RMediation (version 1.2.2)

mbco: Model-based Constrained Optimization (MBCO) Chi-squared Test

Description

This function computes asymptotic MBCO chi-squared test for a smooth function of model parameters including a function of indirect effects.

Usage

mbco(
  h0 = NULL,
  h1 = NULL,
  R = 10L,
  type = "asymp",
  alpha = 0.05,
  checkHess = "No",
  checkSE = "No",
  optim = "SLSQP",
  precision = 1e-09
)

Value

A list that contains

chisq

asymptotic chi-squared test statistic value

df

chi-squared df

p

chi-squared p-value computed based on the method specified by the argument type

Arguments

h0

An OpenMx model estimated under a null hypothesis, which is a more constrained model

h1

An OpenMx model estimated under an alternative hypothesis, which is a less constrained model. This is usually a model hypothesized by a researcher.

R

The number of bootstrap draws.

type

If 'asymp' (default), the asymptotic MBCO chi-squares test comparing fit of h0 and h1. If 'parametric', the parametric bootstrap MBCO chi-squared test is computed. If 'semi', the semi-parametric MBCO chi-squared is computed.

alpha

Significance level with the default value of .05

checkHess

If 'No' (default), the Hessian matrix would not be calculated.

checkSE

if 'No' (default), the standard errors would not be calculated.

optim

Choose optimizer available in OpenMx. The default optimizer is "SLSQP". Other optimizer choices are available. See mxOption for more details.

precision

Functional precision. The default value is set to 1e-9. See mxOption for more details.

Author

Davood Tofighi

References

Tofighi, D., & Kelley, K. (2020). Indirect effects in sequential mediation models: Evaluating methods for hypothesis testing and confidence interval formation. Multivariate Behavioral Research, 55, 188–210. tools:::Rd_expr_doi("https://doi.org/10.1080/00273171.2019.1618545")

Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. tools:::Rd_expr_doi("https://doi.org/10.3389/fpsyg.2019.02989")

Examples

data(memory_exp)
memory_exp$x <- as.numeric(memory_exp$x)-1 # manually creating dummy codes
endVar <- c('x', 'repetition', 'imagery', 'recall')
manifests <- c('x', 'repetition', 'imagery', 'recall')
full_model <- mxModel(
 "memory_example",
 type = "RAM",
 manifestVars = manifests,
 mxPath(
   from = "x",
   to = endVar,
   arrows = 1,
   free = TRUE,
   values = .2,
   labels = c("a1", "a2", "cp")
 ),
 mxPath(
   from = 'repetition',
  to = 'recall',
  arrows = 1,
   free = TRUE,
   values = .2,
   labels = 'b1'
 ),
 mxPath(
   from = 'imagery',
 to = 'recall',
 arrows = 1,
 free = TRUE,
 values = .2,
 labels = "b2"
),
mxPath(
 from = manifests,
 arrows = 2,
 free = TRUE,
 values = .8
),
mxPath(
 from = "one",
 to = endVar,
 arrows = 1,
 free = TRUE,
 values = .1
),
mxAlgebra(a1 * b1, name = "ind1"),
mxAlgebra(a2 * b2, name = "ind2"),
mxCI("ind1", type = "both"),
mxCI("ind2", type = "both"),
mxData(observed = memory_exp, type = "raw")
)
## Reduced  Model for indirect effect: a1*b1
null_model1 <- mxModel(
model= full_model,
name = "Null Model 1",
mxConstraint(ind1 == 0, name = "ind1_eq0_constr")
)
full_model <- mxTryHard(full_model, checkHess=FALSE, silent = TRUE )
null_model1 <- mxTryHard(null_model1, checkHess=FALSE, silent = TRUE )
mbco(null_model1,full_model)