Projects found

Projects for kalman filter:

1-5 of 5 shown   (2 visible only to FishBox members, 1 visible only to project members).    
    
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  • DARTER (Diffusion Approximation Tools for Extinction Risk Estimation)

    This program allows one to walk through the steps required to conduct a population viability analysis, or PVA, using a population time time series. The model outputs probabilities of extinction as a function of time steps into the future, and importantly, gives confidence intervals for these probabilities.

    This tool has two major advantages over traditional PVA techniques:

    1) It uses a state-space Kalman filter that allows for both process and non-process error.

    So what's the big deal? --Functionally it filters the data, and allows a more accurate fit for population parameters of interest.2) It uses a Bayesian sampling-importance-resampling algorithm to fully address uncertainty in the parameter estimates given the data.Rather than developing a single function that describes the probability of population extinction through time, we can use the uncertainty in parameter estimates to develop 'probabilities of probabilities', or, the uncertainty surrounding the probability of extinction through time.

  • Estimating heritability from time series

    by mdscheuerell, last updated 1/5/11, sharing set to public

    We use a multivariate state-space model and a time series of yearly migration dates for sockeye salmon to estimate the heritability coef from the classic breeder's equation.

    The 2 files below are the dataset and the R script.

  • MARSS Dev Site

    This is the DEVELOPMENT site for the MARSS.  For the current MARSS  release go to CRAN or download straight from the R GUI using "Install Packages" menu.

    MARSS fits mulitvariate autoregressive state-space (MARSS) models with Gaussian errors to multivariate time series data.  A MARSS model is:

    x(t) = B(t) x(t-1) + u(t) + C(t)c(t) + v(t), v(t)~MVN(0,Q)

    y(t) = Z(t) x(t) + a(t) + D(t)d(t) + w(t), w(t)~MVN(0,R)

    Project news (Feb 26, 2013): MARSS 3.4 uploaded to CRAN.  I fixed MARSSkfas to work with the new KFAS package in order to use the Koopman/Durbin filter/smoother algorithms. I also coded up a lag-one covariance smoother using an augmented state-space model that you can then run through the smoother to get the lag-one covariances.  I added a coef() and residuals() method to improve output.

    Developers: Eli Holmes, Eric Ward, Mark Scheuerell and Kellie Wills

    Current known issues:

    • When variance is "unconstrained", the covariances can be set to 0 in the degen.test() and this leads to not pos-def matrix and error.  Need to block setting to 0 when this happens, or block covariances set to zero?  Currently, deal with this by setting allow.degen=FALSE when covariances are estimated.
    • demean.states=TRUE is causing the EM algorithm to give drops in logLik. This is not really a bug but perhaps a property of demean.states.  Removed the demean.states option in vrs 3.3.

    MARSS 4.0 in progress:

    • 4.0 involves a substantial change in the model object structure---however the user should not notice the difference.  The change allows the developers to more easily code up new model structures. 
    • Progress continues on writing functions for standard output, e.g. predict function added. 
    • Integration with LateX begun so that output can be sent to a tex or pdf file instead of just the console.

     

  • PVA estimation code

    A series of modules for estimation of PVA parameters from time series data. Uses kalman filters, REML, and slope methods.
  • Teaching code for State-space models

    This is some matlab labs for teaching basic concepts about stochastic population trajectories and estimation for those trajectories.

Projects for kalman filter:

1-5 of 5 shown   (2 visible only to FishBox members, 1 visible only to project members).    
    
1  

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