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Reorganize preprocessing; use pair as random factor #62

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@kliegl kliegl commented May 19, 2024

First draft of slight reorganization of preprocessing
Adding square of wrdlen to fixed effects; there is some link to literature
First draft of using pair as random factor

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kliegl and others added 2 commits May 19, 2024 21:50
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

The other variables are covariates that have been shown to affect LDT accuracy and reaction times. Available are:

+ Ortho_N: numer of orthographic neighbors

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[Typos] reported by reviewdog 🐶

Suggested change
+ Ortho_N: numer of orthographic neighbors
+ Ortho_N: number of orthographic neighbors

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
describe(elpldtitem)
```

We incoporate `item`, `pair`, `isword`, and `wrdlen` into `ldttrial` using `item` as the key variable.

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[Typos] reported by reviewdog 🐶

Suggested change
We incoporate `item`, `pair`, `isword`, and `wrdlen` into `ldttrial` using `item` as the key variable.
We incorporate `item`, `pair`, `isword`, and `wrdlen` into `ldttrial` using `item` as the key variable.

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
qqcaterpillar!(Figure(; size=(600, 450)), ranefinfo(elm02, :subj))
```

Both `isword` and `wrdlen` vary within subjects and between items. We can estimate variance components (VCs) and correlation paramters (CPs) for this complex LMM and check whether they are supported by the data and increase the goodness of fit of the model.

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[Typos] reported by reviewdog 🐶

Suggested change
Both `isword` and `wrdlen` vary within subjects and between items. We can estimate variance components (VCs) and correlation paramters (CPs) for this complex LMM and check whether they are supported by the data and increase the goodness of fit of the model.
Both `isword` and `wrdlen` vary within subjects and between items. We can estimate variance components (VCs) and correlation parameters (CPs) for this complex LMM and check whether they are supported by the data and increase the goodness of fit of the model.

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dmbates commented May 20, 2024

The way I would calculate age of participant at the time of first test is

function age_at_test(DOB, S1start)
    (byr, bmo, bday) = yearmonthday(DOB)
    (syr, smo, sday) = yearmonthday(S1start)
    ydiff = syr - byr
    ((smo < bmo) || (smo == bmo && sday < bday)) && return ydiff - 1
    return ydiff
end

transform!(elpldtsubj, [:DOB, :S1start] => ByRow(age_at_test) => :age)

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dmbates commented May 21, 2024

@kliegl I have a suggested change for computing age_at_event and the :age column for the subjects. Is it okay for me to commit directly to this branch or would you prefer me to create a branch from this branch and commit there?

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dmbates commented May 21, 2024

@kliegl You have a note after the discussion of the elpldtitem table on word frequencies, etc. The reason they are not included here is because they are only defined for words. We could determine the value for the word and assign it to both the word and non-word in a pair.

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kliegl commented May 21, 2024

@kliegl You have a note after the discussion of the elpldtitem table on word frequencies, etc. The reason they are not included here is because they are only defined for words. We could determine the value for the word and assign it to both the word and non-word in a pair.

Yes, I agree.

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3 participants