= Tutorial on using Mixed Models ANOVA in R =

== Sample data ==

Here is the header and first 10 lines of data from the file `cleaned_small_1.csv`.
(The names of the variables will be read from the header line and have been shortened
to a maximum of 7 characters just for legibility here.)

{{{
Subject	Session	Respons	RT	Trial	Item	Accur	Task	Freq	N	LexStat
b	2	4	639	2	earn	correct	S	H	L	W
b	2	5	596	3	suif	correct	S		L	P
b	2	5	407	4	nomad	error	S	L	L	W
b	2	4	432	5	monk	correct	S	L	L	W
b	2	4	509	6	gentle	correct	S	H	H	W
b	2	5	699	7	lafe	correct	S		H	P
b	2	5	990	8	tase	correct	S		H	P
b	2	4	430	9	animal	correct	S	H	L	W
b	2	5	705	10	swinch	correct	S		H	P
b	2	4	566	11	lesson	correct	S	H	H	W
...
}}}

Here's the structure:

 *'''Subject''': 15 subjects.
 *'''Session''': 2,3,5,6. The experiment contains four sessions. 
   *Session 2 and 3 are normal lexical decision task in which participants are told to distinguish between words and pseudowords. 
   *Session 5 and 6 are lexical decision with a deadline in which participants are told to make a very quick response (before 460ms). 
 *'''Respons''': which button they pressed. Participants are instructed to press right button when they see words and press the left button when they see pseudowords. 
   *4=right hand
   *5=left hand. 
 *'''Trial''': order of stimuli presentation in each session
 *'''Item''': words or pseudowords presented to participants
 *'''Accur''': correct responses or errors
 *'''Task''': 
   *S=normal lexical decision
   *D=lexical decision with a deadline
 *'''Frequency''': 
   *H=high frequency words
   *L=low frequency words
   *Pseudowords don't have this parameter. 
 *'''N''': 
   *L=low neighbourhood size
   *H=high neighbourhood size.
 *'''!LexStat''':
   *W=words
   *P=pseudowords. 

== Importing the data ==

{{{
datafile <- "~/mixed_models_in_R/cleaned_small_1.csv"
sampledata <- read.table(datafile, head=T, row.names=NULL, sep=",")
# sampledata is a 'table', one of the basic R data structures.

names(sampledata)
# the list of variables in data

sampledata$session <- as.factor(sampledata$Session)
# session was interpreted as a numeric variable but we want to
# treat it as a factor - checked by is.factor(sampledata$session) evaluating as FALSE

sampledata$RT <- as.numeric(sampledata$RT)
# RT was interpreted as a factor because there is a 'space' lurking
# somewhere as one of its values - checked by in.numeric(RT) evaluating as FALSE  
}}}

attach(sampledata) 
# this means we can reference variables directly
# e.g. Subject rather than sampledata$Subject

== Simple data checking ==

{{{
hist(RT)
}}}


produces this histogram which was saved as a png graphics file 
[[attachment:Histo_RT.png|{{attachment:Histo_RT.png|histogram|width=450}}]]

== Resources ==

[[/Resources]]