# Semi-Confirmatory Factor Analysis In lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood or Least Squares

```options(digits = 3)
options(width = 100)
```

In this example, we will show how to use `lslx` to conduct semi-confirmatory factor analysis. The example uses data `HolzingerSwineford1939` in the package `lavaan`. Hence, `lavaan` must be installed.

## Model Specification

In the following specification, `x1` - `x9` is assumed to be measurements of 3 latent factors: `visual`, `textual`, and `speed`.

```model_fa <- "visual  :=> x1 + x2 + x3
textual :=> x4 + x5 + x6
speed   :=> x7 + x8 + x9
visual  :~> x4 + x5 + x6 + x7 + x8 + x9
textual :~> x1 + x2 + x3 + x7 + x8 + x9
speed   :~> x1 + x2 + x3 + x4 + x5 + x6
visual  <=> fix(1)* visual
textual <=> fix(1)* textual
speed   <=> fix(1)* speed"
```

The operator `:=>` means that the LHS latent factors is defined by the RHS observed variables. In particular, the loadings are freely estimated. The operator `:~>` also means that the LHS latent factors is defined by the RHS observed variables, but these loadings are set as penalized coefficients. 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`. However, the inclusion of the penalized loadings indicates that each measurement may not be only influenced by one latent factor. The operator `<=>` means that the LHS and RHS variables/factors are covaried. If the LHS and RHS variable/factor are the same, `<=>` specifies the variance of that variable/factor. For scale setting, `visual <=> fix(1) * visual` makes the variance of `visual` to be zero. Details of model syntax can be found in the section of Model Syntax via `?lslx`.

## Object Initialization

`lslx` is written as an `R6` class. Every time we conduct analysis with `lslx`, an `lslx` object must be initialized. The following code initializes an `lslx` object named `lslx_fa`.

```library(lslx)
lslx_fa <- lslx\$new(model = model_fa, data = lavaan::HolzingerSwineford1939)
```

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 to be fitted (argument `sample_data`). The data set must contain all the observed variables specified in the given model. In is also possible to initialize an `lslx` object via sample moments (see `vignette("structural-equation-modeling")`).

To see the model specification, we may use the `\$extract_specification()` method.

```lslx_fa\$extract_specification()
```

The row names show the coefficient names. The most two relevant columns are `type` which shows the type of the coefficient and `start` which gives the starting value. In `lslx`, many `extract` related methods are defined. `extract` related methods can be used to extract quantities stored in `lslx` object. For details, please see the section of Extract-Related Methods via `?lslx`.

## Model Fitting

After an `lslx` object is initialized, method `\$fit()` can be used to fit the specified model to the given data.

```lslx_fa\$fit(penalty_method = "mcp",
lambda_grid = seq(.01, .60, .01),
delta_grid = c(1.5, 3.0, Inf))
```

The fitting process requires users to specify the penalty method (argument `penalty_method`) and the considered penalty levels (argument `lambda_grid` and `delta_grid`). In this example, the `mcp` penalty is implemented on the lambda grid `seq(.01, .60, .01)` and delta grid `c(1.5, 3, Inf)`. Note that in this example `lambda = 0` is not considered because it may result in unidentified model. All the fitting result will be stored in the `fitting` field of `lslx_fa`.

## Model Summarizing

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 Bayesian information criterion (BIC).

```lslx_fa\$summarize(selector = "bic")
```

In this example, we can see that most penalized coefficients are estimated as zero under the selected penalty level except for `x9<-visual`, which shows the benefit of using the semi-confirmatory approach. 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.

## Visualization

`lslx` provides four methods for visualizing the fitting results. The method `\$plot_numerical_condition()` shows the numerical condition under all the penalty levels. The following code plots the values of `n_iter_out` (number of iterations in outer loop), `objective_gradient_abs_max` (maximum of absolute value of gradient of objective function), and `objective_hessian_convexity` (minimum of univariate approximate hessian). The plot can be used to evaluate the quality of numerical optimization.

```lslx_fa\$plot_numerical_condition()
```

The method `\$plot_information_criterion()` shows the values of information criteria under all the penalty levels.

```lslx_fa\$plot_information_criterion()
```

The method `\$plot_fit_index()` shows the values of fit indices under all the penalty levels.

```lslx_fa\$plot_fit_index()
```

The method `\$plot_coefficient()` shows the solution path of coefficients in the given block. The following code plots the solution paths of all coefficients in the block `y<-f`, which contains all the regression coefficients from latent factors to observed variables (i.e., factor loadings).

```lslx_fa\$plot_coefficient(block = "y<-f")
```

## Objects Extraction

In `lslx`, many quantities related to SEM can be extracted by extract-related method. For example, the loading matrix can be obtained by

```lslx_fa\$extract_coefficient_matrix(selector = "bic", block = "y<-f")
```

The model-implied covariance matrix and residual matrix can be obtained by

```lslx_fa\$extract_implied_cov(selector = "bic")
```
```lslx_fa\$extract_residual_cov(selector = "bic")
```

## Wrapper Function `plsem()` and `S3` Methods

After version 0.6.3, the previous analysis can be equivalently conducted by `plsem()` with `lavaan` style model syntax.

```model_fa <- "visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9
pen() * visual  =~ x4 + x5 + x6 + x7 + x8 + x9
pen() * textual =~ x1 + x2 + x3 + x7 + x8 + x9
pen() * speed   =~ x1 + x2 + x3 + x4 + x5 + x6
visual  ~~ 1 * visual
textual ~~ 1 * textual
speed   ~~ 1 * speed"

lslx_fa <- plsem(model = model_fa,
data = lavaan::HolzingerSwineford1939,
penalty_method = "mcp",
lambda_grid = seq(.01, .60, .01),
delta_grid = c(1.5, 3.0, Inf))

summary(lslx_fa, selector = "bic")
```

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lslx documentation built on April 28, 2020, 1:09 a.m.