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Projects found

Projects for Time series analysis:

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

  • Bayesian MAR(1) model

    This set of Matlab scripts conducts a first order MAR(1) model to estimate interactive effects, and covariate effects, on a time series of data from a community.

    It uses a Gibbs sampler to estimate parameters, and currently is set up with diffuse priors on all parameters for the model. It is pretty basic at this point, but it works.

    Note on the Gamma distribution:

    Going back and forth between Matlab/R/WinBUGS can be confusing, because of the different parameterizations of the gamma pdf. Here's the Matlab/BUGS forms and the R equivalents:

    Matlab: X ~ g(a,b) E[X] = ab

    R equivalent: X ~ g(shape=a,scale=b)

    BUGS: X ~ g(a,b) E[X] = a/b

    R equivalent: X ~ g(shape=a,rate=b) OR

    X ~ g(shape=a,scale=1/b)

  • 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.

  • LAMBDA

    by e2holmes, last updated 9/11/07, sharing set to public
    LAMBDA is a MatLab toolkit designed to do MAR-1 based data analysis on long-term datasets and is based on the methods described in Ives et al. 2003, Ecological Monographs 73:301-330. LAMBDA is designed to allow the user to step through the entire modeling process, from importing the data, to obtaining descriptive statistics of the dataset, to, finally, performing a MAR-1 regression model and obtaining output parameters. A MAR-1 process is a Multivariate, Auto-Regressive first (1st) order process. Essentially, it is a means of estimating interactions between multiple variates from time series data, using matrix algebra. A MAR-1 model is a stochastic, non-mechanistic model that uses time series data on species numbers and co-variates to deduce inter-population interactions and the effects of covariates (e.g., physical variables) on populations.

    Where does it come from?

    LAMBDA is a product of the Mathematical Biology program at the Northwest Fisheries Science Center in Seattle, WA, and was developed with support by NOAA/NMFS and the National Research Council. It is open-source software released under the GNU GPL license, meaning you are free to use and modify it in (almost) any way you see fit. LAMBDA was developed by Steven Viscido while on a National Research Council postdoctoral associateship with Elizabeth Holmes.

    Credits

    LAMBDA is based on the techniques outlined in the paper Ives et al. 2003, Ecological Monographs 73:301-330. The code for the actual MAR-1 regression was based on the "MARbasic.m" MatLab script written by Tony Ives (available at the Ecological Archives). The parameter search code was based on an unpublished script written by Tony Ives. All other code was written by Steven Viscido.

    Executable version

    Download the LAMBDA executable along the installation instructions from the links below.

    LAMBDA_MCR_pkg.exe 0.9.2 Warning: This is a 138 MB file!

    Installation Instructions

    The executable version of LAMBDA does not require MatLab. Its system requirements are Windows XP/2000, 256 MB of RAM, and 150 MB of Hard Drive Space.

    If Installation hangs

    This is a bug on MatLab's side. To work around it, you will need to install vcredist_x86.exe (32 bit systems) or vcredist_x64.exe (64 bit system) first and then repeat the LAMBDA installation. But read the If_Installation_Hangs.txt readme file if this happens to you. This bug affects about 20% of computers, randomly it would seem.

    Matlab Source Code

    This is not needed if you are using the executable version. Current source code version is LAMBDA_0.9.2Beta.zip. The source code can be downloaded below. Requirements for running LAMBDA from the source code are
    • MatLab version 7.0.1 (R14) w/service Pack 1, or later
    • MatLab's Statistics and Optimization toolboxes
    • At least 256 MB of RAM on your system
    • 5 MB of Hard Drive space for the LAMBDA installation
  • MARSS Dev Site

    MARSS is an R package to fit mulitvariate autoregressive state-space (MARSS) models with Gaussian errors to multivariate time series data.  Developers: Eli Holmes, Eric Ward, Mark Scheuerell and Kellie Wills. 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. To see what's coming in the next version, click NEWS in the files.

    To install patches
    source("patchfile.r") #replace with actual file name
    library(R.utils) #install if needed
    #replace patch_fun with function name in patchfile.r
    reassignInPackage("patch_fun", pkgName="MARSS", patch_fun)
    To install the development version
    library(devtools)
    install_url("https://fishbox.nwfsc.noaa.gov/zip/e2holmes/MARSS%20Dev%20Site/MARSS_source.zip")
  • PVA estimation code

    A series of modules for estimation of PVA parameters from time series data. Uses kalman filters, REML, and slope methods.

Projects for Time series analysis:

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

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