dill’s gists

dill / README

Last active August 28, 2024 12:27

McDonnellbot

Thanks to wikipedia for the data :)

dill / all_the_single_palettes.txt

Last active November 11, 2015 16:21

👑🐝 All the single palettes 👑🐝 (one line per palette)

dill / exif_plot.R

Created November 8, 2015 04:35

Goofy plotting of EXIF data

	# get EXIF and make a map

	library(leaflet)
	library(lubridate)
	library(plyr)

	# all my photos are in a directory pre with subdirectories
	pre <- "~/Dropbox/Photos/"
	paths_to_photos <- c("some_sub_directory"
	)

dill / extra_plot.R

Last active October 29, 2015 13:29

Make GAM term plots that extend outside the range of the data to show how bad extrapolation is for Laura M

	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)

dill / givh-gam.R

Created October 26, 2015 22:04

Code to to gIVH with mgcv see http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141416

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

	### David L Miller [email protected]
	### License: GNU GPL v3


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

dill / base_graphics.R

Last active October 14, 2015 19:41

Comparison of plotting covariates against distance, base graphics vs. ggplot2

	# 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 / example.R

Last active August 27, 2016 17:00

Visualising concurvity between terms in a GAM

	# 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 / wiggly.R

Last active October 9, 2015 14:43

Goofy animation of integrating 2nd derivatives for a GAM penalty

	# 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) 0.2x^11(10(1-x))^6+10(10x)^3(1-x)^10

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

dill / mrf-time.R

Created September 4, 2015 18:50

Example of Markov Random Field with temporal interaction

	# 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 / verhoef.R

Created August 28, 2015 03:18

ver Hoef and Boveng (2007) plots

	# 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

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