eco-stats.unsw.edu.au
Eco-Stats Ecological Statistics Research Group - University of New South Wales - Australia
http://www.eco-stats.unsw.edu.au/opportunities.html
Analysis of multivariate abundances. Are you interested in joining Eco-Stats? Not 100% sure the ecology focus is for you? Firstly, note that prior experience in ecology (while useful) is not required, just a willingness to learn! But secondly, problems we deal with are encountered in other disciplines also (e.g. bioinformatics, epidemiology) so skills you hone in our group enable all sorts of interesting research careers. Eco-Stats Alumni. And the Centre for Ecosystem Science. 5K/year top-up scholarships...
eco-stats.unsw.edu.au
Eco-Stats Ecological Statistics Research Group - University of New South Wales - Australia
http://www.eco-stats.unsw.edu.au/sitemap.html
Analysis of multivariate abundances. Analysis of multivariate abundances. Point process modelling of kangaroo roadkills. Community-level modeling of presence-only data. Finite mixture modelling and ecological applications. High dimensional data analysis. Phone: 61 2 9385-7031. Fax: 61 2 9385-7123. E-mail: David.Warton(at)unsw.edu.au. Red Centre room 2052. School of Mathematics and Statistics. The University of New South Wales. Eco-stats: UNSW Ecological Statistics Research Group.
eco-stats.unsw.edu.au
Eco-Stats Ecological Statistics Research Group - University of New South Wales - Australia
http://www.eco-stats.unsw.edu.au/index.html
Analysis of multivariate abundances. Welcome to Eco-stats: the UNSW Ecological Statistics Research Group. The Eco-Stats Research Group is a team of statistics researchers and students led by David Warton who specialise in ecological statistics — improving the methods for making use of data to answer research questions commonly asked in ecology. We are based in the School of Mathematics and Statistics. But our members are also affiliated with the Evolution and Ecology Research Centre. To find out more.
eco-stats.unsw.edu.au
Eco-Stats Ecological Statistics Research Group - University of New South Wales - Australia
http://www.eco-stats.unsw.edu.au/software.html
Analysis of multivariate abundances. Below are some links to free downloadable softare based on our research. If you have any questions, feel free to ask! S)MATR - (Standardised) Major Axis Estimation and Testing Routines. Software for bivariate line-fitting in allometry. Smatr version 3 is available as an R package. Or try (S)MATR as an executable file. Written by Dan Falster. This comes with a helpful user's guide. Mvabund - model-based analysis of multivariate abundances in ecology. Fax: 61 2 9385-7123.
eco-stats.unsw.edu.au
Eco-Stats Ecological Statistics Research Group - University of New South Wales - Australia
http://www.eco-stats.unsw.edu.au/people.html
Analysis of multivariate abundances. Left to right: David Warton, Eve Slavich, Francis Hui, Jakub Stoklosa, Gordana Popovic, Andrew Letten. Current Eco-stats group members. Professor and Australian Research Council Future Fellow. Research Fellow (2013-), Measurement error models in ecology and multivriate adaptive regression splines for non-normal data. Research Associate (2015-), Design-based inference for multivariate mixed models. Research Associate (2015-), Spatial confounding in point process models.
datavoreconsulting.com
Count data and GLMs: choosing among Poisson, negative binomial, and zero-inflated models | Datavore Consulting
http://datavoreconsulting.com/programming-tips/count-data-glms-choosing-poisson-negative-binomial-zero-inflated-poisson
Math, programming, ecology, and more. Count data and GLMs: choosing among Poisson, negative binomial, and zero-inflated models. Ecologists commonly collect data representing counts of organisms. Generalized linear models (GLMs) provide a powerful tool for analyzing count data. [As mentioned previously. You should generally not transform your data to fit a linear model and, particularly, do not log-transform count data. No of samples per treatment. Mean count for treatment A. Mean count for treatment B.
datavoreconsulting.com
Numerically solving PDEs in Mathematica using finite difference methods | Datavore Consulting
http://datavoreconsulting.com/programming-tips/numerically-solving-pdes-mathematica-finite-differences
Math, programming, ecology, and more. Numerically solving PDEs in Mathematica using finite difference methods. Sadly, some types of PDEs are beyond NDSolve’s capabilities. Confronted with one of these PDEs, a user must resort to a more “manual” procedure to find a numerical solution. In this post, we’ll examine a few tricks that can make this process easier. Consider the following PDE:. We seek a solution,. Xgrid = Range[0, 10]; ygrid = Range[0, 10]; grid = Outer[{#1, #2} &;, xgrid, ygrid];. At each grid...
datavoreconsulting.com
On Labor Day, make your computer’s job easier with Milstein’s method | Datavore Consulting
http://datavoreconsulting.com/programming-tips/labor-day-computers-job-easier-milstiens-method
Math, programming, ecology, and more. On Labor Day, make your computer’s job easier with Milstein’s method. In today’s post, we will explore numerical schemes for integrating stochastic differential equations in Mathematica. We will take an informal approach; for an in-depth treatment of stochastic differential equations, I recommend that you look at. Stochastic Processes for Physicists. Modeling with Ito Stochastic Differential Equations. In the realm of stochastic differential equations, there are much...
datavoreconsulting.com
Volume Rendering and Large Data Sets | Datavore Consulting
http://datavoreconsulting.com/general/volumetric-rendering-mathematica
Math, programming, ecology, and more. Volume Rendering and Large Data Sets. Volume rendering, a new feature in Mathematica 9, provides an efficient way to visualize very large data sets. I first learned of this feature from reading Jeffery Bryant’s post. In today’s post, we will use volume rendering for a more prosaic example: color analysis of a photo of puppies. Our picture is not huge, so plotting points would work. With very large data sets, though, plotting individual points becomes unmanageable...
