similar to: C code for KalmnaLike

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?

hi, i am looking for c code of kalman filtering please can you help me...thankyou bye... --------------------------------- [[alternative HTML version deleted]]

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. ------------------------------------------

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

Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? -

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"

>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

1. The function "KalmanLike" seems to change its inputs AND PREVIOUSLY MADE copies of the inputs. Consider the following (using R 2.1.0 patched under Windows XP): > Fig2.1 <- StructTS(x=Nile, type="level") > unlist(Fig2.1$model0[2:3]) a P 1120 286379470 > tst2 <- tst <- Fig2.1$model0 > tst23 <- tst[2:3] > tst23u <-

I am trying to use KalmanSmooth to smooth a time series fitted by arima (and with missing values), but the $smooth component of the output baffles me. Look at the following example: testts <- arima.sim(list(ar=0.9),n=100) testts[6:14] <- NA testmod <- arima(testts, c(1,0,0)) testsmooth <- KalmanSmooth(testts, testmod$model) par(mfrow=c(2,1)) plot(testsmooth$smooth,

Hello, I'm trying to find out if something has been written in R regarding data assimilation and inverse modeling. These searches do not return anything that look like Kalman filter variations (EK, SEEK, ROEK, etc.) help.search("assimilation") help.search("inverse model") Regards, ************************************************** AVIS DE NON-RESPONSABILITE: Ce

I am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

I familarized myelf with kalmanlike and structts which are approaches for building and estimating ( and forecasting ) state space models ( or the equivalent arima models ). back in 2003, gavin simpson wrote an email describing the west and harrison apprach to estimate state space models and asked if anything was out there for using that approach. the goals of this approach are the same as kalman

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)))

Hola, Estoy intentando implementar el filtro de Kalman para un modelo de series de tiempo que estoy haciendo, me gustaría saber si alguien me puede colaborar ya que soy principiante en R. Muchas gracias! Cordialmente, JAVIER SANTIAGO PARRA RAMOS INGENIERO DE SISTEMAS [[alternative HTML version deleted]]

I am running Samba in AD mode with 3 Samba DCs. I am trying to verify that I really am seeing all incoming connections in the log files to help trouble shooting. We work with Sernet who are AWESOME people, especially Bjorn, but I was wondering if there were any other ideas. Right now we have "log level = 1 auth_audit:3 auth_json_audit:3" set in our smb.conf. Are there any other ways

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,

Hi, Following convention below: y(t) = Ax(t)+Bu(t)+eps(t) # observation eq x(t) = Cx(t-1)+Du(t)+eta(t) # state eq I modified the following routine (which I copied from: http://www.stat.pitt.edu/stoffer/tsa2/Rcode/Kall.R) to accommodate u(t), an exogenous input to the system. for (i in 2:N){ xp[[i]]=C%*%xf[[i-1]] Pp[[i]]=C%*%Pf[[i-1]]%*%t(C)+Q siginv=A[[i]]%*%Pp[[i]]%*%t(A[[i]])+R

Good morning, I have some problem adding ablines in Rstudio. The lines drew by the software doesen?t match with the added values, what can I do? pfaOK<-qcc(D[!trial], sizes=size[!trial], type="p", nsigmas=2, data.name="Polli positivi al Campylobacter") pfaOK1<-qcc(D[!trial], sizes=size[!trial], type="p", data.name="Polli positivi al Campylobacter")

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is:

help.search("Kalman") says to look at help(KalmanLike, package=ts). Andy -----Original Message----- From: Mohamed A. Kerasha [mailto:mohamed at engr.uconn.edu] Sent: Tuesday, November 19, 2002 9:27 AM To: r-help at stat.math.ethz.ch Subject: [R] Kalman Filter Hi all, Does any one know if there is Kalman Filter code or library in R. Thanks, Mohamed.