lslx: R6 class for semi-confirmatory structural equation modeling...

Description Value Usage Overview Model Syntax Optimization Algorithm Missing Data Penalty Level Selection Model Fit Evaluation Coefficient Evaluation Initialize Method Set-Related Methods Fit-Related Methods Summarize Method Test-Related Methods Plot-Related Methods Get-Related Methods Extract-Related Methods References Examples

Description

R6 class for semi-confirmatory structural equation modeling via penalized likelihood

Value

Object of lslx R6 class for fitting semi-confirmatory structural equation modeling (SEM) with penalized likelihood (PL).

Usage

lslx is an R6ClassGenerator for constructing an lslx object that has methods for fitting semi-confirmatory SEM. In a simpliest case, the use of lslx involves three major steps

  1. Initialize a new lslx object by specifying a model and importing a data set.

    r6_lslx <- lslx$new(model, data)

  2. Fit the specified model to the imported data with given fitting control.

    r6_lslx$fit(penalty_method, lambda_grid, delta_grid)

  3. Summarize the fitting results with specified selector.

    r6_lslx$summarize(selector)

To cite lslx in publications use:

Po-Hsien Huang (in press). lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood. Journal of Statistical Software.

Overview

lslx is a package for fitting semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) developed by Huang, Chen, and Weng (2017). In this semi-confirmatory method, an SEM model is distinguished into two parts: a confirmatory part and an exploratory part. The confirmatory part includes all of the freely estimated parameters and fixed parameters that are allowed for theory testing. The exploratory part is composed by a set of penalized parameters describing relationships that cannot be clearly determined by available substantive theory. By implementing a sparsity-inducing penalty and choosing an optimal penalty level, the relationships in the exploratory part can be efficiently identified by the sparsity pattern of these penalized parameters. After Version 0.6.7, lslx also supports penalized least squares for SEM with ordianl data under delta parameterization. The technical details of lslx can be found in its JSS paper (Huang, 2020) <doi:10.18637/jss.v093.i07> or Vignette for Package lslx (https://cran.r-project.org/web/packages/lslx/vignettes/vignette-lslx.pdf).

The main function lslx generates an object of lslx R6 class. R6 class is established via package R6 (Chang, 2017) that facilitates encapsulation object-oriented programming in R system. Hence, the lslx object is self-contained. On the one hand, lslx object stores model, data, and fitting results. On the other hand, it has many built-in methods to respecify model, fit the model to data, and test goodness of fit and coefficients. The initialization of a new lslx object requires importing a model and a data set to be analyzed. After an lslx object is initialized, build-in methods can be used to modify the object, find the estimates, and summarize fitting result. Details of object initialization is described in the section of Initialize Method.

In the current semi-confirmatory approach, the model specification is quite similar to the traditional practice of SEM except that some parameters can be set as penalized. Model specification in lslx mainly relies on the argument model when creating a new lslx object. After a lslx object is initialized, the initialized model can be still modified by set-related methods. These set-related methods may hugely change the initialized model by just one simple command. This two-step approach allows users specifying their own models flexibly and efficiently. Details of the model specification can be found in the sections of Model Syntax and Set-Related Methods.

Given a penalty level, lslx finds a PL estimate by minimizing a penalized maximum likelihood (ML) loss or a least squares loss functions (including OLS, DWLS, and WLS). The penalty function can be set as lasso (Tibshirani, 1996), ridge (Hoerl & Kennard, 1970), elastic net (Zou & Hastie, 2005), or mcp (minimax concave penalty; Zhang, 2010). lslx solves the optimization problem based on an improved glmnet method (Friedman, Hastie, & Tibshirani, 2010) made by Yuan, Ho, and Lin (2012). The underlying optimizer is written by using Rcpp (Eddelbuettel & Francois, 2011) and RcppEigen (Bates & Eddelbuettel, 2013). Our experiences show that the algorithm can efficiently find a local minimum provided that (1) the starting value is reasonable, and (2) the saturated covariance matrix is not nearly singular. Details of optimization algorithm and how to implement the algorithm can be found in the sections of Optimization Algorithm and Fit-Related Methods.

