similar to: Question on KalmanSmooth

On Sat, 03 Apr 2004 21:34:25 -0400, you wrote: >Defining the following: > > set.seed(123) > > kalmanTorture <- function(iter) { > x <- arima.sim(model = list(ar=0.9, ma=0.5),n=150 ) > x[10:20] <- NA > mod <- arima(x, order=c(1,0,1) ) > for (i in 1:iter) { > smooth <- KalmanSmooth(x, mod=mod$model)$smooth > if (any(is.na(smooth)))

Hello, In R I am trying to use Kalman filtering to find a solution for an hydrological problem. With Kalman Filtering I want to estimate the discharge comming from three storage bassins. I have programmed a function in R which can run KalmanSmooth. When I'm asking for the function and putting in values, R detects the following error: "Error in as.vector(data) : Argument "S1" is

Having a ModulePass that requires a FunctionPass that in turn requires another ModulePass results in an assertion being fired. Is this expected behavior (that seems to be undocumented), or a bug? Specifically, the following code will emit the assertion: [VMCore/PassManager.cpp:1597: virtual void llvm::ModulePass::assignPassManager(llvm::PMStack&, llvm::PassManagerType): Assertion

Hi My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am trying to implement Kalman Filter into my school work. I have some problems with understanding of R version of Kalman Filter in package stats( functions KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). 1) Can you tell me how are you seting the initial values of state vector in Kalman Filter? Are you using some method?

"A module pass can use function level passes (e.g. dominators) using getAnalysis interfacegetAnalysis<DominatorTree>(Function), if the function pass does not require any module passes." http://llvm.org/docs/WritingAnLLVMPass.html In your case, A module pass (ModPass2) is trying tu use function level pass (FunPass1) which uses module level pass (ModPass1). This is not

Hola! I got problems using an objects returned from arima (in KalmanSmooth(my.ts, ModArima$model), because my.ts showed up to have storage mode "integer" (is.integer(my.ts was TRUE). Should storage.mode() of a ts be allowed to be integer, should ts() someplace say storage.mode(ts.out) <- "double", or maybe inside arima() storage.mode(x) <- "double"

Hi, I''m new to puppet and trying to get started using it just in local mode. My basic question is: how can i reference a static file within the module (eg in the module''s files/ subdirectory, as described in http://reductivelabs.com/trac/puppet/wiki/ModuleOrganisation). I suppose i could try using a full.absolute path to the file, but that seems to completely defeat the purpose

Hi it is possible to see the C code for the KalmanLike and Kalmansmooth functions with R? Otherwise, without using R, how can I get the code? Thank arianna

In the following scrap of code (pared down from actually useful code), the func->dump() command segfaults iff the commented-out line is uncommented. This is with llvm 3.0. I'm only dipping my toes into the waters of llvm for the first time, and have no idea what I am doing incorrectly. In actual code, I would be wanting to call the memcpy intrinsic eventually, of course. int main(void)

>From the help document on KalmanLike, KalmanRun, etc., I see the linear Gaussian state space model is a <- T a + R e y = Z' a + eta following the book of Durbin and Koopman. In practice, it is useful to run Kalman filtering/smoothing/forecasting with exogenous factor: a <- T a + L b + R e y = Z' a + M b + eta where b is some known vector (a function of time). Some other

Hi R-Experts, Currently I'm using an univariate time series in which I'm going to apply KalmanLike(),KalmanForecast (),KalmanSmooth(), KalmanRun(). For I use it before makeARIMA () but I don't understand and i don't know to include the seasonal coefficients. Can anyone help me citing a suitable example? Thanks in advance. ------------------------------------------

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

I have a bunch of clean timeseries data obtained from a sensor and I'd like to apply a Kalman Filter to it to smoothe it out. Through a few days of Googling, reading papers, implementing such a filter in various languages, I finally realised that it may be built into R. So I did a "??kalman" at the R prompt and found that it is indeed there. However, the help page is a tad bare,

https://bugs.freedesktop.org/show_bug.cgi?id=51207 Bug #: 51207 Summary: Background corruption in Firefox w/ cairo Classification: Unclassified Product: Mesa Version: 8.0 Platform: x86-64 (AMD64) OS/Version: Linux (All) Status: NEW Severity: normal Priority: medium Component:

Hello everyone, Since my question is quite theorical, I am not sure whether it is the right place to ask, but anyway... I am working on time series and I looked at some way to fit my data through arima models. Since these data are updated frequently, I was looking at a way to update the model "on line" (to get a kind of recursive estimation) So the next step was to express the arima

Good to know. I was referencing a local copy of 2.3 docs which didn't include the "does not require any module passes" statement. It appears the docs were changed two days before 2.4 was released in November. I suppose I should update my docs more often. Are there any plans to change this restriction, or any best practices to get similar behavior? Since immutable pass is a subclass

I have a shorewall 2.0.14 running on a single interface machine (nwww in the log below) that is attempting to be well screwed down. The policy file reads:- #SOURCE DEST POLICY LOG LEVEL LIMIT:BURST fw net DROP info net all DROP info # The FOLLOWING POLICY MUST BE LAST all all

Hi, Few days ago I posted a question to r-sig-finance, which I thought would be an easy one. To my surprise I have received no replies, which makes me think that it is either harder than I thought, or that it makes no sense. I am reposting the message (with some modifications) on the R-help in a hope to get some leads, suggestions for alternatives, etc. My apologies to those who had seen this on

Anyone know how to esimate parameters in the system: x[k]=Ax[k-1]+ B + Gv[k-1] y[k]=x[k]+Du[k]+Hw[k] a system with exogenous u[k] in the measurement eq., v,w are iid, both eq. are gaussian. Thanks, Oyvind --------------------------------- [[alternative HTML version deleted]]

Hi *, Firstly, thank you so much for your time to read my email. I am currently interested in how to use R to predict time series from models fitted by ARIMA. The package I used is basic stats package, and the method I used is predict.Arima. What I know is that ARIMA parameters are estimated by Kalman Filter, but I have difficulty in understanding how exactly maximum likelihood (ML) estimator