AIC_psem: 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
)

Value

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

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

Author

Jon Lefcheck <LefcheckJ@si.edu>, 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.