For a blog post on calculating probabilities

exponents_model1.R

## 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 
## 1.913918 3.099787 
c(j2, k2)
## semiweak     mono 
## 1.335928 2.046796
c(j3, k3)
## Gram2semiweak     Gram2mono 
##      1.375976      2.112944 

exponents_model2.R

## What if we don't have any by-word random effects?
mod2 <- glmer(Del ~ Gram2 + (Gram2|Speaker), data = psm, family =binomial(link = cloglog))

j4 <- exp(fixef(mod2)["Gram2semiweak"])
k4 <- exp(fixef(mod2)["Gram2mono"])

c(j1, k1)
## semiweak     mono 
## 1.913918 3.099787 
c(j2, k2)
## semiweak     mono 
## 1.335928 2.046796
c(j3, k3)
## Gram2semiweak     Gram2mono 
##      1.375976      2.112944 
c(j4, k4)
## Gram2semiweak     Gram2mono 
##      1.565090      3.193196 

load_buckeye.R

library(plyr)
library(lme4)
library(RCurl)

b <- read.delim(textConnection(getURL("https://raw.github.com/JoFrhwld/TD-Classifier/master/Data/new_buckeye.txt")))
eval(parse(text = getURL("https://raw.github.com/JoFrhwld/TD-Classifier/master/R/buckCoder.R")))

b <- subset(b, !(FolSeg %in% c("apical")) & PreSeg != "/r/")

psm <- subset(b, Gram2 %in% c("past","semiweak","mono"))

psm$Word <- as.character(psm$Word)

psm$Gram2 <- as.factor(psm$Gram2)
psm$Gram2 <- relevel(psm$Gram2, "past")

nested_mean.R

## Calculate proportion of pronounced /t d/
### First within speaker within grammatical class within word
### Then within speaker within grammatical class
### Then within grammatical class

td.speaker.gram.word <- ddply(psm, .(Speaker, Gram2, Word), summarise,
                              td = mean(td), .progress = "text")
td.speaker.gram <- ddply(td.speaker.gram.word, .(Speaker, Gram2), summarise,
                         td = mean(td))
td.gram <- ddply(td.speaker.gram, .(Gram2), summarise,
                 td = mean(td))

td.gram.v <- td.gram$td
names(td.gram.v) <- td.gram$Gram2

j2 = log(td.gram.v["semiweak"])/log(td.gram.v["past"])
k2 = log(td.gram.v["mono"])/log(td.gram.v["past"])

c(j1, k1)
## semiweak     mono 
## 1.913918 3.099787 
c(j2, k2)
## semiweak     mono 
## 1.335928 2.046796 

pool_all_mean.R

## Calculate proportion of pronounced /t d/, collapsing across all speakers and words
td.mean1.df <- ddply(psm, .(Gram2), summarise, td = mean(td))

td.mean1.v <- td.mean1.df$td
names(td.mean1.v) <- td.mean1.df$Gram2

j1 = log(td.mean1.v["semiweak"])/log(td.mean1.v["past"])
k1 = log(td.mean1.v["mono"])/log(td.mean1.v["past"])

c(j1, k1)
## semiweak     mono 
## 1.913918 3.099787