dill

# get UN resolutions

# Annoyingly the UN website is not very crawl-able.
# Using RSelenium you can download Resolution data

library(RSelenium)
library(stringr)

# download directory
dl_dir <- path.expand("~/Downloads/UN69/")

dill

## This is a very silly script to get JSON data for an R twitter bot that produces
## tweets of the form "verb that noun" using verbs from package:base and nouns from
## built in R data.
##
## It uses @v21's http://cheapbotsdonequick.com/
##
## David L. Miller 2015 MIT license
## update from Nick Golding 2018

cat("{\n")

dill

library(rphylopic)

# get a (sperm) whale! (this is "Physeter catodon" by Noah Schlottman)
# http://phylopic.org/image/dc76cbdb-dba5-4d8f-8cf3-809515c30dbd/
whale <- image_data("dc76cbdb-dba5-4d8f-8cf3-809515c30dbd", size=512)

# failwhale
image(whale[[1]][,,4])

dill

# makes plots (somewhat) like those in ver Hoef and Boveng (2007)

# Jay M. Ver Hoef and Peter L. Boveng 2007. QUASI-POISSON VS. NEGATIVE BINOMIAL REGRESSION: HOW SHOULD WE MODEL OVERDISPERSED COUNT DATA? Ecology 88:2766–2772. http://dx.doi.org/10.1890/07-0043.1
# http://www.utstat.utoronto.ca/reid/sta2201s/QUASI-POISSON.pdf


# calling with something like: 

# par(mfrow=c(1,2))
# # define the breaks

dill

# Markov Random Fields with temporal interactions

# David L Miller 2015
# Released under MIT license, YMMV


# example from ?mgcv::smooth.construct.mrf.smooth.spec
library(mgcv)
## Load Columbus Ohio crime data (see ?columbus for details and credits)
data(columb)       ## data frame

dill

# David L Miller 2015, MIT license

library(numDeriv)
library(animation)

# function taken from the "Gu & Wahba 4 term additive example" from mgcv::gamSim
f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10

xvals <- seq(0,1,len=100)

dill

# example from ?mgcv::concurvity

library(mgcv)
## simulate data with concurvity...
set.seed(8);n<- 200
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
            (10 * x)^3 * (1 - x)^10
t <- sort(runif(n)) ## first covariate
## make covariate x a smooth function of t + noise...
x <- f2(t) + rnorm(n)*3

dill

# plot of size versus distance and sea state versus distance
# w. linear model and LOESS smoother overlay
# base graphics
par(mfrow=c(1,2))

plot(distdata[c("size","distance")], xlab="Group size", ylab="Distance (m)",pch=19,col=rgb(0,0,0,0.4), cex=0.6)
# increase span from default 0.75 for slightly smoother curve
lo <- loess(distance ~ size, distdata, span=0.8)
lmm <- lm(distance ~ size, distdata)
preddat <- data.frame(size=seq(0,8,1))

dill

### analysis of ribbon seal data using a frequentist
### approach to a GAM

### David L Miller dave@ninepointeightone.net
### License: GNU GPL v3


# load data from
# https://github.com/pconn/SpatPred/blob/master/SpatPred/data/Ribbon_data.rda
load("Ribbon_data.rda")

dill

load("best_model.Rdata")
library(dsm)
library(raster)

# lazily get the plot data for the rug plot
plotdat <- plot(M)

# load the raster and mudge it into the format I want
dists <- stack("NA_Shore_Dist_10km_mean_10km.img")
dists <- as.data.frame(dists)