Open mattansb opened 2 years ago
From the fungible package which implements those methods
https://github.com/cran/fungible/blob/master/R/seBeta.R
https://github.com/cran/fungible/blob/master/R/seBetaCor.R
https://github.com/cran/fungible/blob/master/R/seBetaFixed.R
And somewhat cleaner implementation of the normal-theory methods
https://github.com/psychmeta/psychmeta/blob/b9e27791563f4b98b5e1aaeb5bc97d97bb28d055/R/lm_mat.R#L483
This only applies when standardize
is not "refit"
, right?
Where do we get all the information, like cov.x
or cov.xy
, needed for the function arguments?
This only applies when
standardize
is not"refit"
, right?
Strictly, no, pre-processing the variables still introduces the uncertainty in SD.
but I'm not sure how feasible it would be to wrangle to necessary information in the process to get accurate CIs there.
Where do we get all the information, like
cov.x
orcov.xy
, needed for the function arguments?
You can extract the model matrix from the model and compute the necessary covariances using it
Originally posted on https://github.com/easystats/parameters/issues/655
@bwiernik:
@strengejacke