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In this example, we will show how to use `lslx`

to conduct multi-group factor analysis.
The example uses data `HolzingerSwineford1939`

in the package `lavaan`

.
Hence, `lavaan`

must be installed.

In the following specification, `x1`

- `x9`

is assumed to be measurements of 3 latent factors: `visual`

, `textual`

, and `speed`

.

model_mgfa <- "visual :=> 1 * x1 + x2 + x3 textual :=> 1 * x4 + x5 + x6 speed :=> 1 * x7 + x8 + x9"

The operator `:=>`

means that the LHS latent factors is defined by the RHS observed variables.
In this model, `visual`

is mainly measured by `x1`

- `x3`

, `textual`

is mainly measured by `x4`

- `x6`

, and `speed`

is mainly measured by `x7`

- `x9`

.
Loadings of `x1`

, `x4`

, and `x7`

are fixed at 1 for scale setting.
The above specification is valid for both groups.
Details of model syntax can be found in the section of Model Syntax via `?lslx`

.

`lslx`

is written as an `R6`

class.
Everytime we conduct analysis with `lslx`

, an `lslx`

object must be initialized.
The following code initializes an `lslx`

object named `lslx_mgfa`

.

library(lslx) lslx_mgfa <- lslx$new(model = model_mgfa, data = lavaan::HolzingerSwineford1939, group_variable = "school", reference_group = "Pasteur")

Here, `lslx`

is the object generator for `lslx`

object and `new`

is the build-in method of `lslx`

to generate a new `lslx`

object.
The initialization of `lslx`

requires users to specify a model for model specification (argument `model`

) and a data set to be fitted (argument `sample_data`

).
The data set must contain all the observed variables specified in the given model.
Because in this example a multi-group analysis is considered, variable for group labeling (argument `group_variable`

) must be specified.
In lslx, two types of parameterization can be used in multi-group analysis.
The first type is the same with the traditional multi-group SEM, which treats model parameters in each group separately.
The second type sets one group as reference and treats model parameters in other groups as increments with respect to the reference.
Under the second type of parameterization, the group heterogeneity can be efficiently explored if we treat the increments as penalized parameters.
In this example, `Pasteur`

is set as reference.
Hence, the parameters in `Grant-White`

now reflect differences from the reference.

After an `lslx`

object is initialized, the heterogeneity of a multi-group model can be quickly respecified by `$free_heterogeneity()`

, `$fix_heterogeneity()`

, and `$penalize_heterogeneity()`

methods.
The following code sets `x2<-visual`

, `x3<-visual`

, `x5<-textual`

, `x6<-textual`

, `x8<-speed`

, `x9<-speed`

, and
`x2<-1`

, `x3<-1`

, `x5<-1`

, `x6<-1`

, `x8<-1`

, `x9<-1`

in `Grant-White`

as penalized parameters.
Note that parameters in `Grant-White`

now reflect differences since `Pasteur`

is set as reference.

lslx_mgfa$penalize_heterogeneity(block = c("y<-1", "y<-f"), group = "Grant-White")

Since the homogeneity of latent factor means may not be a reasonable assumption when examining measurement invariance, the following code relaxes this assumption

lslx_mgfa$free_block(block = "f<-1", group = "Grant-White")

To see more methods to modify a specified model, please check the section of Set-Related Method via `?lslx`

.

After an `lslx`

object is initialized, method `$fit_mcp()`

can be used to fit the specified model into the given data with MCP.

lslx_mgfa$fit_mcp()

All the fitting result will be stored in the `fitting`

field of `lslx_mgfa`

.

Unlike traditional SEM analysis, `lslx`

fits the model into data under all the penalty levels considered.
To summarize the fitting result, a selector to determine an optimal penalty level must be specified.
Available selectors can be found in the section of Penalty Level Selection via `?lslx`

.
The following code summarize the fitting result under the penalty level selected by Haughtonâ€™s Bayesian information criterion (HBIC).

lslx_mgfa$summarize(selector = "hbic")

In this example, we can see that all of the loadings are invariant across the two groups.
However, the intercepts of `x3`

and `x7`

seem to be not invariant.
The `$summarize()`

method also shows the result of significance tests for the coefficients.
In `lslx`

, the default standard errors are calculated based on sandwich formula whenever raw data is available.
It is generally valid even when the model is misspecified and the data is not normal.
However, it may not be valid after selecting an optimal penalty level.

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