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

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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.