piecewiseSEM (version 2.0.2)

sem.aic: sem.aic

Description

AIC for piecewiseSEM (old)

Usage

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)

Arguments

modelList

a list of regressions representing the structural equation model

data

a data.frame used to construct the structured equations

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 grouping.vars. Default is mean

adjust.p

whether p-values degrees of freedom should be adjusted. Default is FALSE

basis.set

provide an optional basis set

pvalues.df

an optional data.frame corresponding to p-values for independence claims

model.control

a list of model control arguments to be passed to d-sep models

.progressBar

enable optional text progress bar. Default is TRUE

Value

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)

Details

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.