similar to: Kalman Filter

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? -

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?

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 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

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:

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

Hi, I am studying Kalman Filter and it seems to be difficult for me to apply the filter on a simple ARMA. It is easy to construct the state-space model, for instance: dlmModARMA(ar=c(0.4,-0.2),ma=c(0.2,-0.1, sigma2=1) but applying the dlmFilter on it, it doesn't work... I don't know if my problem is clear but if anyone has already worked on Kalman filter, it could be great to advise me!

Dear R users, Just to clarify. I am not offering to pay someone to do my Dissertation. These 4-5 commands on Kalman Filter would be only a tiny part of my 10,000 words dissertation. A part that even after trying for a few days, I am still stuck on. I am offering ?50, just to say thanks. Regards -- View this message in context:

If you mean this paper by Fahrmeir: http://biomet.oxfordjournals.org/cgi/content/abstract/81/2/317 I would recommend BayesX: http://www.stat.uni-muenchen.de/~bayesx/. BayesX interfaces with R and estimates discrete (and continuous) time survival data with penalized regression methods. If you are looking for a bona fide Bayesian survival analysis method and do not wish to spend a lot of time

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,

Hello all, I have a very large file (280k lines) containing three comma separated variables. The first variable is a 0 or 1 depicting a pass or fail. The other two are X and Y coordinates. Is there a good way I can represent this data in a chart/plot form other than using a 3d histogram? If I need to use the histogram, should I base my chart off the example contained in the RGL package? Thanks a

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

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]]

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

Dear All, Could anyone?give me a hand?to suggest few packages in R to running Kalman prediction and filtration ? Thanks Fir

Hi, I'm trying to use the kalman filter to estimate the variable drift of a random walk, given that I have a vector of time series data. Anyone have any thoughts on how to do this in R? Thanks, Alex [[alternative HTML version deleted]]

Hello My name is greigiano am student of Applied Economics, Department of Rural Economy. I am working on an article forecasting, which use the dynamic linear model, a model state space. I am wondering all the commands in R, to represent the linear dynamic model and Kalman filter. I am available for any questions. -- DEUS Seja Louvado Que ELE Ilumine sua vida Assim como ELE tem Iluminado a Minha

Hi Does anyone know of any R code for estimating a *multivariate* state space model using a Kalman filter where the output matrix H(t) is time-varying but predictable (i.e. measurable w.r.t information at time t-1) in the observation equation y(t) = H(t) z(t) + R w(t)? [Here y(t) are the observations, z(t) is the state variable, w(t) the observation error and R R' the observation error

Dear all, I am currently trying to use the "dlm" package for Kalman filtering. My model is very simple: Y_t = F'_t Theta_t + v_t Theta_t = G_t Theta_t-1 + w_t v_t ~ N(0,V_t) = N(0,V) w_t ~ N(0,W_t) = N(0,W) Y_ t is a univariate time series (1x1) F_t is a vector of factor returns (Kx1) Theta_t is the state vector (Kx1) G_t is the identity matrix My first

Hi, Is there a Branch and Bound routines or library for R?. Thanks, -Mohamed -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch