Friday, November 30, 2012

Winter stats reading group starting up: Hierarchical Modeling and Analysis for Spatial Data

The NWFSC/SAFS stats reading group is reading "Hierarchical Modeling and Analysis for Spatial Data" by Banerjee et al. this quarter.  Fridays 3pm at SAFS 229 during Winter Qtr 2013.  Open to interested statistical ecologists.  Contact Eli.

Monday, November 12, 2012

New paper on spatial-temporal time series modeling

New paper just out by Eric Ward using Bayesian state-space time-series models.

"Applying time series models with spatial correlation to identify the scale of variation in habitat metrics related to threatened coho salmon (Oncorhynchus kisutch) in the Pacific Northwest"
Eric J. Ward, George R. Pess, Kara Anlauf-Dunn, and Chris E. Jordan
Canadian Journal of Fisheries and Aquatic Science (link to paper)

Abstract: Trend analyses are common in the analysis of fisheries data, yet the majority of them ignore either observation error or spatial correlation. In this analysis, we applied a novel hierarchical Bayesian state-space time series model with spatial correlation to a 12-year data set of habitat variables related to coho salmon (Oncorhynchus kisutch) in coastal Oregon, USA. This model allowed us to estimate the degree of spatial correlation separately for each habitat variable and the importance of observation error relative to environmental stochasticity. This framework allows us to identify variables that would benefit from additional sampling and variables where sampling could be reduced. Of the eight variables included in our analysis, we found three metrics related to habitat quality correlated at large spatial scales (gradient, fine sediment, shade cover). Variables with higher observation error (pools, active channel width, fine sediment) could be made more precise with more repeat visits. Our spatio-temporal model is flexible and extendable to virtually any spatially explicit monitoring data set, even with large amounts of missing data and no repeated observations. Potential extensions include fisheries catch data, abiotic indicators, invasive species, or species of conservation concern.

Wednesday, August 1, 2012

Time-series analysis course winter 2012

Fish 50X: Applied Time Series Analysis in Fisheries and Environmental Sciences
Winter 2012
Fisheries Dept, University of Washington

Instructors: Eric Ward, Eli Holmes, Mark Scheuerell

Reviews current applications of univariate and multivariate time series models for biological and environmental data, emphasizing the estimation, inference, and forecasting aspects of time-series models. Explores effects of covariates and anthropogenic drivers for species that are exploited and/or of conservation concern. Recommended: FISH 552 or prior experience with R (e.g. FISH 560), QSCI 482 or basic statistics, and at least 1 course in population dynamics (FISH 454 or 458).

R Journal article on the MARSS package

Holmes, E. E., Ward, E. J. and K. Wills. 2012. MARSS: Multivariate autoregressive state-space models for analyzing time-series data. R Journal 4: 11-19.

MARSS 3.1 released on CRAN

MARSS 3.1 is now up on CRAN.  This allows for time-varying constraints and covariates.  See the updated User Guide (on CRAN).  The major changes are internal and allow for us easily write customized functions for different MARSS forms (like AR-p processes and DFA models).   3.1 is considerably slower than 2.x, however this should be fixed in 3.2 or 3.3 when the Kalman filter in the KFAS package can be hooked back up to MARSS (temporarily disabled).

Saturday, June 23, 2012

MARSS 3.0 posted for testing

Hi MARSS users,

A version of MARSS 3.0 is now up for testing.  It should be backwards compatible with any MARSS 2.x code you have unless you use  control$diffuse or control$kf.x0.  diffuse now goes into your model list and kf.x0 is called tinitx and goes in the model list too.  control$kf.x0="x00" is now model$tinitx=0 and control$kf.x0="x10" is model$tinitx=1.

Big changes are
  • Time-varying parameters are allowed.  See the Quick_Start.pdf to get a brief intro to that feature but it should be pretty self-explanatory.
  • Covariates can be added in the standard way.  Again see the Quick_Start.pdf for quick intro.  See chapter in User Guide on estimating species interactions for an example.
  • There is a "form" argument in the MARSS() call that allows one to specify special types of models.  Default is "marxss" which covers MARSS + covariates.  The only other form now is "dfa" for Dynamic Factor Analysis.  Check out the DFA chapter in the User Guide for an intro to the form="dfa" which allows you to do a standard DFA by just passing in m (number of states), data and covariates (if wanted).  The dfa form is basic now.  Later we will specialize its output to give loadings etc.
  • The AR-p models work now with method="kem" which is much, much faster than method="BFGS".  See chapter in User Guide on AR-p models.
  • You can enter things like B=diag(list("1+2*c+3*b",0,0,"2+3c"),2,2) in your list matrices and MARSS will know what to do, i.e. it will estimate B.c and B.b and it knows that B(1,1)=1+2c+3b .  I haven't seen people want to do this..., but you can.
  • The print call takes a argument called "what".  Use ?print.marssMLE to see how to use it.  It'll make it easier to print things from your marssMLE objects (what you get back from a MARSS call).
Here is are the tar.gz and .zip files for the 3.0 version.   You'll find links to the User Guide and Quick_Start guide here too.
Plan is to upload to CRAN about July 1 assuming no big issues arise. Right now all the prior examples in the User Guide 2.8 and the man files work as before.  I've included new examples with covariates using the new covariate code in the chapter on estimating spp interactions and I've added covariates to the DFA chapter.  I've included a little more code in the AR-1 chapter on estimating those models.

Feel free to try it out.  The more real-world testing it gets before being uploaded to CRAN the better.



Thursday, March 22, 2012

Article from the group on analysis of marine plankton community structure

Scheef, L.P., D.E. Pendleton, S.E. Hampton, S.L. Katz, E.E. Holmes, M.D. Scheuerell, and D.G. Johns. 2012. Assessing marine plankton community structure from long-term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station vs. spatiallydistributed sampling data. Limnology & Oceanography: Methods 10: 54-64.

ABSTRACT: We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time-series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time-series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time-series data collected in marine environments.

Time-series analysis workshop Sat Aug 5, Portland, OR

Eli, Eric and Mark will offer their 1-day workshop on multivariate time-series analysis using the MARSS package again at the annual ESA meeting (  Workshop is scheduled for the Sat before the meeting.  We are working on new case studies involving incorporation of covariates into analyses.  MARSS 3.0 will be done by then.  This is a major update that allows all parameters to incorporate time-varying covariates.