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Author Archives: robayedavies
Modelling – mixed effects – concepts
I am going to take another shot at considering mixed effects modelling. This time from a perspective closer to my starting point. I first learnt about mixed-effects modelling through reading about it in, I think, some paper or chapter by … Continue reading
Posted in 16. Modelling - mixed-effects
Tagged anova(), F', lmer(), minF', mixed effects, mixed-e, regression
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Picture break
Picture taken in San Francisco flower conservatory, a wonderful place with wonderfully informative guides. We will go on to a bit more model checking before considering the random effects formally.
Posted in .Interim directions
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Mixed effects – continuing the extended example
As noted in a previous post, our mixed effects analysis of the ML lexical decision data suggested that RTs were influenced by significant effects due to: cAge + cART + cTOWRENW + cLength + cOLD + cBG_Mean + cLgSUBTLCD + … Continue reading
Modelling – mixed effects – concepts
In the previous post, we ran through an extended example of a mixed-effects modelling analysis of the ML lexical decision data. We ended the post by getting p-values for the effects of the predictors included in our model. That was … Continue reading
Posted in 16. Modelling - mixed-effects
Tagged hierarchical, mixed effects, multilevel
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Modelling – extended example mixed-effects analysis
In the previous post, we ran through an example of a mixed-effects analysis completed using the lmer() function from the lme4 package (Bates, 2005; Bates, Maelchler & Bolker, 2013). We will not, yet, really fulfill the promise to develop our … Continue reading
Posted in 16. Modelling - mixed-effects
Tagged AIC, anova(), BIC, chi-sq, fixed effects, lmer(), MCMC, random effects
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Modelling – example mixed-effects analysis
In the last post, I showed how to collate the various data bases we constructed or collated from the data collection achieved in a study of lexical decision. We ended the post by producing a .csv file of the output … Continue reading
Modelling – look ahead to mixed-effects modelling
So far, we have been looking at the participant attributes or the item attributes for our lexical decision study. It is time to move on to consider a mixed-effects analysis examining whether or how lexical decision responses were influenced by: … Continue reading
Posted in 16. Modelling - mixed-effects
Tagged ddply(), length(), levels(), merge, summary
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Modelling – ordinary least squares regression
Let’s work on our understanding of regression while we work through some examples. We have two datasets to work with, one on the attributes of the participants of a lexical decision study (their age, reading skill etc.), and one on … Continue reading
Modelling – ordinary least squares regression in R
We will be looking at how you can do regression in R. More than one function call will do this, lm() and ols() in R. Don’t ask me why, I might find out another time. Ordinary least squares regression: — … Continue reading
Modelling – some conceptual foundations
We have discussed the relationships between pairs of variables, we will now move on to analyzing our data using linear regression. Slides on regression can be downloaded here. You will see in those slides that I rely very heavily on … Continue reading
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