When conducting SEM, missing data are easily encountered. lslx can handle missing data problem by listwise deletion and two-step methods. Details of the methods for missing data can be found in the section of Missing Data.

After fitting the specified model to data under all of the considered penalty levels, an optimal penalty level should be chosen. A naive method for penalty level selection is using information criteria. Huang, Chen, and Weng (2017) have shown the asymptotic properties of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selecting the penalty level. In lslx, information criteria other an AIC and BIC can be also used. However, the empirical performances of these included criteria should be further studied. Details of choosing an optimal penalty level can be found in the section of Penalty Level Selection.

Given a penalty level, it is important to evaluate the goodness-of-fit of selected model and coefficients. In lslx, it is possible to make statistical inferences for goodness-of-fit and coefficients. However, the inference methods assume that no model selection is conducted, which is not true in the case of using PL. After version 0.6.4, several post-selection mehtods are available (Huang, 2019b). Details of statistical inference can be found in the sections of Model Fit Evaluation and Coefficient Evaluation. Implementations of these methods can be found in the sections of Summarize Method and Test-Related Methods.

Besides making statistical inference, lslx has methods for plotting the fitting results, include visualizing quality of optimization and the values of information criteria, fit indices, and coefficient estimates. Details of methods for plotting can be found in the section of Plot-Related Methods.

An object of lslx R6 class is composed by three R6 class objects: lslxModel, lslxData, and lslxFitting. lslxModel contains the specified model and lslxData object stores the imported data to be analyzed. When fitting the model to data, a reduced model and data will be sent to lslxFitting. After the underlying optimizer finishes its job, the fitting results will be also stored in lslxFitting. Since the three members are set as private, they can be only assessed by defined member functions. Other than the three members, quantities that are crucial for SEM can be also extracted, such as model-implied moments, information matrix, and etc.. Details of methods for obtaining private members and SEM-related quantities can be found in the sections of Get-Related Methods and Extract-Related Methods.

Model Syntax

With lslx the relationships among observed variables and latent factors are mainly specified via equation-like syntax. The creation of syntax in lslx is highly motivated by lavaan (Rosseel, 2012), a successful package for fitting SEM. However, lslx utilizes slightly more complex, but still intuitive operators to describe relations among variables.

Example 1: Multiple Regression Model

Consider the first example of model that specifies a multiple regression model

y <= x1 + x2

In this example, an dependent variable y is predicted by x1 and x2. These three variables are all observed variables and should appear in the given data set. The operator <= means that the regression coefficients from the right-hand side (RHS) variables to the left-hand side (LHS) variables should be freely estimated.

Although it is a very simple example, at least four important things behind this example should be addressed:

  1. For any endogenous variable (i.e., an variable that is influenced by any other variable), its residual term is not required to be specified. In this example, lslx recognizes y is an endogenous variable and hence the variance of corresponding residual will be set as freely estimated parameter. It is also possible to explicitly specify the variance of residual of y by y <=> y. Here, the operator <=> indicates the covariance of the RHS and LHS variables should be freely estimated.

  2. If all of the specified equations do not contain the intercept variable 1, then the intercept of each endogenous and observed variable will be freely estimated. Since the intercept variable 1 doesn't appear in this example, the intercept for y will be set as a freely estimated parameter. We can explicitly set the intercept term by y <= 1. However, under the situation that many equations are specified, once the intercept variable 1 appears in some equation, intercept terms in other equations should be explicitly specified. Otherwise, the intercepts of endogenous and observed variables in other equations will be fixed at zero.

  3. For any set of exogeneous variables (i.e., variables that are not influenced by any other variable in the specified system), not only their variances will be freely estimated, but also their pairwise covariances will be set as freely estimated parameters. In this example, x1 and x2 are both exogeneous variables. Hence, their variance and pairwise covariances will be automatically set as freely estimated parameters. These covariances can be explicitly stated by simply x1 + x2 <=> x1 + x2. The syntax parser in lslx will consider variance/covariance of each combination of LHS and RHS variables.

  4. The intercepts (or means) of exogeneous and observed variables are always set as freely estimated parameters. In this example, the intercepts of x1 and x2 will be freely estimated. It can be stated explicitly by x1 + x2 <= 1. Also, the lslx parser will know that the intercept variable 1 has effect on all of x1 and x2.

