Browse Projects

This program (VBA implemented in Excel) animates the path of a tagged animal in a VR2 hydrohpone network. It DOES NOT animate real time sped up. Instead, it animates movement based on relocation events. Thus, whenever a tag is heard at a new hydrophone, the map updates, and provides a summary of how many times and over how many days the animal/tag was heard at that hydrophone. This is a good strategy when you are interested in highlighting migrations as opposed to daily movements, for instance.

MixSIR is a Bayesian isotope mixing model that incorporates uncertainty in the estimates of mix and source isotope values. The model also provides the opportunity to incorporate prior information for the proportional contribution of each source to the mix. The programs for this project are written in Matlab. I have provided the source files as well as an .exe file package so that those without Matlab can run the program. Collaborations and improvements are welcome!


Prior to installing MixSIR you MUST download the (free) Matlab component runtime library and run it once on your machine. Note also that installing new versions of MixSIR will require a new download and installation of the component runtime library.

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

Miscellaneous plotting functions that come in handy.

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.
So what's the big deal? -- 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.

These are function for generating various standard types of stochastic population processes: random walks, Ornstein-Uhlenbeck, discrete Gompertz, etc. Also some random number generators needed by these are here. These matlab files need the Matlat Statistics Toolbox.

These routines allow you to take a matrix of MCMC samples and calculate the Bayes factor based on the harmonic mean algorithm proposed by Gelfand and Dey (1994). Caution: Bayes factors tend to be numerically unstable!

These files are a collection of Java functions I've written to calculate PDFs/log likelihoods, and to do a variety of random number generator. You can use the built in Java random number generator, but I chose to link these files to the free RngPack library.

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

KalmanEM.R fits mulitvariate state space models to multivariate time series data.

X(t) = B X(t-1) + U + E(t)
Y(t) = X(t) + A + eta(t)

This code is used to estimate maximum likelihood parameters for multivariate state space time series models via an EM algorithm using the Kalman filter+smooter. We assume that there are N observation time series over T years, and that there may be shared parameters (growth rates, process error, observation error) across sites. Further, the errors may be correlated between sites and the N sites may be clustered into groups.

An online workshop on fitting multivariate state-space models to time series data is available with case studies and examples for estimating trends, evaluating population structure, estimating interactions, and analyzing movement data:
MSSM workshop

The base EM algorithm is from Shumway and Stoffer (2006).

The case study pdf is effectively the current manual. Scripts for all the case studies are in the zip file.

This project includes the WinBUGS files illustrating how to include model selection in MCMC. Variances are modeled as a mixture of source/fractionation and additional residual variance. We include to variations on the MixBUGS model, 1 without residual variance, and 1 modeling variance as a mixture (to calculate posterior probabilities). We also include the R-script illustrating that the SIAR package likely contains a bug (even when data are generated from a 0.7-0.2-0.1 mixture, estimated contributions are nearly equal).

Before you begin with this code, make sure you download the latest release of OpenBUGS(now 3.0.3) and it is installed to the default directory, "C:\Program Files\OpenBUGS". We've also found that it helps to have a cleaned up registry + defregmented hard drive before using OpenBUGS. The version of R that you're using should also be noted - R 2.5.1 and 2.7.0 work great with OpenBUGS, but there was a glitch in R 2.6.0 that prevented OpenBUGS from ever running through R. When using BUGS, I think that you can obviously use WinBUGS or OpenBUGS. While both programs should yield the same result, I had much more success with OpenBUGS, particularly in terms of the program not crashing during the burn-in (anyone familiar with 'trap' error messages knows what I'm talking about). There are 2 project files. The first (writeModel.r) is sourced by the second file (runDataCloning2.r). If you want to modify the bounds on parameters, you'll need to edit writeModel.r - otherwise, everything will be done behind the scenes. I've tried to predict many of the errors that might occur - for example, it doesn't make sense to have all sites belonging to the same group (m = 1) and an unconstrained covariance matrix. These files won't automate every possible MSSM that you want to fit, but they will do the majority of them. The R script files actually write the BUGS code for you, so you don't have to know any BUGS coding. You should however, be aware of the priors. Uniform priors are used on SDs and growth rates - to change these, look anywhere in the file for (~ dunif(a,b), where a and b are the limits). The code also includes the option of including an interaction matrix (multiple species) or density dependence matrix (multiple populations). The priors for all of these terms are bounded (0,1). Included are several examples of summarizing parameters, including the median, mean, and using density to estimate the mode. The actual MLEs are going to be the points associated with the best (lowest) density.