxit=100)) user system elapsed 46.007 1.544 65.439 > > SpecPress.boot MODEL BASED BOOTSTRAP FOR TIME SERIES Call: tsboot(tseries = BootData, statistic = boot.specpress, R = 1000, maxit = 100) Bootstrap Statistics : original b...

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]]

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...

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 +++ boot...

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 +++ boot...

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 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 error message from R: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Can someone tells me what's wrong in the programming.   Thank you.   Regards, Lim...

...rgs$fit coeff <- ran.args$paras ts$temp <- fit + coeff[4]*arima.sim( model=list(ar=coeff[1]), n=n.sim,rand.gen=white.noise,ts=tsb ) ts } new.beaver <- beaver.gen( beaver, 100, beaver.args ) beaver.fun <- function(ts) fit(ts)$paras beaver.boot <- tsboot( beaver, beaver.fun, R=99,sim="model", n.sim=100,ran.gen=beaver.gen,ran.args=beaver.args) names(beaver.boot) beaver.boot$t0 beaver.boot$t[1:10,] Maybe there is a more elegant way for doing this. Anyway, boot.ci should give you confidence intervals. Let me know how you a...

...d 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 use of tsboot. Thanks for help rg, Konrad

.... I used "trend.test" function (library=pastecs) and I did: >trend.x=trend.test(x[-c(4,10)],R=999) >trend.x >trend.x$p.value >plot(trend.x) And I get: > trend.x=trend.test(x[-c(4,10)],R=999) > trend.x BLOCK BOOTSTRAP FOR TIME SERIES Fixed Block Length of 1 Call: tsboot(tseries = x, statistic = test.trend, R = R, l = 1, sim = "fixed") Bootstrap Statistics : original bias std. error t1* 0.9818182 -0.9844001 0.3272114 > trend.x$p.value [1] 0 I suppose that the problem arises from the length of the block (1) and in this way I get a rho=0...

.... I used "trend.test" function (library=pastecs) and I did: >trend.x=trend.test(x[-c(4,10)],R=999) >trend.x >trend.x$p.value >plot(trend.x) And I get: > trend.x=trend.test(x[-c(4,10)],R=999) > trend.x BLOCK BOOTSTRAP FOR TIME SERIES Fixed Block Length of 1 Call: tsboot(tseries = x, statistic = test.trend, R = R, l = 1, sim = "fixed") Bootstrap Statistics : original bias std. error t1* 0.9818182 -0.9844001 0.3272114 > trend.x$p.value [1] 0 I suppose that the problem arises from the length of the block (1) and in this way I get a rho=0...

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 the data (as in Davison & Hinkley's book - bootstrap methods and their application) and use this resampling to re-run the model, but this d...

...ervals on Original Scale --------------------------------------------------------------------------- I find it very strange that the 95% confidence interval runs from 0.076 to 0.2109. I had expected that it should be symmetric about 0. What am I missing? As an aside, how would you set about using tsboot() to obtain inference for this AR(1) coefficient? -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org http://ajayshahblog.blogspot.com <*(:-? - wizard who doesn't know the answer.

...N TO "Automatic block-length selection for the dependent bootstrap" by D. Politis and H. White?, Econometric Reviews 28(4), 372-375. The function b.star is now used throughout the np package for automated block length selection for the stationary bootstrap (via the `l = ' argument in tsboot from the boot package). Examples are provided for each function at the end of the respective man pages. We hope you find the functions to be easy to use. Kindly report any bugs, suggested improvements and the like to racinej at mcmaster.ca -- Jeff Professor J. S. Racine Phone: (905) 525...

...N TO "Automatic block-length selection for the dependent bootstrap" by D. Politis and H. White?, Econometric Reviews 28(4), 372-375. The function b.star is now used throughout the np package for automated block length selection for the stationary bootstrap (via the `l = ' argument in tsboot from the boot package). Examples are provided for each function at the end of the respective man pages. We hope you find the functions to be easy to use. Kindly report any bugs, suggested improvements and the like to racinej at mcmaster.ca -- Jeff Professor J. S. Racine Phone: (905) 525...

....org> > Subject: Re: [R-es] Bootstrap > Message-ID: <F6374A99-AF9E-48E1-B951-6A845646456E@iberstat.es> > Content-Type: text/plain; charset=ISO-8859-1; delsp=yes; format=flowed > > Me parece que el block bootstrap es adecuado en tu situación. > Mira por ejemplo la función tsboot() del paquete "boot". > Un saludo. Olivier > -- > ____________________________________ > > Olivier G. Nuñez > Email: onunez@iberstat.es > Tel : +34 663 03 69 09 > Web: http://www.iberstat.es > > ____________________________________ > > > &gt...

Hola buenas En principio a mí no me parece una mala aproximación. Tal vez se podría intentar adaptar el problema a un modelo de supervivencia, pero tendría que pensarlo. ( https://vimeo.com/142732615 ) De todas maneras, creo que coges días al azar para calcular to "proxy". Aunque yo personalmente cogería días consecutivos porque probablemente el consumo en muchos productos no sea