The previous regression example can be equivalently represented by x1 + x2 => y. In lslx all of the directed operators can be reversed under the stage of model specification. Users can choose the directions of operators according to their own preference.

The unique feature of lslx is that all of the parameters can be set as penalized. To penalize all of the regression coefficients, the equation can be modified as

y <~ x1 + x2

Here, the operator <~ means that the regression coefficients from the RHS variables to the LHS variables should be estimated with penalization. If only the coefficient of x1 should be penalized, we can use prefix to partly modify the equation

y <= pen() * x1 + x2

or equivalently

y <~ x1 + free() * x2

Both pen() and free() are prefix to modify the lslx operators. pen() makes the corresponding parameter to be penalized and free() makes it to be freely estimated. Inside the parentheses, starting values can be specified. Any prefix must present before some variable name and divided by asterisk *. Note that prefix can appear in the either RHS or LHS of operators and its function can be 'distributed' to the variables in the other side. For example, free() * y <~ x1 + x2 will be interpreted as that all of the coefficients should be freely estimated. However, any prefix cannot simultaneously appear on both sides of operators, which may result in an ambiguity specification.

Example 2: Factor Analysis Model

Now, we consider another example of equation specification.

y1 + y2 + y3 <=: f1

y4 + y5 + y6 <=: f2

y7 + y8 + y9 <=: f3

This example is a factor analysis model with nine observed variables and three latent factors. In lslx, defining a latent factor can be through the operator <=: which means that the RHS factor is defined by LHS observed variables. The observed variables must be presented in the given data set. Of course, f1 can be equivalently defined by f1 :=> y1 + y2 + y3.

As addressed in the first example, the lslx parser will automatically set many parameters that are not directly presented in these equations.

  1. In this example, all of the observed variables are directed by some latent factor and hence they are endogenous. The variances of their residuals will be set as freely estimated parameters. Also, their intercepts will be freely estimated since no intercept variable 1 presents in the specified equations.

  2. The three latent factors f1, f2, and f3 are exogenous variables. Their pairwise covariances will be also set as freely estimated parameters. However, because they are latent but not observed, their intercepts will be fixed at zero. If user hope to estimate the latent factor means, they should add an additional equation f1 + f2 + f3 <= 1. After adding this equation, on the one hand, the latent intercepts will be set as free as indicated by that equation. On the other hand, since intercept variable 1 now presents in the specified equations, the intercepts for the endogenous and observed variables will be then fixed at zero.

So far, the specification for the factor analysis model is not complete since the scales of factors are not yet determined. In SEM, there are two common ways for scale setting. The first way is to fix some loading per factor. For example, we may respecify the model via

fix(1) * y1 + y2 + y3 <=: f1

fix(1) * y4 + y5 + y6 <=: f2

fix(1) * y7 + y8 + y9 <=: f3

The prefix fix(1) will fix the corresponding loadings to be one. Simply using 1 * y1 will be also interpreted as fixing the loading of y1 at one to mimic lavaan. The second way for scale setting is fixing the variance of latent factors, which can be achieved by specifying additional equations

fix(1) * f1 <=> f1

fix(1) * f2 <=> f2

fix(1) * f3 <=> f3

Note that in the current version of lslx, scale setting will be not made automatically. Users must accomplish it manually.

When conducting factor analysis, we may face the problem that each variable may not be influenced by only one latent factor. The semi-confirmatory factor analysis, which penalizes some part of loading matrix, can be applied in this situation. One possible model specification for the semi-confirmatory approach is

y1 + y2 + y3 <=: f1

y4 + y5 + y6 <=: f2

y7 + y8 + y9 <=: f3

y4 + y5 + y6 + y7 + y8 + y9 <~: f1

y1 + y2 + y3 + y7 + y8 + y9 <~: f2

y4 + y5 + y6 + y7 + y8 + y9 <~: f3

fix(1) * f1 <=> f1

fix(1) * f2 <=> f2

fix(1) * f3 <=> f3

In this specification, loadings in the non-independent cluster will be also estimated but with penalization.

