Reorganize preprocessing; use pair as random factor#62
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largescaledesigned.qmd
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| The other variables are covariates that have been shown to affect LDT accuracy and reaction times. Available are: | ||
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| + Ortho_N: numer of orthographic neighbors |
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| + Ortho_N: numer of orthographic neighbors | |
| + Ortho_N: number of orthographic neighbors |
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largescaledesigned.qmd
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| describe(elpldtitem) | ||
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| We incoporate `item`, `pair`, `isword`, and `wrdlen` into `ldttrial` using `item` as the key variable. |
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| 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. |
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largescaledesigned.qmd
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| qqcaterpillar!(Figure(; size=(600, 450)), ranefinfo(elm02, :subj)) | ||
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| 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 🐶
| 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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The way I would calculate age of participant at the time of first test is |
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@kliegl I have a suggested change for computing |
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@kliegl You have a note after the discussion of the |
Yes, I agree. |
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