similar to: Problem with optim()

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

Dear R-brains, I'm rather new to state-space models and would benefit from the extra confidence in using the excellent package sspir. In a one-factor model, If I am trying to do a simple regression where I assume the intercept is constant and the 'Beta' is changing, how do I do that? How do i Initialize the filter (i.e. what is appropriate to set m0, and C0 for the example below)?

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly

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]]

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? -

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 filterstep function (file sspir_kfs.R),

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 R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Hello everybody, I am writing a review paper about State Space models in R, and I would like to cover as many packages as I reasonably can. So far I am familiar with the following tools to deal with SS models: * StructTS, Kalman* (in stats) * packages dse[1-2] * package sspir * package dlm I would like to have some input from users who work with SS models: are there any other packages for SS

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)

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

Dear R-Users, I'm using SSPIR package for a spatio-temporal application. Is it possible to modify the structure of the involved matrixes (Fmat, Gmat, Vmat,Wmat)? I want to create a model like this #y(t)=k*theta(t)+epsilon(t) #theta(t)=h*theta(t-1)+eta(t) #epsilon(t) N(0,V) V=sigma2*I #eta(t) N(0,W) W=sigma2_eta where the state variable theta has dimension 1(p=1) and at

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

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?

I would like to see R packages arranged by topic. CRAN Task Views are at http://lib.stat.cmu.edu/R/CRAN/src/contrib/Views/ , but I'd like something more detailed. For example, the IMSL Fortran library, version 4 is easy to navigate and has procedures arranged according to following topics: Basic Statistics Regression Correlation Analysis of Variance Categorical and Discrete

Full_Name: Ian McLeod Version: 2.3.1 OS: Windows Submission from: (NULL) (129.100.76.136) > There is a simple bug in acf as shown below: > > z <- 1 > acf(z,lag.max=1,plot=FALSE) > Error in acf(z, lag.max = 1, plot = FALSE) : > 'lag.max' must be at least 1 > This is certainly a bug. There are two problems: (i) the error message is wrong since lag.max is

Hello, I am a new R user and I need R code for GLARMA. I will be really thankful if you help me. Yours sincerely,

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]]

Hi Rers, I have a poission time series model with 5 parameters. I just wanted to remove two of the lag on response in the model and put it as a system model. I am not sure about the codes to combine these two on R. If anybody has any R example (code), please post it. My original model: log(Y(t))~constant+b1*Y(t-1)+b2*Y(t-2)+b3*(variable1)+b4*(variable2)+e I would like to construct a