testMICOM(
.object               = NULL,
.approach_p_adjust    = "none",
.handle_inadmissibles = c("drop", "ignore", "replace"),
.R                    = 499,
.seed                 = NULL,
.verbose              = TRUE
)

## Arguments

.object An R object of class cSEMResults resulting from a call to csem(). Character string or a vector of character strings. Approach used to adjust the p-value for multiple testing. See the methods argument of stats::p.adjust() for a list of choices and their description. Defaults to "none". Character string. How should inadmissible results be treated? One of "drop", "ignore", or "replace". If "drop", all replications/resamples yielding an inadmissible result will be dropped (i.e. the number of results returned will potentially be less than .R). For "ignore" all results are returned even if all or some of the replications yielded inadmissible results (i.e. number of results returned is equal to .R). For "replace" resampling continues until there are exactly .R admissible solutions. Depending on the frequency of inadmissible solutions this may significantly increase computing time. Defaults to "drop". Integer. The number of bootstrap replications. Defaults to 499. Integer or NULL. The random seed to use. Defaults to NULL in which case an arbitrary seed is chosen. Note that the scope of the seed is limited to the body of the function it is used in. Hence, the global seed will not be altered! Logical. Should information (e.g., progress bar) be printed to the console? Defaults to TRUE.

## Value

A named list of class cSEMTestMICOM containing the following list element:

$Step2 A list containing the results of the test for compositional invariance (Step 2). $Step3

A list containing the results of the test for mean and variance equality (Step 3).

## References

Henseler J, Ringle CM, Sarstedt M (2016). “Testing Measurement Invariance of Composites Using Partial Least Squares.” International Marketing Review, 33(3), 405--431. doi: 10.1108/imr-09-2014-0304 , https://doi.org/10.1108/imr-09-2014-0304.

## See also

csem(), cSEMResults, testOMF(), testMGD()

## Examples

if (FALSE) {
# NOTE: to run the example. Download and load the newst version of cSEM.DGP
# from GitHub using devtools::install_github("M-E-Rademaker/cSEM.DGP").

# Create two data generating processes (DGPs) that only differ in how the composite
# X is build. Hence, the two groups are not compositionally invariant.
dgp1 <- "
# Structural model
Y ~ 0.6*X

# Measurement model
Y =~ 1*y1
X <~ 0.4*x1 + 0.8*x2

x1 ~~ 0.3125*x2
"

dgp2 <- "
# Structural model
Y ~ 0.6*X

# Measurement model
Y =~ 1*y1
X <~ 0.8*x1 + 0.4*x2

x1 ~~ 0.3125*x2
"

g1 <- generateData(dgp1, .N = 399, .empirical = TRUE) # requires cSEM.DGP
g2 <- generateData(dgp2, .N = 200, .empirical = TRUE) # requires cSEM.DGP

# Model is the same for both DGPs
model <- "
# Structural model
Y ~ X

# Measurement model
Y =~ y1
X <~ x1 + x2
"

# Estimate
csem_results <- csem(.data = list("group1" = g1, "group2" = g2), model)

# Test
testMICOM(csem_results, .R = 50, .alpha = c(0.01, 0.05), .seed = 1987)
}