[R-sig-ME] Free statistical analysis material?

[Luca Danieli]

Hello everybody,

I am trying the difficult task to conclude an interdisciplinary PhD.
Statistics looks nice, and I have learned a lot about the basic principles and methodologies, and how they work.

But I miss a lot. In particular all the little variations and methods due to interpretations and methodologies (for example now I am looking at the function of contrasts in mixed-effects models), and generally, from theory to applied statistics there is an incredible gap.

Is anybody in this list (as I don't really have a mentor on statistics nor I know statisticians) be able to point me to some free materials (books, tutorials) to study the topic in detail, but not too much in detail?

For example, in this moment, I am trying to figure the following script out. I understand it on its general lines, but there are really obscure points in my head on understanding the "why".
In the following example, what I don't understand is just the contrasts, but the person who is following me (who is a very nice person) has given me the task to figure out the best way to make a contrast "2 conditions > 6 conditions". She has suggested some guessing, but she is not a specialist.

I was thinking that maybe you that are specialists know some free not-too-long source that I could read to move around.

----

library(lmerTest)

str(datasheet.complete)
# set Score as numeric
datasheet.complete$Score = as.numeric(datasheet.complete$Score)

levels(datasheet.complete$Closure)

# closure contrasts
cl_c1 = c(-1/3,-1/3,-1/3,1)
cl_c2 = c(-1/2,-1/2,1,0)
cl_c3 = c(-1,1,0,0)
closuremat.temp = rbind(constant = 1/4,cl_c1,cl_c2,cl_c3)
closuremat = solve(closuremat.temp)
closuremat = closuremat[, -1]
closuremat

# expertise contrasts

exp_c1 = c(-1/2,-1/2,1)
exp_c2 = c(-1,1,0)
expmat.temp = rbind(constant = 1/3,exp_c1,exp_c2)
expmat = solve(expmat.temp)
expmat = expmat[, -1]
expmat

# set contrast
contrasts(datasheet.complete$Closure) = closuremat
contrasts(datasheet.complete$ExpertiseType) = expmat


modela = lmer(Score~1+(1|Participant)+(1|Item), data = datasheet.complete, REML = TRUE)
modelb = update(modela,.~.+ExpertiseType)
modelc = update(modelb,.~.+Closure)
modeld = update(modelc,.~.+ExpertiseType*Closure)

anova(modela,modelb,modelc,modeld)

model = lmer(Score~Closure*ExpertiseType+(1|Participant)+(1|Item), data = datasheet.complete, REML = TRUE)
summary(model)


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