Derive independence claims for SEM (old)
sem.basis.set(modelList, corr.errors = NULL, add.vars = NULL)
| modelList | a |
|---|---|
| corr.errors | a vector of variables with correlated errors (separated by "~~") |
| add.vars | a vector of additional variables whose independence claims should be |
Returns a list of independence claims. Each entry in the list is a vector
where the first entry is the predictor whose independence from the response is being evaluated,
the second is the response, and remaining entries represent the variables on which the independence
claim are conditional
Variables with correlated errors have no direct relationship but rather are hypothesized to be driven
by the same underlying factor. This covariance should be reflected as correlated errors
(double-headed arrow).
Correlated errors are specified using the same syntax as the lavaan package:
var1 ~~ var2. Variables with correlated errors are ignored in the basis set under the
assumption that their correlations will be quantified later using the function sem.coefs.
The argument add.vars requires a vector of character strings corresponding to column names
in the dataset used to construct the models in modelList. This is useful if comparing
nested SEMs where one wishes to account for additional variables whose independence claims should
be evaluated, but which do not have any hypothesized paths in the current SEM. The default assumes
there is no additional independence claims that do not appear in the model list.