DL Miller dill

## andy.R
library(mgcv)
set.seed(2) ## simulate some data...
dat <- gamSim(1,n=400,dist="normal",scale=2)
b <- gam(y~s(x1), data=dat, fit=FALSE)

newpred <- data.frame(x1=seq(0, 10, length.out=100))

# Lp for the data
lp_data <- PredictMat(b$smooth[[1]], dat)

## tax.R
# Tax data from NYT article

# data scraped from https://www.nytimes.com/interactive/2019/10/06/opinion/income-tax-rate-wealthy.html

# load data scraped from SVG
tax <- readLines("taxdat.dat")


taxy <- c()
for(i in 1:length(tax)){

## cloudy_day.R
# generate some relaxing cloudy sky squares
#
# David Lawrence Miller (2019)

library(INLA)
library(ggplot2)

## function to simulate spatial fields
simdat <- function(n, seed, prior.range = c(5, .05),
                   prior.sigma = c(.05, .05)){

## good_modelz.R
# data digitized from https://twitter.com/nberpubs/status/1062080096549261312
# thanks @tpoi and co! https://cran.r-project.org/web/packages/digitize/index.html

# load the above data
dat <- read.csv("nber.csv")

# it's ya boi, mgcv
library(mgcv)

# fit the next stupidest model

## dat.csv

          
            tax_rate
             tax_rev

            
               0.07357329
               -0.0567062

            
               8.81285407
                2.4505785

            
              12.84385295
                3.5807126

            
              16.23680790
                2.1860302

            
              19.18260073
                2.4643621

            
              19.29050823
                1.9514672

            
              18.45218145
                1.2480201

            
              26.73285538
                1.5886201

            
              22.37159417
                3.3657369

## frank_hats.csv

          
            Hat
             Season
             Episode

            
              Thick
               6
               9

            
              Wet To Activate
               4
               3

            
              Ho Ho Horny
               2
               9

            
              Horny
               3
               1

            
              Horny
               3
               1

            
              Rods
               1
               9

            
              Feet
               6
               6

            
              Feet
               6
               7

            
              Squeeze It
               1
               15

## preds.R
# average predictions
library(Distance)
library(dsm)

# download data from http://workshops.distancesampling.org/stand-intermed-2018/practicals/spermwhale.RData
load("spermwhale.RData")

# make sure we get the same results every time
set.seed(123)

## thingo.R
# little animation to illustrate uncertainty in a density map

library(dsm)
library(mvtnorm)
library(ggplot2)
library(animation)
library(viridis)
library(cowplot)

set.seed(1997)

## contour_experiment.R
###


n <- 200

add_layer <- function(coords=data.frame(x=0, y=0), layer=0){
  so_rad <- seq(0, 2*pi-0.1, len=n)

  if(layer==0){
    lens0 <- 0.1*runif(1, 0.1, 1)

## figure-captions.R
## this code makes figure captions work in HTML output

# adapted from http://gforge.se/2014/01/fast-track-publishing-using-knitr-part-iii/
library(knitr)
options(figure_counter = TRUE)

# Evaluate the figure caption after the chunk,
# sometimes you want to calculate stuff inside the
# chunk that you want to include in the caption and
# it is therefore useful to evaluate it afterwards.
	library(mgcv)
	set.seed(2) ## simulate some data...
	dat <- gamSim(1,n=400,dist="normal",scale=2)
	b <- gam(y~s(x1), data=dat, fit=FALSE)

	newpred <- data.frame(x1=seq(0, 10, length.out=100))

	# Lp for the data
	lp_data <- PredictMat(b$smooth[[1]], dat)
	# Tax data from NYT article

	# data scraped from https://www.nytimes.com/interactive/2019/10/06/opinion/income-tax-rate-wealthy.html

	# load data scraped from SVG
	tax <- readLines("taxdat.dat")


	taxy <- c()
	for(i in 1:length(tax)){
	# generate some relaxing cloudy sky squares
	#
	# David Lawrence Miller (2019)

	library(INLA)
	library(ggplot2)

	## function to simulate spatial fields
	simdat <- function(n, seed, prior.range = c(5, .05),
	prior.sigma = c(.05, .05)){
	# data digitized from https://twitter.com/nberpubs/status/1062080096549261312
	# thanks @tpoi and co! https://cran.r-project.org/web/packages/digitize/index.html

	# load the above data
	dat <- read.csv("nber.csv")

	# it's ya boi, mgcv
	library(mgcv)

	# fit the next stupidest model
	tax_rate	tax_rev
	0.07357329	-0.0567062
	8.81285407	2.4505785
	12.84385295	3.5807126
	16.23680790	2.1860302
	19.18260073	2.4643621
	19.29050823	1.9514672
	18.45218145	1.2480201
	26.73285538	1.5886201
	22.37159417	3.3657369
Hat	Season	Episode
Thick	6	9
Wet To Activate	4	3
Ho Ho Horny	2	9
Horny	3	1
Horny	3	1
Rods	1	9
Feet	6	6
Feet	6	7
Squeeze It	1	15
	# average predictions
	library(Distance)
	library(dsm)

	# download data from http://workshops.distancesampling.org/stand-intermed-2018/practicals/spermwhale.RData
	load("spermwhale.RData")

	# make sure we get the same results every time
	set.seed(123)
	# little animation to illustrate uncertainty in a density map

	library(dsm)
	library(mvtnorm)
	library(ggplot2)
	library(animation)
	library(viridis)
	library(cowplot)

	set.seed(1997)
	###


	n <- 200

	add_layer <- function(coords=data.frame(x=0, y=0), layer=0){
	so_rad <- seq(0, 2*pi-0.1, len=n)

	if(layer==0){
	lens0 <- 0.1*runif(1, 0.1, 1)
	## this code makes figure captions work in HTML output

	# adapted from http://gforge.se/2014/01/fast-track-publishing-using-knitr-part-iii/
	library(knitr)
	options(figure_counter = TRUE)

	# Evaluate the figure caption after the chunk,
	# sometimes you want to calculate stuff inside the
	# chunk that you want to include in the caption and
	# it is therefore useful to evaluate it afterwards.