Shohei Shimizu

result.get_probabilities() gives the bootstrap probabilities of whether direct effects are non-zero (directed edges exist). result.get_total_causal_effects() give the bootstrap probabilities of whether total effects are non-zero (directed paths exist).

Hi, those total effects in the bootstrap outputs are the medians over the bootstrap samples. You can find all the bootstrap results here: https://lingam.readthedocs.io/en/latest/reference/bootstrap.html

if your discrete variables are collected using 5 point likert scale, it would be ok to use DirectLiNGAM thinking they are approximately continuous. DirectLiNGAM assume variables are continuous. Error variables...

Thanks. I thought your suggestion is to aim to manage non-stationarity of time series. Am I correct?

"global_only" is computationally faster, but could be somewhat less accurate.

LiM assumes binary variables. If your variables are 5 stars, you could assume such 5 stars are approximated well by continuous variables. Then, you could analyze your variables by methods...

I meant just assuming your 5 stars variables, that would take 1, 2, 3, 4, 5, are continuous.

If they are no ordinal, but categorical, no methods are available now in the package.

Hi, multiscale bootstrap is not implemented in this package for LiNGAM. Though not specialized to LiNGAM, in general cases, R package for multi scale bootstrap is available: https://github.com/shimo-lab/scaleboot

Yes. Basically, it would be something like computing bootstrap probabilities with different numbers of bootstrap resampling and then giving them to the R code.