semTools (version 0.5-7)

goricaSEM: Wrapper for goric.lavaan() from the restriktor package

Description

The goricaSEM() function is an interface to restriktor::goric.lavaan(), allowing users to perform generalized order-restricted information criterion approximation (GORICA) analysis specifically for structural equation models fitted using the lavaan package.

Usage

goricaSEM(object, ..., hypotheses = NULL, comparison = NULL,
  type = "gorica", standardized = FALSE, debug = FALSE)

Value

A list containing the results of the goric.lavaan function, including:

• The log-likelihood.

• Penalty term.

• GORIC(A) values and weights.

• Relative GORIC(A) weights.

Arguments

object

A lavaan::lavaan object.

...

Additional arguments passed to restriktor::goric.lavaan().

hypotheses

A named list of hypotheses to test. See Details for information on how to specify hypotheses.

comparison

A character string specifying the type of comparison. Options are "unconstrained", "complement", or "none". Default behavior depends on the number of hypotheses.

type

A character string indicating the type of analysis, either "gorica" (default) or "goricac".

standardized

logical indicating whether standardized estimates are used in the analysis. Defaults to FALSE.

debug

logical indicating whether to print debugging information. Defaults to FALSE.

Author

Leonard Vanbrabant and Rebecca Kuiper

Details

This function is designed as a wrapper for the restriktor::goric.lavaan() function. It calculates GORICA values and weights, which can be used to compare models or hypotheses under inequality constraints.

The hypotheses= argument allows users to specify constraints in text-based syntax or matrix notation. For text-based syntax, constraints are specified as a string (e.g., "a1 > a2"). For matrix notation, a named list with $constraints, $rhs, and $neq elements can be provided.

The comparison= argument determines whether the specified hypothesis is compared against its "complement", the "unconstrained" model, or neither ("none").

References

Kuiper, R. M., Hoijtink, H., & Silvapulle, M. J. (2011). An Akaike-type information criterion for model selection under inequality constraints. Biometrika, 98(2), 495--501. tools:::Rd_expr_doi("10.1093/biomet/asr002")

Vanbrabant, L., Van Loey, N., & Kuiper, R. M. (2020). Evaluating a theory-based hypothesis against its complement using an AIC-type information criterion with an application to facial burn injury. Psychological Methods, 25(2), 129--142. tools:::Rd_expr_doi("10.1037/met0000238")

See Also

restriktor::goric.lavaan()

Examples

## Example: Perform GORICA analysis on a lavaan model
library(lavaan)
library(restriktor)

## Define the SEM model
model <- '
  ind60 =~ x1 + x2 + x3
  dem60 =~ y1 + a1*y2 + b1*y3 + c1*y4
  dem65 =~ y5 + a2*y6 + b2*y7 + c2*y8
  dem60 ~ ind60
  dem65 ~ ind60 + dem60
  y1 ~~ y5
  y2 ~~ y4 + y6
  y3 ~~ y7
  y4 ~~ y8
  y6 ~~ y8
'

## Fit the model
data(PoliticalDemocracy)
fit <- sem(model, data = PoliticalDemocracy)

## Define hypotheses
myHypothesis <- 'a1 > a2, b1 > b2, c1 > c2'

## Perform GORICA analysis
result <- goricaSEM(fit, hypotheses = list(H1 = myHypothesis),
                    standardized = FALSE, comparison = "complement",
                    type = "gorica")

## Print result
print(result)