| Proposal for new DSM syntax | |
| The current syntax for dsm.fit is based on pretty old code, I think that it makes sense (especially as we add more functionality) to make a change to the way models are specified. The basic idea is to make things look more like mgcv::gam(). | |
| Currently things like the family and knots are specified using lists of lists, this involves lots of typing and doesn't make much sense if you're used to fitting GAMs using mgcv. This becomes even more of a pain if we want to start using gamm() for mixed models (I do anyway...) | |
| Proposal: | |
| dsm(formula, ddf.obj, segment.data, observation.data, engine=c("gam","gamm","glm"), convert.units=1, family=quasipoisson(link="log"), group=FALSE, ...) |
| # watch all .Rmd files in a directory, if any change, knit them | |
| library(knitr) | |
| # use watch from Hadley's testthat package | |
| library(testthat) | |
| watchr<-function(path){ | |
| # the path supplied here can just be something like "./" |
** THIS DOESN'T WORK ANY MORE ** Recent updates to XCode seem to have stopped this from working. I am attempting to get this working again. Updates will be posted here.
The version of gcc that ships with Mac OS X doesn't have openMP enabled, which means you can't use "helper threads", which make pqR super-fast. So, we first need to install that and get OS X to use it...
Get homebrew so you can install a newer version of gcc, then:
| #!/bin/bash | |
| # alarm clock script | |
| # | |
| # requires: get-iplayer | |
| # git clone git@github.com:dinkypumpkin/get_iplayer.git | |
| # get_iplayer --prefs-add --rtmp-tv-opts="--swfVfy=http://www.bbc.co.uk/emp/releases/iplayer/revisions/617463_618125_4/617463_618125_4_emp.swf" | |
| # need to install rtmpdump |
| # use MVB's foodweb to build a graphviz-compatible "dot" file | |
| # this can then be used to generate a svg of the graph using: | |
| # $ dot -Tsvg file.dot -o file.svg | |
| # example usage: | |
| # fw2dot("mrds","mrds.dot") | |
| library(mvbutils) | |
| fw2dot <- function(package.name,filename){ |
| # animate a (very boring) distance sampling survey | |
| # watch a gif at: http://converged.yt/distance-animation.gif | |
| # written by David Lawrence Miller, with tweaks from Eric Rexstad | |
| # released under the MIT license | |
| library(animation) | |
| # make sure that we get the same result every time... | |
| set.seed(1234) |
| # R plotting routines for the package mgcv (c) Simon Wood 2000-2010 | |
| ## With contributions from Henric Nilsson | |
| plot.mgcv.smooth <- function(x,P=NULL,data=NULL,label="",se1.mult=1,se2.mult=2, | |
| partial.resids=FALSE,rug=TRUE,se=TRUE,scale=-1,n=100,n2=40, | |
| pers=FALSE,theta=30,phi=30,jit=FALSE,xlab=NULL,ylab=NULL,main=NULL, | |
| ylim=NULL,xlim=NULL,too.far=0.1,shade=FALSE,shade.col="gray80", | |
| shift=0,trans=I,by.resids=FALSE,scheme=0,...) { | |
| ## default plot method for smooth objects `x' inheriting from "mgcv.smooth" |
Here are some tips on getting R development working with Yosemite. Contribute what you know below and I'll add it in.
I went ahead and re-installed all of my homebrew. You can find out what you have installed with
Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal and plant distributions.
Here I'll give an overview of "density surface models", which consist of a spatial model of the abundance which has been corrected for uncertain detection via distance sampling methods. The spatial model consists of a generalised additive (mixed) model, which can include various components, such as smooth terms and random effects.
In particular, I'll highlight modelling techniques that may be advantageous to applied ecologists: quantification of uncertainty in a two-stage model, correction for availability bias, alternative response distributions, autocorrelation and smoothing in areas with complex bound
Many models that we build rely on combining several components, often representing different processes at work in the system. For example in ecology we may have a component for detectability of the species by observers, one for whether the animals are available, one describing their spatial distribution.
Defining such a complex model in R can result in writing a very long call to a particular function, which may or may not work at any point. Since these processes are often (conditionally) independent, we can separate these components in the likelihood and code. Using the + operator we can then combine them in a simple way that's easy for those doing the modelling to understand. Since each step produces an object, we can perform model validation, diagnostics and checking during construction.
Through an example applied to distance sampling, I'll show an example of this kind of approach to model building and propose some other situations where this kind of strategy may