After version 0.6.3, lslx supports basic lavaan operators. The previous model can be equivalently specified as

f1 =~ y1 + y2 + y3

f2 =~ y4 + y5 + y6

f3 =~ y7 + y8 + y9

pen() * f1 =~ y4 + y5 + y6 + y7 + y8 + y9

pen() * f2 =~ y1 + y2 + y3 + y7 + y8 + y9

pen() * f3 =~ y4 + y5 + y6 + y7 + y8 + y9

f1 ~~ 1 * f1

f2 ~~ 1 * f2

f3 ~~ 1 * f3

Example 3: Path Models with both Observed Variables and Latent Factors

In the third example, we consider a path model with both observed variables and latent factors

fix(1) * y1 + y2 + y3 <=: f1

fix(1) * y4 + y5 + y6 <=: f2

fix(1) * y7 + y8 + y9 <=: f3

f3 <= f1 + f2

f1 + f2 + f3 <~ x1 + x2

f1 <~> f2

The first three equations specify the measurement model for y1 - y9 and f1 - f3. The forth equation describes the relations among latent factor. The fifth equation sets all the coefficients from x1 - x2 to f1 - f3 to be penalized. The final equation states that the covariance of residuals of f1 and f2 is estimated with penalization, which is achieved by the operator <~>.

Like Example 1 and 2, many parameters in the current example are automatically set by lslx.

  1. Because y1 - y9 and f1 - f3 are all endogenous, the variances of their residuals will be treated as freely estimated parameters. Also, due to the non-presence of intercept variable 1, the intercept of y1 - y9 will be set as free parameters and the intercept of f1 - f3 will be set as zero.

  2. The variance, intercepts, and pairwise covariances of exogenous and observed variables x1 - x2 will be all estimated freely.

In this example, we can see that model specification in lslx is quite flexible. Like usual SEM, users can specify their models according some substantive theory. If no theory is available to guide the relationships in some part of the model, the semi-confirmatory approach can set this part as exploratory by setting the corresponding parameters as penalized.

Example 4: Multi-Group Factor Analysis Model

In the fourth example, we consider a multi-group factor analysis model.

fix(1) * y1 + y2 + y3 <=: f1

fix(1) * y4 + y5 + y6 <=: f2

fix(1) * y7 + y8 + y9 <=: f3

The syntax specifies a factor analysis model with nine observed variables and three latent factors. Loadings for y2, y3, y5, y6, y8, and y9 are freely estimated in both groups. Loadings for y1, y4, and y7 are set as fixed for scale setting in both groups. You may observe that the syntax for multi-group analysis is the same as that for single group analysis. That is true because in lslx a multi-group analysis is mainly identified by specifying a group variable. If the imported data can be divided into several samples based on some group variable (argument group_variable in new method, please see the section of Initialize Method) for group labeling, lslx will automatically conduct multi-group analysis (see example of Semi-Confirmatory Multi-Group Factor Analysis in the Section of Examples).

Sometimes, we may hope to specify different model structures for the two groups. It can be achieved by using vector version of prefix, which is also motivated by the syntax in lavaan. For example, if we hope to restrict the loading for y2 to be 1 in the first group but set it as freely estimate parameter in the second group. Then we may use

fix(1) * y1 + c(fix(1), free()) * y2 + y3 <=: f1

Note that the order of groups is important here. Since lslx treats the group variable as factor, its order is determined by the sorted name of groups. For example, if three groups c, a, and b are considered, then the first group is a, the second is b, and the third is c.

In the current version of lslx, coefficient constraints cannot be imposed. It seems that testing coefficient invariance across groups is impossible in lslx. However, the present package parameterizes group coefficients in different way compared to other SEM software (Huang, 2018). Under lslx, each group coefficient is decomposed into a sum of a reference component and an increment component. If the reference component is assumed to be zero, the increment component represents the group coefficient, which is equivalent to the usual parameterization in other software solutions. On the other hand, if some group is set as reference (argument reference_group in new method, please see the section of Initialize Method), then the reference component now represents the group coefficient of the reference group and other increment components represent the differences from the reference group. The coefficient invariance across groups can be evaluated by examining the value or sparsity of the corresponding increment component.

