Test Group Constraints

Test the model fit change when one or more between-group constraints are imposed.

Usage

test_group_equal(
  fit = fit,
  group.equal = NULL,
  group.partial = NULL,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

fit: The fit object. Must be the output of lavaan::lavaan() or its wrappers, such as lavaan::sem() and lavaan::cfa(). The model must be a multigroup model.
group.equal: The same argument used by lavaan. A character vector with one or more of these values: "regressions", "loadings", "lv.covariances", "lv.variances", "intercepts", "means", "thresholds", "residual.covariances", "composite.weights", and "residuals".
group.partial: The same argument used by lavaan. The parameters that should be free across groups. Used with group.equal to exclude some parameters from those requested to be equal across groups by group.equal.
check_post_check: Logical. If TRUE, the default, and the model is fitted by lavaan, the test will be conducted only if the model passes the post.check conducted by lavaan::lavInspect() (with what = "post.check").
...: Optional arguments to be passed to lavaan::lavTestLRT().
fit_name: The name of the model fit object to be extracted. Default is "fit". Used only when more than one model is fitted in each replication. This should be the name of the model on which the test is to be conducted.
get_map_names: Logical. Used by power4test() to determine how to extract stored information and assign them to this function. Users should not use this argument.
get_test_name: Logical. Used by power4test() to get the default name of this test. Users should not use this argument.

Value

In its normal usage, it returns a one-row data frame with the following columns:

est: The chi-square difference.
cilo and cihi: NA. Not used.
sig: Whether the chi-square difference test is significant
test_label: The constraints imposted.

Details

This function is to be used in power4test() for testing the difference in model fit when one or more between-group constraints are imposed , by setting it to the test_fun argument.

Examples


# Specify the model

mod <-
"
m ~ x
y ~ m + x
"

# Specify the population values

mod_es <-
"
y ~ m: l
m ~ x:
  - nil
  - s
y ~ x: nil
"

# Simulate the data

sim_only <- power4test(nrep = 2,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       iseed = 1234)
#> Simulate the data:
#> Fit the model(s):

# Do the tests in each replication

test_out <- power4test(object = sim_only,
                       test_fun = test_group_equal,
                       test_args = list(group.equal = "regressions"))
#> Do the test: test_group_equal 

print(test_out,
      test_long = TRUE)
#> 
#> ====================== Model Information ======================
#> 
#> == Model on Factors/Variables ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> == Model on Variables/Indicators ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> ====== Population Values ======
#> 
#> 
#> Group 1 [Group1]:
#> 
#> Regressions:
#>                    Population
#>   m ~                        
#>     x                 0.000  
#>   y ~                        
#>     m                 0.500  
#>     x                 0.000  
#> 
#> Variances:
#>                    Population
#>    .m                 1.000  
#>    .y                 0.750  
#>     x                 1.000  
#> 
#> 
#> Group 2 [Group2]:
#> 
#> Regressions:
#>                    Population
#>   m ~                        
#>     x                 0.100  
#>   y ~                        
#>     m                 0.500  
#>     x                 0.000  
#> 
#> Variances:
#>                    Population
#>    .m                 0.990  
#>    .y                 0.750  
#>     x                 1.000  
#> 
#> == Population Conditional/Indirect Effect(s) ==
#> 
#> == Indirect Effect(s) ==
#> 
#>                      ind
#> Group1.x -> m -> y 0.000
#> Group2.x -> m -> y 0.050
#> 
#>  - The 'ind' column shows the indirect effect(s).
#>  
#> ======================= Data Information =======================
#> 
#> Number of Replications:  2 
#> Sample Sizes:  200 
#> 
#> Call print with 'data_long = TRUE' for further information.
#> 
#> ==================== Extra Element(s) Found ====================
#> 
#> - fit
#> 
#> === Element(s) of the First Dataset ===
#> 
#> ============ <fit> ============
#> 
#> lavaan 0.6-21 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                        14
#> 
#>   Number of observations per group:                   
#>     Group1                                         100
#>     Group2                                         100
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0
#>   Test statistic for each group:
#>     Group1                                       0.000
#>     Group2                                       0.000
#> 
#> ================== <test_group_equal> ==================
#> 
#> Mean(s) across replication:
#>           test_label   est cilo cihi   sig pvalue
#> 1 equal(regressions) 3.469   NA   NA 0.000  0.343
#> 
#> - The column 'sig' shows the rejection rates.
#> - If the null hypothesis is false, the rate is the power.
#> - Number of valid replications for rejection rate(s): 2 
#> - Proportion of valid replications for rejection rate(s): 1.000

Usage

Arguments

Value

Details

See also

Examples