similar to: time-varying parameters kalman filter estimation problem using FKF package

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Hi, I would like to know what should I garantee about P and GGt in order to have F = Z %*% P %*% t(Z) + GGt always as a positive definite matrix. Being more precise: I am trying to find minimum likelihood parameters by using the function 'optim' to find the lowest value generated by $LogLik from the function 'fkf' (http://127.0.0.1:27262/library/FKF/html/fkf.html). The

  Dear Fellow R Users, I am experimenting right now the FKF package. I started by working out the first example included in the package and I am already confused. Would you offer some kind suggestions? What I want to do is to write down the state transition equation and the measurement equation explicitly. Following the first example: According to: dt <- matrix(0, nrow = 2) the state vector

hi, I have 2 files example 1 and example 2 and would like to know what is in example2 and not in example1 (attached) V1 contain data which could be in duplicated which I am using as identifiers I used setdiff(example2$V1,example1$V1) to find the identifiers which are specific to example2: [1] "rs2276598" "rs17253672" I am looking for a way to get an output with all

Hello R-experts, I wonder whether any of the R-packages cover the Hilbert-Huang Transform methodology (HHT)? Regards, Torbjorn -- Torbj?rn Lorentzen | torbjorn.lorentzen at bjerknes.uib.no |torbjorn.lorentzen at uni.no | http://www.bjerknes.uib.no/ Phone: +47 55 58 25 05 | Cellphone: +47 906 972 36 | Bjerknes Centre for Climate Research | Geophysical Institute | University of Bergen |

Hi all, what would be the best way to install windows xp pro over XEN. I guess its only possible with NFS,HHT OR FTP shares. Does anyone have idea about it? How to have these share so that i could have Windows xp as a guest OS over xen(FC6)? Thanks -- View this message in context: http://www.nabble.com/installing-xen-over-XEN-tf2794004.html#a7794684 Sent from the Xen - User mailing list archive

Hi, I'll coded your example into R code: Table_A <- c('abc at gmail.com', 'John Chan', '0909') Table_A <- rbind(Table_A, c('bcd at yahoo.com', 'Tim Ma', '89089')) colnames(Table_A) <- c('Email', 'Name', 'Phone') Table_A Table_B <- c('abc at gmail.com', 'John Chan', 'M',

Hi all, I try to exceute a function "myfun" that should use as input "input1.csv" and "input2.csv" . Then I try to save the output dat33 on a csv file (on per each time I execute input1..input 2 and so on). So my problem is how to finally obtain several csv file with "ggt1.csv", "ggt2.csv". The program creates ggt1.csv but BUT when runs

Dear R-users, I'm estimating a two- dimensional state-space model using the FKF package. The resulting log likelihood function is maximized using auglag from the Alabama package. The procedure works well for a subset of my data, but if I try to use the entire data set I get the following error message. Error in eigen(a$hessian) : infinite or missing values in 'x' What's even

Hey guys, I have what i think is a really simple problem :( I installed the seqinr library. I want to do an RSCU analysis. But i can't get it to work in even the simplest case. for example, if i have a string read in: > newdata5 $testseq [1] "agtgagatgatagatagatagatagatagatagatagaccccccagata" and then i perform an RSCU analysis on it... >

To R programming experts, I am a undergraduate student, and now doing research personally. I apply diagonal bivariate poisson (R package "bivpois") with stochatics weighted function (refer to dixoncoles97 section 4.5 to 4.7). However I dont know how to fit this stochatical weighted function to the completed bivariate poisson model. I know that some other references for dynamic soccer

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

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Hey Guys, i want to do a CAPM-GARCH model. I didn?t find anything posted online. (If there is something - shame on me - i didn?t find it.) My Problem: What is the difference if I let the residuals ?e? follow a garch process ? How do I do my regression analysis now? I began reading about regression analyis with heteroscedasticity, but didn?t get it. So i started programming. First

Hi, all: I met "Non-conforming parameters for function %*%" problem, when I run the Jags model in R. My model is like this: model{ for(i in 1:n){ for(j in 1:t[i]){ et[i,j]<-yt[i,j]-beta0+betax*xt[i,j]+betat*t[i,j] } for(a in 1:t[i]){ for(b in 1:t[i]){ sigma[i,a,b]<-pow(rho0,abs(t[a]-t[b])) } } phi[i]<-

Hi This is my function. It serves an HTML page after the calculations. I'm connecting to a MSSQL DB using pyodbc. def CAPM(self,client): r=self.r cds="1590" bm="20559" d1 = [] v1 = [] v2 = [] print"Parsing GET Params" params=client.g[1].split("&") for items in

Thanks - Peter, Eivind, Rui Sorry, I perhaps could not explain it properly in the first go. Trying to simplify it here with an example - Say I have two dataframes as below that are NOT equally-sized data frames (i.e., number of columns are different in each table): Table_A: Email Name Phone abc at gmail.com John Chan 0909 bcd at yahoo.com Tim Ma

# Using Moving/Rolling Windows, here we do an OLS Regression with corrections for #Heteroscedasticity and Autocorrelations (HAC) using Newey West Method. This code is a #extension of Ajay Shah?s code for moving windows simple OLS regression. # The easiest way to adjust for Autocorrelations and Heteroscedasticity in the OLS residuals is to #use the coeftest function that is included in the