similar to: dlmSum(...) and non-constant state space models

Hi, All, I'm trying to build a customized backend and I need to lower the formal arguments like this: There are several specific registers just for storing formal arguments. And also there are several general purpose registers for computation. If there is an instruction which uses parameters, I should first use a move instruction, which moves the value to general purpose register. For

Hello all, I am working with the dlm package, specifcially doing a dlm multivariate Y linear regression using dlmModReg and dlmFilter and dlmSmooth... I have altereted the inputs into dlmModReg to make them time-varying using JFF, JW etc. How do I track the results of the time varying system matrices? For example what I am really interested in is JW - my system variance matrix for each time

Hi all R gurus out there, Im a kind of newbie to kalman-filters after some research I have found that the dlm package is the easiest to start with. So be patient if some of my questions are too basic. I would like to set up a beta estimation between an asset and a market index using a kalman-filter. Much littarture says it gives superior estimates compared to OLS estimates. So I would like to

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''m having a problem that I am sure there is a simple solution for, but I can''t see it. I have the following partial: <% form_for :time_entry, TimeEntry.new, :url => hash_for_time_entries_url(:period => @period, :action => ''new''), :html => { :id => ''time-entry-form'' } do |f| %> <table border="0"

Hi. I''m having a problem that I am sure there is a simple solution for, but I can''t see it. I have the following partial: <% form_for :time_entry, TimeEntry.new, :url => hash_for_time_entries_url(:period => @period, :action => ''new''), :html => { :id => ''time-entry-form'' } do |f| %> <table border="0"

Hello R users, I have gene expression data of two groups of genes (large and small). Gene expression intensities of those genes are classified into 1 to 10 levels. What I want is to make a random set of genes that have the same levels as the small group from large group using sample(). I used smallvec to hold the number of genes in each levels (1 to 10) for small group, largevec for large group.

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

I uploaded a new version of package dlm to CRAN. dlm provides functions for maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Gaussian linear state space models, also known as Dynamic Linear Models. The most important visible changes from the previous version are the following. 1) Missing values are now allowed in the observations. 2) Extractor and replacement

I uploaded a new version of package dlm to CRAN. dlm provides functions for maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Gaussian linear state space models, also known as Dynamic Linear Models. The most important visible changes from the previous version are the following. 1) Missing values are now allowed in the observations. 2) Extractor and replacement

Hi there, I have a major problem (major for me that is) with solve.QP and I'm new at this. You see, to solve my quadratic program I need to have the lagrange multipliers after each iteration. Solve.QP gives me the solution, the unconstrained solution aswell as the optimal value. Does anybody have an idea for how I could extract the multipliers? Thanx, Serge "Beatus qui prodest quibus

Hi. Here are my patches and makefiles for compiling ogg, vorbis and vorbis-tools on DOS with DJGPP. They can build them from cvs snapshots and need minimal maintainance (even extract source lists from `makefile.am's). Everything seems to work, except one FP exception on encode at middle bitrates (lowest two and highest bitrates don't cause it) - I don't think it's my