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#    DISCLAIMER
#    This program is free software; you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation; either version 2 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
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#
#    The R code below was tested on R version 2.10.0
#    September 2010
#
#    Analyses and figures for the study:
#
#    Hohenstein, S., Laubrock, J., & Kliegl, R. (2010).
#	 Semantic preview benefit during eye movements in reading: A parafoveal fast-priming study.
#	 Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 1150-1170.


require(lme4)		# for statistical analyses
require(ggplot2)	# for plotting


rm(list = ls())

#### load data
load('Hohenstein_et_al.2010.rda')


#### INSTRUCTION:

# note: all analyses are done with the object 'b2.rs';
# create this object from the data of either Experiment 1, 2, 3, or 4

## Choose the data set to analyze 
## Experiment 1:
b2.rs <- Exp1
## Experiment 1:
#b2.rs <- Exp2
## Experiment 1:
#b2.rs <- Exp3
## Experiment 1:
#b2.rs <- Exp4


## b2.rs
# the object 'b2.rs' is the main data frame containing all target reading data and is used for most analyses
#
# INDEPENDENT VARIABLES:
# prime: 	preview type (factor)
# p.dur:	prime duration (factor)
# cond:		condition (factor generated by crossing prime type and prime duration)
# id:		subject id
# sn:		sentence id
#
# READING MEASURES:
# o:			initial target fixation position (ranges from 0 to 1)
# refix:		dichtomic variable indicating whether the target was refixated
# tar.GD:		target gaze duration [ms]
# tar.FFD:		target first fixation duration [ms]

## Cmat2b
#  the object 'Cmat2b' is the contrast matrix for experiments with prime durations of 35, 80, and 125 ms;
#  it includes linear and quadratic contrasts for prime durations and prime contrast within each prime duration

## Cmat2c
#  the object 'Cmat2c' is the contrast matrix for the experiment with prime durations of 20, 40, and 60 ms;
#  it includes linear and quadratic contrasts for prime durations and prime contrast within each prime duration





#### STATISTICAL ANALYSES FOR EXPERIMENTS 1 TO 3

## contrasts and global priming effects

# a) target gaze duration
lmer(tar.GD ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2b))
lmer(tar.GD ~ prime + (1 | sn) + (1 | id), data = b2.rs)
# b) target first fixation duration
lmer(tar.FFD ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2b))
lmer(tar.FFD ~ prime + (1 | sn) + (1 | id), data = b2.rs)
# c) target refixation probability
glmer(refix ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2b), family = "binomial")
glmer(refix ~ prime + (1 | sn) + (1 | id), data = b2.rs, family = "binomial")
# d) target landing position
lmer(o ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2b))
lmer(o ~ prime + (1 | sn) + (1 | id), data = b2.rs)



#### STATISTICAL ANALYSES FOR EXPERIMENT 4

## contrasts and global priming effects

# a) pretarget gaze duration
lmer(tar.GD ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2c))
lmer(tar.GD ~ prime + (1 | sn) + (1 | id), data = b2.rs)
# b) target first fixation duration
lmer(tar.FFD ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2c))
lmer(tar.FFD ~ prime + (1 | sn) + (1 | id), data = b2.rs)
# c) target refixation probability
glmer(refix ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2c), family = "binomial")
glmer(refix ~ prime + (1 | sn) + (1 | id), data = b2.rs, family = "binomial")
# d) target landing position
lmer(o ~ cond + (1 | sn) + (1 | id), data = b2.rs, contrasts = list(cond = Cmat2c))
lmer(o ~ prime + (1 | sn) + (1 | id), data = b2.rs)



#### FIGURE FOR EXPERIMENTS 1 TO 4

## Figure: Target gaze duration as as function of the six experimental conditions

# cast data into a proper form
fig.data <- cast(b2.rs, prime + p.dur ~ .,
	      	function(x) c( GD=round(mean(x)), SD = round(sd(x)), SE=round(sd(x)/sqrt(length(x)),+Inf), N=length(x) ))
	      
# extract adjusted SEs for graphics (without iem and subject difference)
model.fig <- lmer(tar.GD ~ prime * p.dur + (1 | sn) + (1 | id), data = b2.rs)  
b2.rs$residuals <- resid(model.fig)

SE.adjusted <- aggregate(b2.rs$residuals, by = list(p.dur = b2.rs$p.dur, prime = b2.rs$prime), function(x) c(sd(x)/sqrt(length(x))))

# add adjusted residual information
fig.data$SE.adj <- SE.adjusted$x

# plot data
p <- ggplot(fig.data, aes(y = GD, x = p.dur, linetype = prime, shape = prime) ) + geom_point(size = 3) + geom_line(aes(group = prime), size = 0.8) + geom_errorbar(aes(min = GD-SE.adj, max = GD+SE.adj, width = 0.2), size = 0.7)
p <- p + scale_x_discrete("Prime duration [ms]") + scale_y_continuous("Gaze duration [ms]") + scale_shape("Prime") + scale_linetype("Prime")
p <- p + opts(legend.position = c(0.2, 0.85))
p