Free statistical analysis material?
Contrasts are confusing, and not specific to LMMs. You might see if http://bbolker.github.io/mixedmodels-misc/notes/contrasts.pdf helps at all. (From a quick glance at your question & code below, I'm not sure what you mean by "2 conditions > 6 conditions" ???)
On 2018-05-14 03:48 PM, Luca Danieli wrote:
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) [[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models