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). http://cran.r-project.org/web/packages/MARSS
Wednesday, August 1, 2012
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
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.
http://fishbox.iugo-cafe.org/
http://fishbox.iugo-cafe.org/
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.
Cheers,
Eli
Feel free to try it out. The more real-world testing it gets before being uploaded to CRAN the better.
Cheers,
Eli
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.
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 (http://www.esa.org/portland). 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.
Wednesday, November 9, 2011
New article by Eric in Conservation Letters
Integrating diet and
movement data to identify hot spots of predation risk and areas of
conservation concern for endangered species,
Eric J. Ward, Phillip S. Levin, Monique M. Lance, Steven J. Jeffries, Alejandro Acevedo-Gutiérrez
Effective management of threatened and endangered species requires an understanding of how species of conservation concern are distributed spatially, as well as the spatial distribution of risks to the population, such as predation or human impacts (fishing, pollution, loss of habitat). Identifying high risk areas is particularly important when designing reserves or protected areas. Our novel approach incorporates data on distribution, movement, and diet of a generalist marine predator (harbor seals) to identify and map ‘hot-spots’ of predation risk for an endangered prey species (rockfish). Areas with high concentrations of seals (including some current marine reserves) are also estimated hot spots for rockfish predation. While marine reserve planning currently targets areas with good habitat and low human disturbance, our modeling suggests that future terrestrial and marine reserve design may be made more effective by incorporating other components of the food web that either directly or indirectly interact with target species.
http://onlinelibrary.wiley.com/doi/10.1111/j.1755-263X.2011.00210.x/abstract;jsessionid=7EB218B3908AAED4EC8A2E308F6C9046.d01t02
Effective management of threatened and endangered species requires an understanding of how species of conservation concern are distributed spatially, as well as the spatial distribution of risks to the population, such as predation or human impacts (fishing, pollution, loss of habitat). Identifying high risk areas is particularly important when designing reserves or protected areas. Our novel approach incorporates data on distribution, movement, and diet of a generalist marine predator (harbor seals) to identify and map ‘hot-spots’ of predation risk for an endangered prey species (rockfish). Areas with high concentrations of seals (including some current marine reserves) are also estimated hot spots for rockfish predation. While marine reserve planning currently targets areas with good habitat and low human disturbance, our modeling suggests that future terrestrial and marine reserve design may be made more effective by incorporating other components of the food web that either directly or indirectly interact with target species.
http://onlinelibrary.wiley.com/doi/10.1111/j.1755-263X.2011.00210.x/abstract;jsessionid=7EB218B3908AAED4EC8A2E308F6C9046.d01t02
Thursday, August 11, 2011
Post-doc opening in our research group
Time-series modeling of large-scale population and community processes
Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA
NOAA’s Northwest Fisheries Science Center (NWFSC) has a large research group using time-series modeling to study ecological dynamics. Our research interests are diverse, including estimating species-interaction strengths, inferring environmental and anthropogenic drivers of population and community dynamics, estimating stability metrics, detecting change points and regime shifts. We have numerous long-term and large-scale time-series data on fish, marine mammals, and plankton, which we use to study a wide array of basic and applied ecological and fishery questions.
We are seeking a post-doctoral scientist (recent or fairly recent grad) to join our research group. Strong quantitative skills along with a background in aquatic or fisheries ecology are necessary for this position including a record of publication. The ideal candidate would also have experience and interest in one or more of the following areas:
· statistical modeling, esp. hierarchical modeling
· time-series analysis
· population and/or community dynamics
· fisheries management
· salmon biology
Post-doctoral positions are initially supported for 1 year with extensions up to 3 years contingent on satisfactory progress and submitted publications.
Why come post-doc at the NWFSC? You will join a supportive, collaborative and productive team of quantitative ecologists at NWFSC who are using time-series modeling to study ecological dynamics. Our center of 300+ research scientists has a large number of post-doctoral fellows and provides a stimulating and productive environment for research. Post-docs trained in our group obtain a strong grounding in modern ecological statistics and have high success obtaining positions at both federal research and academic institutions. Close proximity to the University of Washington (a 10min walk) facilitates on-going collaborations with faculty and post-docs in the UW School for Fishery and Aquatic Sciences and other departments across campus.
Interested? Contact one of the PIs below to discuss the position in more detail. Please attach a CV, recent publications, , and a brief statement describing your background, including any programming and modeling expertise. If you will be at the AFS meeting, we'd love to talk with you. Review of candidates will start in earnest after AFS in mid-September.
PIs on this project are:
Eli Holmes eli.holmes@noaa.gov http://faculty.washington.edu/eeholmes/
Mark Scheuerell mark.scheuerell@noaa.gov http://faculty.washington.edu/scheuerl/
Eric Ward eric.ward@noaa.gov http://sites.google.com/site/ericward2/
Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA
NOAA’s Northwest Fisheries Science Center (NWFSC) has a large research group using time-series modeling to study ecological dynamics. Our research interests are diverse, including estimating species-interaction strengths, inferring environmental and anthropogenic drivers of population and community dynamics, estimating stability metrics, detecting change points and regime shifts. We have numerous long-term and large-scale time-series data on fish, marine mammals, and plankton, which we use to study a wide array of basic and applied ecological and fishery questions.
We are seeking a post-doctoral scientist (recent or fairly recent grad) to join our research group. Strong quantitative skills along with a background in aquatic or fisheries ecology are necessary for this position including a record of publication. The ideal candidate would also have experience and interest in one or more of the following areas:
· statistical modeling, esp. hierarchical modeling
· time-series analysis
· population and/or community dynamics
· fisheries management
· salmon biology
Post-doctoral positions are initially supported for 1 year with extensions up to 3 years contingent on satisfactory progress and submitted publications.
Why come post-doc at the NWFSC? You will join a supportive, collaborative and productive team of quantitative ecologists at NWFSC who are using time-series modeling to study ecological dynamics. Our center of 300+ research scientists has a large number of post-doctoral fellows and provides a stimulating and productive environment for research. Post-docs trained in our group obtain a strong grounding in modern ecological statistics and have high success obtaining positions at both federal research and academic institutions. Close proximity to the University of Washington (a 10min walk) facilitates on-going collaborations with faculty and post-docs in the UW School for Fishery and Aquatic Sciences and other departments across campus.
Interested? Contact one of the PIs below to discuss the position in more detail. Please attach a CV, recent publications, , and a brief statement describing your background, including any programming and modeling expertise. If you will be at the AFS meeting, we'd love to talk with you. Review of candidates will start in earnest after AFS in mid-September.
PIs on this project are:
Eli Holmes eli.holmes@noaa.gov http://faculty.washington.edu/eeholmes/
Mark Scheuerell mark.scheuerell@noaa.gov http://faculty.washington.edu/scheuerl/
Eric Ward eric.ward@noaa.gov http://sites.google.com/site/ericward2/
Tuesday, August 2, 2011
Eric and Brice teach at an isotop mixing workshop in Ireland
Eric and Brice participated in a workshop on stable isotope analysis using their package MixSIR at a workshop sponsored by Trinty College in Dublin, Ireland. http://www.tcd.ie/Zoology/research/research/theoretical/isotopeworkshop.php
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