goldingn

# clear the workspace
rm(list = ls())

# load the relevant libraries

# install.packages(rCUR)
library(rCUR)  # for CUR decomposition
# install.packages(irlba)
library(irlba)  # for fast svd

goldingn

# converting maxent/Poisson model output to true probability of presence

# clear workspace
rm(list = ls())

# set rng seed
set.seed(1)

# number of datapoints
n <- 100000

goldingn

# _____________     ____________       ____________         ___________________
#  \  ______   \__ _\   ______  \     _\   ______  \       _\      ___  \______
#   \       /   \  \ \  \     \  \__ _\ \  \     \  \     _\ \  \/    \  \_____
#    \     /     \  \ \  \     \  \  \ \ \  \     \  \__ _\ \ \  \     \  \____
#     \   /______ \  \ \  \_____\  \  \ \ \  \_____\  \  \ \ \ \  \     \  \___
#      \___________\__\_\___________\__\_\_\___________\__\_\_\_\___________\__
#                      \____________\___\_\____________\___\_\_\____________\__
#                                        \____________\_____\_\____________\___
# SPECIES DISTRIBUTION MODELLING                             \____________\____
#

goldingn

geti <- function (x, i) {
  
  # return the elements of an array `x` of unknown dimension
  # at the ith index of its first dimension
  
  # throw an error if x isn't an array
  stopifnot(is.array(x))
 
  # get the dimension of x
  k <- length(dim(x))

goldingn

fuzzyMatch <- function (a, b) {
  
  # no-frills fuzzy matching of strings between character vectors
  # `a` and `b` (essentially a wrapper around a stringdist function)
  # The function returns a two column matrix giving the matching index
  # (as `match` would return) and a matrix giving the distances, so you
  # can check how well it did on the hardest words.
  # Warning - this uses all of your cores.
  
  # load the stringdist package

goldingn

# load devtools and install the preferred version of circleplot
library(devtools)
install_github('mjwestgate/circleplot')
#install_github('goldingn/circleplot')
library(circleplot)

# set RNG seed
set.seed(1)

# create a fake symmetric matrix

goldingn

# set RNG seed
set.seed(1)

# make some fake data
n <- 100
m <- 5
y <- rbinom(n, 1, 0.85)
x <- sample(letters[1:m], n, replace = TRUE)

# fit a logistic regression model

goldingn

#!/bin/bash

# modify RStudio's options to send plots to quartz by default,
# and open help and local html in the default browser

echo "
# ~~~~~~~~~~~
# Tweaks to view help and html in the browser, and plots in quartz, not the RStudio viewer pane
browser <- '/usr/bin/open'
device <- 'quartz'

goldingn

#!/bin/bash

# Trigger an Rstudio-server instance in docker, linked to the current filesystem
# and open in a browser.

# **WARNING** this will create a .gitconfig file in the working directory, potentially overwriting one that's already there!
# it will also create a .rstudio folder, and possibly other things

# This script should be run from the Docker Quickstart Terminal on OSX.
# I.e. it uses the an up-to-date docker installation which handles the VM side of things, *not boot2docker*

goldingn

# make a nice viridis GMRF tesselation for Richard
rm(list = ls())
set.seed(1)

library(INLA)
library(raster)
library(viridis)
library(fields)

# grid sizes for sampling the GRF and for the final image