Comments (3)
Thank you so much, that is perfect.
May be it is useful to point toward the documentation of stdEff() in the doc in semEff(). Because, right now, we need to go from semEff() to bootEff() and from bootEff() to stdEff().
A proposition:
#' Direct effects are calculated as fully standardised model coefficients for
#' each response variable (see [stdEff()] for details), while indirect effects are the product of these
#' direct effects operating along causal pathways in the SEM. The total
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Thanks Alain, that's a good suggestion, I'll prob update that documentation in the next patch release, due soon.
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Hi Alain,
Standardisation is all explained in the documentation for stdEff(), with relevant references therein. But basically, the coefficients themselves are standardised rather than the variables (except in some limited cases where mean-centring of variables is also done, e.g. interactions).
Cheers,
Mark
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Related Issues (18)
- Considerations for GLMM? HOT 1
- Get the CI as a data.frame after bootstrap HOT 1
- In (function (m, w) : 101 model fit(s) or parameter estimation(s) failed. NAs reported/generated. HOT 12
- semEff documentation not clear for using psem objects HOT 1
- Can't list correlated erros with cor.err HOT 1
- Transfer `digits` argument from `semEff()` to `print.semEff()`/`summary.semEff()`?
- lapply bootEff HOT 1
- "WARNING:All vanes of t1* are NA" - only on one computer when running the same code, also 10x slower HOT 1
- Error message "estimated adjustment 'a' is NA" HOT 1
- semEff() provides much lower estimates than piecewiseSEM::coefs() for psem objects with lme4 (poisson errors) HOT 1
- how to cite semEff? HOT 1
- getData does not work HOT 1
- How to use semEff with glmmTMB? HOT 1
- summary(semEff(sem.Eff.boot)) output to matrix/dataframe HOT 2
- confidence intervals for getEff() functions HOT 3
- SemEff using gls models HOT 4
- BootEff in psem with interaction terms HOT 1
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