similar to: recursive multivariate filter with time-varying coefficients

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

Hi All, Does someone have an idea of how to cleverly convert a categorical timeseries into a transition matrix? Ie, I have something like: x<- c(1,1,2,1,1,2,2,2,1,2), And I want a matrix with counts and/or probabilities: > tr <- matrix(c(2,3,2,2),2,2) > tr [,1] [,2] [1,] 2 2 [2,] 3 2 Meaning that there are two transitions from 1 to 1, two from 1 to 2, three from 2 to 1

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

I'd like to estimate time varying coefficients in a linear regression using a Kalman filter. Even if the Kalman Filter seems to be available in some packages I can't figure out how to use it to estimate the coefficients. Is there anyway to do that in R? Any help appreciated Thanks

Dear useRs, I'm trying to create a barplot like so: x=matrix(1:10,2,5) barplot(x,leg=c("left","right"),besid=T) The legend is placed in default position topright, however the data are plotted there too. I tried controlling the legend position by adding x="topleft" but this results in an error that x matches multiple formal arguments. Leaving out the legend

Is there a package in R that handles general linear (in-)equality + box constrained optimization? If it is not there, could anyone advise me which way to go? And/or point me to packages that solve these problems partially? best, ingmar -- Ingmar Visser Department of Psychology, University of Amsterdam Roetersstraat 15, 1018 WB Amsterdam The Netherlands http://users.fmg.uva.nl/ivisser/ tel:

Hi, I was trying to repeat the estimation of threshold GARCH models from the book "Analysis of Financial Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2

Hi all, I'm writing a vignette for my package, and I would like to include some of the package help files in there as well. Is there an easy way of doing so? I tried using R CMD Rdconv to generate latex files from .Rd files but I am not sure how to include these into a .Rnw file (ie the vignette source). The resulting file from Rdconv do not readily compile using latex ... The other option I

I had written a vignette and included a \usepackage{Rd} command to make it possible to include latex'ed Rd files in the vignette. However, when loading Rd.sty texShop produces the following error: l. 180 ...d}[1]{\ifmmode\bm{#1}\else\textbf{#1}\fi} This is on Max OS X 3.9 and R 2.0.1 Has anyone seen this before and/or is it problematic? I'm not sure whether the output suffers from this

Hi all, I am working on Macos x 10.3 (Panther) to build a package consisting of C/C++ code that is called from R. In the C/C++-sources I use several commands to print info to the console: std::cout << "info" << endl; and: Rprintf("info\n"); Both work fine when R is run on the command line but neither works when running Raqau (I did check the preference boxes

gamlss.mx can fit these I believe (although no experience with these myself) flexmix may be (relatively easily) adaptable to accomplish this as well hth, Ingmar Ingmar Visser Universitair Hoofddocent ontwikkelingspsychologie | Directeur College Psychologie Afdeling Psychologie | Faculteit Maatschappij- en Gedragswetenschappen | Universiteit van Amsterdam Bezoek | Nieuwe Achtergracht 129B | Kamer

Dear R-helpers, I need to compute probabilties of multinomial observations, eg by doing the following: y=sample(1:3,15,1) prob=matrix(runif(45),15) prob=prob/rowSums(prob) diag(prob[,y]) However, my question is whether this is the most efficient way to do this. In the call prob[,y] a whole matrix is computed which seems a bit of a waste. Is there maybe a vectorized version of dmultinom which

Hi Tom, I am not entirely sure what the problem, you haven't been very specific. If you want general linear constraints on your parameters, ie linear combinations of parameters summing to some value, constrOptim may be of help. hth, ingmar Ingmar Visser Developmental Processes Research Group Department of Psychology University of Amsterdam http://users.fmg.uva.nl/ivisser/ -----Original

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

Hi all, How can a multivariate Poisson time series be modeled? Aspects of glm, forecast, dse and dynlm seem relevant but not quite complete--but hopefully what I am missing is how to assemble them effectively. What I am looking to do is model my dependent variable y_t as a Poisson family function of lags of several independent variables and lags of y_t. I would like to include all lags up