Information criterion values for SEM

Information criterion values for SEM

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

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.

Author

Jon Lefcheck <LefcheckJ@si.edu>, Jim Grace