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)
a vector of variables with correlated errors (separated by "~~")
a vector of additional variables whose independence claims should be evaluated, but which do not appear in the model list
an optional variable that represents the levels of data aggregation for a multi-level dataset
a function defining how variables are aggregated in
whether p-values degrees of freedom should be adjusted. Default is
provide an optional basis set
enable optional text progress bar. Default is
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.