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

\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)

JoFrhwld

list2fd <- function(list, basis){
  if(class(list[[1]]) == "fdSmooth"){
    coef_list <- lapply(list, function(x)x$fd$coefs)
  }else if(class(list[[1]]) == "fd"){
    coef_list <- lapply(list, function(x)x$coefs)
  }
  n_coefs <- unlist(lapply(coef_list, length))

  if(!all(n_coefs == max(n_coefs))) stop()
  

JoFrhwld

#' Find zero crossings in an fd object
#' 
#' @import fda
#' @import magrittr
#' 
#' @param fd an fd object
#' @param Lfdobj the derivative (0, 1, 2)
#' @param slope The slope of interest at the zero crossing
#' @param eps The prediction granularity
#' @param min Localize the zero crossing search to be greater than min

JoFrhwld

#### Values created by statistics
Statistical layers added to plots actually create new pieces of data, like the y-coordinates of the smoother. Some statistical layers create a few different values, and you can choose which one you want to plot. For example, here is a density plot, where the kernel density estimate is represented by a colored line.

```{r tidy = F, fig.width = 8/1.2, fig.height=5/1.2}
ggplot(I_jean, aes(Dur_msec, color = Word))+
  geom_density()
```

You have to understand the densities represented in this plot as being conditional on selecting a specific word. That is, given that we have decided to think about the lexical item "I've", what is the probability it will be found in a specific range of durations? 

JoFrhwld

# http://www.cdc.gov/nchs/data/nhsr/nhsr010.pdf

n_fem = 604
h_fem = 162.2
se_fem = 0.34
sd_fem = se_fem * sqrt(n_fem)

n_mal = 591
h_mal = 176.6
se_mal = 0.38