# Demonstrate effect of number of observation on credibility interval and on amount of shrinkage
library(lme4)
library(reshape)

rm(list=ls())

load("MaskedPriming.rda")
d <- subset(d, e=="1")       # Keep only data from Exp 1

# LM - w/ all data
(feID.1 <- coef(lmList(rt ~ 1 | subjects, data=d))[1])

# LMM - w/ full data
(m1 <- lmer(rt ~ 1 + (1|subjects), data=d))
(reID.1 <- ranef(m1, postVar=TRUE))
dotplot(reID.1, scales = list(x = list(relation = 'free', rot=0, cex=1), y = list(cex=0.5) ) )

# Remove random quarter of trials for 2 extreme and 1 medium subjects
ix <- which(as.numeric(d$subjects) %in% c(5, 7, 9))
b <- d[-sample(ix, length(ix)*3/4), ]

# LM - w/ missing data
(feID.2 <- coef(lmList(rt ~ 1 | subjects, data=b))[1])

# LMM - w/ missing data
(m2 <- lmer(rt ~ (1|subjects), data=b))
(reID.2 <- ranef(m2, postVar=TRUE))
dotplot(reID.2, scales = list(x = list(relation = 'free', rot=0, cex=1), y = list(cex=0.5) ) )

# Combine means in wide format
df <- data.frame(cbind(unlist(feID.1), unlist(reID.1)+fixef(m1), unlist(feID.2), unlist(reID.2)+fixef(m2)))
rownames(df) <- substr(rownames(df), 12,13)
names(df) <- c("feID.1", "reID.1", "feID.2", "reID.2")
df$id <- as.numeric(rownames(df)) 

# Convert to long format
df.rs <- melt(df, id.var="id", measure.var=c("feID.1", "reID.1", "feID.2", "reID.2"))

# Plot the four means
dotplot(id ~ value, data=df.rs, group=variable, ref=TRUE, auto.key = list(columns=2), #auto.key=TRUE,  
	    scales = list(x = list(relation = 'free', rot=0, cex=1), y = list(cex=.5) ), pch=16 )