Goodness-of-fit tests for piecewise SEM (old)
sem.fit(modelList, data, conditional = FALSE, corr.errors = NULL, add.vars = NULL, grouping.vars = NULL, grouping.fun = mean, adjust.p = FALSE, basis.set = NULL, pvalues.df = NULL, model.control = NULL, .progressBar = TRUE)
modelList | a |
---|---|
data | a |
conditional | whether the full set of conditioning variables should be returned.
Default is |
corr.errors | a vector of variables with correlated errors (separated by "~~") |
add.vars | a vector of additional variables whose independence claims should be evaluated, but which do not appear in the model list |
grouping.vars | an optional variable that represents the levels of data aggregation for a multi-level dataset |
grouping.fun | a function defining how variables are aggregated in |
adjust.p | whether p-values degrees of freedom should be adjusted. Default is |
basis.set | provide an optional basis set |
pvalues.df | an optional |
model.control | a |
.progressBar | enable optional text progress bar. Default is |
a list
corresponding to: the tests of directed separation, the Fisher's C statistic,
and the AIC of the model
Tests independence claims and calculates Fisher's C statistic and associated p-value, and AIC and AICc, for a piecewise structural equation model (SEM).