Logistic Regression in structural equation modeling SEM framework
Dear lavaan users,
as far as i understand regression models are a special case of the more general structural equation models. i wonder, whether it is possible to model logistic regression in lavaan? Wouldn't one just need to transform the left-hand-side of the formula with the logit? Probably it's much more complicated, isn't it?
It is. Lavaan can not handle logistic regression (yet). It can handle
probit regression (using the probit link instead of the logit link) for
binary outcomes. But parameters can only be estimated by using WLS(MV),
not ML (at least not in 0.5-9), but the results are typically almost
identical. The only downside is that you can not directly interpret the
(exponentiated) regression coefficients as odds (ratios).
This would be an example (if y is binary):
model <- br="" x1="" x2="" x3="" y="">fit <- data="myData," model="" nbsp="" ordered="y" sem="" span="">
Dear lavaan users,
as far as i understand regression models are a special case of the more general structural equation models. i wonder, whether it is possible to model logistic regression in lavaan? Wouldn't one just need to transform the left-hand-side of the formula with the logit? Probably it's much more complicated, isn't it?
It is. Lavaan can not handle logistic regression (yet). It can handle
probit regression (using the probit link instead of the logit link) for
binary outcomes. But parameters can only be estimated by using WLS(MV),
not ML (at least not in 0.5-9), but the results are typically almost
identical. The only downside is that you can not directly interpret the
(exponentiated) regression coefficients as odds (ratios).
This would be an example (if y is binary):
model <- br="" x1="" x2="" x3="" y="">fit <- data="myData," model="" nbsp="" ordered="y" sem="" span="">
No comments:
Post a Comment
Thank you