Optimization Algorithm

Let θ denote the vector of model parameter. lslx tries to find a PL estimate for θ by minimizing the objective function objective(θ, λ) = loss(θ) + regularizer(θ, λ) where loss is the ML loss function, regularizer is a regularizer, possibly lasso (Tibshirani, 1996) or mcp (Zhang, 2010), and λ is a regularization parameter. The optimization algorithm for minimizing the PL criterion is based on an improved glmnet method (Friedman, Hastie, & Tibshirani, 2010) made by Yuan, Ho, and Lin (2012). The algorithm can be understood as a quasi-Newton method with inner loop and outer loop. The inner loop of the algorithm derives a quasi-Newton direction by minimizing a quadratic approximated objective function via coordinate descent. To save the computation time, the Hessian matrix for the quadratic term is approximated by the identity matrix, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, or the expected Hessian (Fisher scoring). Although the computational cost of BFGS approximation is much smaller than calculating expected hessian, our experience shows that the two methods perform similarly in terms of computation time because more outer iterations are required for BFGS. The inner loop stops if the change of the derived direction is quite small. The outer loop of the algorithm updates the value of parameter estimate via the derived quasi-Newton direction and Armijo's rule. The outer loop stops if the maximal absolute element of subgradient of objective function is smaller than the specified tolerance. The minimizer is the so-called PL estimates. Note that the PL estimates is a function of penalty level, i.e., PL estimates can vary under different penalty levels. An optimal penalty level can be chosen by using model selection criterion.

In lslx, PL estimates under each penalty level and convexity level specified by user will be calculated. The convexity levels will be sorted from large to small based on the suggestion of Mazumder (2011). The previous obtained PL estimate will be used as warm start for further minimization problem. Since the solution path is continuous, the warm start can speed up the convergence of minimization (see Friedman, Hastie, & Tibshirani, 2010).

Missing Data

When conducting SEM, it is easy to encounter the problem of missing data. In lslx, missing data can be handled by the listwise deletion method and the two-stage method (Yuan & Bentler, 2000). The listwise deletion method only uses fully complete observations for further SEM analysis. If the missing mechanism is missing completely at random (MCAR; Rubin, 1976), the listwise deletion method can yield a consistent estimator. The two-stage method first calculates the saturated moments by minimizing the likelihoods based on all of the available observations and then use the obtained saturated moment estimates for further SEM analysis. Under the assumption of missing at random (MAR; Rubin, 1976), it has been shown that the two-stage method can yield a consistent estimate. In addition, the standard errors of coefficients can be also consistently estimated if a correct asymptotic covariance of saturated moments is used. Because the two-stage approach is generally valid and efficient compared to the listwise deletion method, lslx set the two-stage method as default for handling the missing data problem. The current version also supports the use of auxiliary variables (see Savalei & Bentler, 2008). If the two-stage method is implemented, the standard error formula will be corrected for the presence of missing data (see Yuan & Lu, 2008 for technical details).

So far, lslx doesn't include the full-information maximum likelihood (FIML) method for missing values. One reason is that PL can be computationally intensive if many penalty levels are considered. The additional E-step in each iteration of FIML makes the problem worse. Another reason is that the two-step method has been shown to outperform FIML in simulation settings (Savalei & Falk, 2014). Therefore, we tend to believe that the implementation of FIML in PL may not bring further advantages over the two-step method.

Penalty Level Selection

Penalty level selection in lslx is based on optimizing the value of some information criterion. Many information criteria are available for this task. In the current version, available information criteria are

aic

Akaike Information Criterion (Akaike, 1974)

AIC(θ)=loss(θ) - (2 / N) * df(θ)

aic3

Akaike Information Criterion with Penalty Being 3 (Sclove, 1987)

AIC3(θ)=loss(θ) - (3 / N) * df(θ)

caic

Consistent Akaike Information Criterion (Bozdogan, 1987)

CAIC(θ)=loss(θ) - ((log(N) + 1) / N) * df(θ)

bic

Bayesian Information Criterion (Schwarz, 1978)

BIC(θ)=loss(θ) - (log(N) / N) * df(θ)

abic

Adjusted Bayesian Information Criterion (Sclove, 1987)

