# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment
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Support the auto combination of numeric variables #17
[x] Check is that a unique numeric variable which one of the covariance data frame.
[x] Add return outcomes which one is real numeric variable (ex. age). -- If False Positive, That doesn't matter in estimation. Because that's just a trial, not harm.
Details
https://github.com/seonghobae/kaefa/blob/da6bf9df8acbdbd32f00c4c44b6f7e84b6efa8f5/R/utils.R#L3-L68
In current,
kaefa:::.covdataFixedEffectCombreturns a list which includes the classified categorical variable candidates for fixed and random effect.So, In currently, Support the auto combination of numeric variables required.
Especially, L9 to L16 was incomplete.
https://github.com/seonghobae/kaefa/blob/da6bf9df8acbdbd32f00c4c44b6f7e84b6efa8f5/R/utils.R#L9-L16
Todo
[x] Check is that a unique numeric variable which one of the covariance data frame.
[x] Add return outcomes which one is real numeric variable (ex. age). -- If False Positive, That doesn't matter in estimation. Because that's just a trial, not harm.
[x] Link with combination function in internal. https://github.com/seonghobae/kaefa/blob/da6bf9df8acbdbd32f00c4c44b6f7e84b6efa8f5/R/newEngine.R#L75-L85 https://github.com/seonghobae/kaefa/blob/da6bf9df8acbdbd32f00c4c44b6f7e84b6efa8f5/R/kaefa.R#L383