Time-series analysis workshop at ESA on Sat Aug 6th

Eli, Eric and Mark will be teaching a 1-day workshop on ecological time-series analysis using state-space models at the ESA annual meeting:  http://eco.confex.com/eco/2011/webprogram/Session6999.html

MARSS 2.3 uploaded to CRAN

The MARSS package fits constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models to multivariate time series data via priamarily an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided. Functions are provided for parametric and innovations bootstrapping, Kalman filter and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the hessian approximation and via bootstrapping and calculation of auxilliary residuals for detecting outliers and shocks. The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.

http://cran.r-project.org/web/packages/MARSS/index.html

MARSS 2.3 is a big change over 1.1

• Allows B and Z estimation 
• Allows models with diagonal elements of R or Q set to 0. So you can have partially deterministic systems.
• Covariates are allowed (see user guide and look up 'covariates' in the index)
• Allows many types of Q and R matrices. Few constraints except it needs to be a valid variance-covariance matrix. See section in Derivations.pdf 
• Allows you to set the initial state at t=0 or t=1
• Allows diffuse priors if initial state at t=1 and method=BFGS
• case studies added to the user guide on dynamic factor analysis, B estimation, covariates, and detecting outliers and shocks

New NASA grant to study effect of Arctic climate change on bowhead whales

Eli and Dan were awarded a NASA grant to study the effect of Arctic climate change on bowhead whales in the Beaufort Sea.  This is a collaboration between the Northwest Fisheries Science Center, the University of Washington Advanced Physics Laboratory, the National Marine Mammal Laboratory, and the New England Aquarium.

http://www.nasa.gov/topics/earth/features/climate_partners.html

MARSS 2.1 ready for testing

Download from the MARSS Dev Site
The big update of MARSS to allow B and Z estimation is finally ready for testing. It'll be awhile before its ready for CRAN since I need to work on a couple new case studies and fix bugs and stuff that comes up in testing. I added a mock up of a new case study on dynamic factor analysis (sensu Alain Zuur) since that is of special interest to fisheries folks.  See the editlog on the dev site to see the recent changes

MARSS is an R package which fits multivariate state-space models to time series data. It is different than other state-space packages in that it does maximization via a constrained Expectation-Maximization (EM) algorithm (an extension of the EM algorithm developed by Shumway and Stoffer 2000). The EM algorithm is slower (much) than the BFGS algorithm used in other packages, but can be more stable (less likely to get hung up on numerical problems) for some types of state-space models also the EM algorithm used to produce good initial conditions for Bayesian or Nelder-Mead algorithms.

New in version 2.1
* B and Z estimation
* Dynamic factor analysis (sensu Harvey 1989 and Zuur et al 2003)
* Allows degenerate Q and R (variances=0)
* Fewer constraints on what type of Q and R matrices you can fit

MARSS 2.1 still does not allow covariates. Including that is next on my to-do list, but it will be awhile. MARSS 2.1 also does not allow temporally varying parameters. Again, it's easy enough to include but is not high on my to-do list.

Winter session of the NWFSC Stats Reading group has started

This quarter's book is

Royle, J.A. and R.M. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations, and Communities. Academic Press, San Diego, CA. xviii, 444 pp

Stephanie Hampton talking at ASLO 2011 about some joint work on plankton dynamics

CHALLENGES AND SOLUTIONS TO ANALYZING MARINE COMMUNITIES WITH MULTIVARIATE AUTOREGRESSIVE (MAR) MODELS
Multivariate autoregressive (MAR) models have a long history of use in economics, and have been used successfully to understand trophic interactions and community stability in freshwater plankton communities. Uses of MARs in marine ecosystems have been limited, however, possibly due to several challenges that arise with marine data. For example, many marine monitoring programs 1) use less consistent sampling locations, 2) have more variable time intervals, 3) may have higher observation error, and 4) include highly mobile or transient taxa. Comparison of results from standard MARs applied to point and transect data suggested that information loss in applying standard MARs to transect data can be substantial. Additionally, MAR results typically indicate more trophic links in freshwater communities than comparable marine communities, suggesting that modifications to MARs are necessary when analyzing typical marine data. Development of state-space MARs may help elucidate patterns in marine data by explicitly estimating species interactions in light of both process and observation errors. Extension of Bayesian methods also allows for probabilistic statements about parameters. We demonstrate these improvements in MARs via analyses of several plankton datasets.

This work associated with a CAMEO grant to S. Hampton, S. Katz, E. Holmes, and M. Scheuerell.

New isotope mixing model paper

Ward, E.J.*, Semmens, B.X., Phillips, D.L., and Moore, J.W. 2011. A quantitative approach for grouping sources in stable isotope mixing models. In press, Ecosphere * joint 1st authorship