ABIC(θ)=loss(θ) - (log((N + 2) / 24 ) / N) * df(θ)

hbic

Haughton Bayesian Information Criterion (Haughton, 1997)

HBIC(θ)=loss(θ) - (log(N / π ) / N) * df(θ)

where

Note the formula for calculating the information criteria in lslx are different to other software solutions. The loss function value is used to replace the likelihood function value and hence the penalty term is also divided by sample size N. For each information criterion, a robust version is calculated if raw data is available. Their corresponding names are raic, raic3, rcaic, rbic, rabic, and rhbic with "r" standing for "robust". These robust criteria use the Satorra-Bentler scaling factor for correcting degree of freedom. For the case of normal data and correctly specified model, the two versions will be the same asymptotically.

Huang, Chen, and Weng (2017) have study the asymptotic behaviors of aic and bic under penalized estimation. They show that under suitable conditions, aic can select a model with minimum expected loss and bic can choose the most parsimonious one from models that attain the minimum expected loss. By the order of penalty term, we may expect: (1) the large sample behaviors of aic3 and tic will be similar to aic; and (2) the asymptotic behaviors of caic, abic, and hbic will be similar to bic. However, their small-sample performances require further studies.

Model Fit Evaluation

Given a chosen penalty level, we may evaluate the overall model fit by using fit indices. In the current version, available fit indices for model evaluation are

rmsea

Root Mean Square Error of Approximation (Steiger, 1998; Steiger & Lind, 1980)

RMSEA(θ)=√(G * max(loss(θ) / df(θ) - 1 / N, 0))

cfi

Comparative Fit Index (Bentler, 1990)

CFI(θ)=(max(loss_0 - df_0 / N, 0) - max(loss(θ) - df(θ) / N, 0)) / max(loss_0 - df_0 / N, 0)

nnfi

Non-Normed Fit Index (Tucker & Lewis, 1973)

NNFI(θ)=(loss_0 / df_0 - loss(θ) / df(θ)) / (loss_0 / df_0 - 1 /N)

srmr

Standardized Root Mean of Residual (Bentler, 1995)

SRMR(θ)=√(∑_g w_g ∑_i ∑_{j ≤q i} ((σ_{gij} - s_{gij})^2 / (σ_{gii} * σ_{gjj})) / (G * P * (P + 1) / 2)

+∑_g w_g ∑_i ((μ_{gi} - m_{gi}) ^ 2 / σ_{gii}) / (G * P))

where

In lslx, the baseline model is the model that assumes a diagonal covariance matrix and a saturated mean. Hence, the baseline model may not be appropriate if users hope to evaluate the goodness-of-fit of mean structure.

It is also possible to test overall model fit by formal statistical test. In the current version, statistical tests for likelihood ratio (LR) and root mean square error of approximation (RMSEA) can be implemented. If raw data is available, lslx calculates mean-adjusted versions of LR statistic (Satorra & Bentler, 1994) and RMSEA intervals (Brosseau-Liard, Savalei & Li, 2012; Li & Bentler, 2006). It should be noted that the classical tests may not be valid after penalty level selection because the task of penalty level selection may destroy the sampling distribution of test statistics (see Pötscher, 1991 for discussion). Valid post model selection inference methods require further development.

Coefficient Evaluation

Given a chosen penalty level, we may evaluate the significance of coefficients (or parameters). In the current version, standard errors based on the expected/observed Fisher information matrix and the sandwich formula are available (see Yuan & Hayashi, 2006 for discussion). Because the sandwich formula is generally valid compared to the approaches based on Fisher information, lslx uses the sandwich formula as default whenever raw data is available. Note that sandwich covariance matrix in lslx is calculated based on Equation (14) in Yuan and Hayashi (2006) but not Equation (2.12a) in Browne (1984) to accommodate the potential model misspecification. Again, the significance tests may not be valid after penalty level selection. After version 0.6.4, several post-selection mehtods are available (Huang, in press), inlcuding PoSI method with Scheffe constant (Berk, Brown, Buja, Zhang, & Zhao, 2013) and Polyhedral method (Lee, Sun, Sun, & Taylor, 2016).

