search for: kfilter

I'm currently running into a little trouble with the kfilter method, and would love some clarification if you are able to offer it. When trying to run kfilter, I've been running into errors that seem to result from having mismatched dimensions. Specifically, the dimension of my observations is 2, while the dimension of the state space is 4. In the filt...

...}, # var observation equation + Wmat = function(tt, x, phi) { + WM <- mat + cat("Wmat", dim(WM), "\n") + WM + }, + m0 = matrix(c(1, 2), nrow = 1), + C0 = mat) Gmat 2 2 Gmat 2 2 > > model.f <- kfilter(model) Gmat 2 2 Wmat 2 2 Fmat 2 2 Vmat 2 2 Error in y - f : non-conformable arrays >

...0,0.07,0.04,-0.02,0,-0.03,0.04,-0.04,-0.01,0.03,0.02,0.05,0.04, 0.05,0.03,0,-0.04,0.05,0.05,0.06,0.02,0.04,-0.06) ) ss <- ssm(y ~ tvar(x), time = 1:nrow(dfrm), family=gaussian(link="identity"), data=dfrm) smooth.params <- smoother(kfilter(ss$ss))$m (1) I read in http://ww.math.aau.dk/~mbn/Teaching/MarkovE05/Lecture3.pdf that this is requred as there is a bug in sspir. To what should I set ss$ss$m0 and ss$ss$C0? (I did notice that smoother() replaces these, but it still matters what I initialize it to in the first place) Many than...

...oped 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? - kfilter? Thanks for helping. I have ask the same question in the help list “sig-dynamic-models” Best, Thomas [[alternative HTML version deleted]]

...o be happening because the function to be optimized, which is minus the likelihood of a SS object (see package SSPIR) is badly behaved: > model.opt <- model > phi(model.opt) <- phi_opt$par > for(i in seq(0.0001, 0.0015, by = 0.0001)) { + phi(model.opt)[3] <- i + model.f <- kfilter(model.opt) + cat(i, model.f$likelihood, "\n") + } 1e-04 828.8 2e-04 888.2 3e-04 930.5 4e-04 961.1 5e-04 983.5 6e-04 1000 7e-04 1012 8e-04 1021 9e-04 1027 0.001 1031 0.0011 1034 0.0012 1035 0.0013 1035 0.0014 1035 0.0015 1033 > This is in practice a problem as one has to manually twe...

Hey all, I am trying to work under a State Space form, but I didn't get the help exactly. Have anyone eles used this functions? I was used to work with S-PLUS, but I have some codes I need to adpt. Thanks alot, Bernardo [[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

...roblem is: > > > > x[t]=(lambda[t], R[t])? > > A[t]=(1-kappa+delta_t, 0; 0, 1+mu) > > B[t]=(1,0;0,1) > > epsilon[t]=(epsilon_l, epsilon_r)? > > C[t]=(1,1) > > Eta[t]=0 > > > > I used serveral alternative methods (dlm, kalmanLike, fkf, kfilter) for > parameter estimation but I don?t understand the syntax and the correct > input > for model estimation. > > > > Can anybody help me, which packed is the most best for my problem and how > is > it to control? > > > > Thanks for helping. > >...