AIC for piecewiseSEM (old)
sem.aic(modelList, data, 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 |
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 |
Returns a data.frame
where the first entry is the AIC score, and the second is
the AICc score, and the third is the likelihood degrees of freedom (K)
This function calculates AIC and AICc (corrected for small sample sizes) values for a piecewise structural equation model (SEM).
For linear mixed effects models, p-values can be adjusted to accommodate the full model degrees of freedom
using the argument p.adjust = TRUE
. For more information, see Shipley 2013.