JoFrhwld

scored_vclass <- function(df,class,vowels, dims = c("F1","F2")){
	require(MASS)
	df <- df[df[ ,class] %in% vowels, ]
	

	df[ ,class] <- as.character(df[ ,class])
	df[,class] <- as.factor(df[,class])
	
	
	for(i in dims){

JoFrhwld

clip.border <- function(deldir.obj, border){
	## deldir.obj = output of deldir::deldir
	## border = list of borders defined in data frames
	##		Importantly, with columns called "x" and "y" 
	require(gpclib)

	## bord will be a gpc polygon of all of the regions given to the argument "border"
	bord <- as(matrix(ncol = 2), "gpc.poly")
	for(i in seq(along = border)){
		b <- as(border[[i]], "gpc.poly")

JoFrhwld

##########
## Block 1: Preparing the data

library(plyr)
library(reshape)

## Create a Subsystem Column
all_philly$Subsystem <- all_philly$VClass
levels(all_philly$Subsystem) <- c("Vhr", "Vw", "V", "misc", "Vh", "Vh", "Vhr", "Vw", "Vy", 
"Vy", "V", "Vy", "Vy", "Vy", "V", "Vw", "Vy", "Vy", "Vhr", 

JoFrhwld

library(rgdal) #This may be a pain to get installed. The OGR functions come from it.
## install gdal, available here: http://www.gdal.org/
## install proj.4, available here: http://trac.osgeo.org/proj/
## For some reason, R wasn't expecting for libproj to be installed where it was,
## so I had to tell it to look at /usr/local/bin

library(ggplot2)
library(reshape2)
library(plyr)
gpclibPermit()

JoFrhwld

library(rgdal) #This may be a pain to get installed. The OGR functions come from it.
## install gdal, available here: http://www.gdal.org/
## install proj.4, available here: http://trac.osgeo.org/proj/
## For some reason, R wasn't expecting for libproj to be installed where it was,
## so I had to tell it to look at /usr/local/bin

library(ggplot2)
library(reshape2)
library(plyr)
gpclibPermit()

JoFrhwld

## ggplot2 and mgcv for the plot
library(ggplot2)
library(mgcv)

## scales packages to define revlog
library(scales)

revlog_trans <- function(base = exp(1)){
    ## Define the desired transformation.
    trans <- function(x){

JoFrhwld

\documentclass{beamer}
\usetheme{Singapore}
\usepackage{tipa}

%% Make the r-code small
\ifdefined\knitrout
  \renewenvironment{knitrout}{\begin{footnotesize}}{\end{footnotesize}}
\else
\fi

JoFrhwld

library(ggplot2)
library(plyr)

## Set your effect size, and desired p-value threshold here
effect = 0.1
thresh = 0.05

## Sets up the simulation parameters
pars = list(mean1 = 1, mean2 = 1+effect, sd1 = 1, sd2 = 1)
nsim = 1000

JoFrhwld

## You need to model the "failures"
psm$Del <- 1-psm$td

mod <- glmer(Del ~ Gram2 + (Gram2|Speaker) + (1|Word), data = psm, family = binomial(link = cloglog))

j3 <- exp(fixef(mod)["Gram2semiweak"])
k3 <- exp(fixef(mod)["Gram2mono"])

c(j1, k1)
## semiweak     mono 

JoFrhwld

ggplot(Vy, aes(F2, F1, shape = Vowel))+
  geom_point()+
  theme_bw()+
  scale_x_continuous(name = "F2", trans = revlog_trans())+
  scale_y_reverse(name = "F1")+
  stat_ellipse(level = 0.95)+
  opts(legend.position = "none")+
  annotate(geom="text", x=2900, y = 460, label = "iyC", size = 5)+
  annotate(geom="text", x=2400, y = 750, label = "eyC", size = 5)+
  annotate(geom="text", x=1850, y = 970, label = "ay", size = 5)