AIC_psem: Information criterion values for SEM

In piecewiseSEM: Piecewise Structural Equation Modeling

Information criterion values for SEM

Description

Information criterion values for SEM

Usage

AIC_psem(
  modelList,
  AIC.type = "loglik",
  Cstat = NULL,
  add.claims = NULL,
  basis.set = NULL,
  direction = NULL,
  interactions = FALSE,
  conserve = FALSE,
  conditional = FALSE,
  .progressBar = FALSE
)

Arguments

modelList	a list of structural equations
AIC.type	whether the log-likelihood "loglik" or d-sep "dsep" AIC score should be reported. Default is "loglik"
Cstat	Fisher's C statistic obtained from fisherC
add.claims	an optional vector of additional independence claims (P-values) to be added to the basis set
basis.set	An optional list of independence claims.
direction	a vector of claims defining the specific directionality of any independence claim(s)
interactions	whether interactions should be included in independence claims. Default is FALSE
conserve	whether the most conservative P-value should be returned (See Details) Default is FALSE
conditional	whether the conditioning variables should be shown in the table. Default is FALSE
.progressBar	an optional progress bar. Default is FALSE

Value

a data.frame of AIC, AICc, d.f., and sample size

Author(s)

Jon Lefcheck <[e-mail]>, Jim Grace

References

Shipley, Bill, and Jacob C. Douma. "Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs." Ecology 101.3 (2020): e02960.

Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d‐separation test." Ecology 94.3 (2013): 560-564.

piecewiseSEM documentation built on March 7, 2023, 7:45 p.m.