Initialize Method

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$new(model, data, numeric_variable, ordered_variable, 
  group_variable, reference_group, weight_variable, auxiliary_variable, 
  sample_cov, sample_mean, sample_size, sample_moment_acov, verbose = TRUE)
Arguments
model

A character with length one to represent the model specification.

data

A data.frame of raw data. It must contains variables specified in model (and possibly the variables specified by group_variable and weight_variable).

numeric_variable

A character to specify which response variables should be transfromed into numeric.

ordered_variable

A character to specify which response variables should be transfromed into ordered.

weight_variable

A character with length one to specify what variable is used for sampling weight.

auxiliary_variable

A character to specify what variable(s) is used as auxiliary variable(s) for estimating saturated moments when missing data presents and two-step method is implemented. Auxiliary variable(s) must be numeric. If any categorical auxiliary is considered, please transform it into dummy variables before initialization.

group_variable

A character with length one to specify what variable is used for labeling group.

reference_group

A character with length one to specify which group is set as reference.

sample_cov

A numeric matrix (single group case) or a list of numeric matrix (multi-group case) to represent sample covariance matrixs. It must have row and column names that match the variable names specified in model.

sample_mean

A numeric (single group case) or a list of numeric (multi-group case) to represent sample mean vectors.

sample_size

A numeric (single group case) with length one or a list of numeric (multi-group case) to represent the sample sizes.

sample_moment_acov

A numeric matrix (single group case) or a list of numeric matrix (multi-group case) to represent asymptotic covariance for moments.

verbose

A logical to specify whether messages made by lslx should be printed.

Details

$new() initializes a new object of lslx R6 class for fitting semi-confirmatory structural equation modeling (SEM). In most cases, a new lslx object is initialized by supplying model and data. For details of syntax for model specification, see the section of Model Syntax. By default, types of response variables (numeric versus ordered) will be infered according to the imported data.frame. If users hope to transform variables types in lslx, numeric_variable and ordered_variable can be used. When multi-group analysis is desired, argument group_variable should be given to specify what variable is used for labeling group. Argument reference_group can be used to set reference group. Note that if some group is set as reference, the coefficients in other groups will represent increments from the reference. When the missingness of data depends on some other variables, auxiliary_variable can be used to specify auxiliary variables for estimate saturated moments. For details of missing data, see the section of Missing Data. If raw data is not available, lslx also supports initialization via sample moments. In that case, sample_cov and sample_size are required. If sample_mean is missing under moment initialization, it is assumed to be zero.

Set-Related Methods

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$free_coefficient(name, start, verbose = TRUE)
$penalize_coefficient(name, start, verbose = TRUE)
$fix_coefficient(name, start, verbose = TRUE)

$free_directed(left, right, group, verbose = TRUE)
$penalize_directed(left, right, group, verbose = TRUE)
$fix_directed(left, right, group, verbose = TRUE)

$free_undirected(both, group, verbose = TRUE)
$penalize_undirected(both, group, verbose = TRUE)
$fix_undirected(both, group, verbose = TRUE)

$free_block(block, group, type, verbose = TRUE)
$penalize_block(block, group, type, verbose = TRUE)
$fix_block(block, group, type, verbose = TRUE)

$free_heterogeneity(block, group, verbose = TRUE)
$penalize_heterogeneity(block, group, verbose = TRUE)
$fix_heterogeneity(block, group, verbose = TRUE)
Arguments
name

A character to indicate which coefficients should be reset.

start

A numeric to specify starting values. The length of start should be one or match the length of name to avoid ambiguity. If start is missing, the starting value will be set as (1) NA for free or penalized coefficient; and (2) 0 for fixed coefficient.

left

A character to indicate variable names in the left-hand side of operator "<-".

right

A character to indicate variable names in the right-hand side of operator "<-".

both

A character to indicate variable names in both side of operator "<->".

group

A character to indicate group names that the specified relations belong to.

block

A character with length one to indicate a block such that the corresponding target coefficient will be reset. Its value must be "f<-1", "y<-1", "f<-f", "f<-y", "y<-f", "y<-y", "f<->f", "f<->y", "y<->f", or "y<->y".

type

A character to indicate which type of parameter should be changed in the given block. Its value must be "free", "fixed", or "pen". If type is not specified, the types of all parameters in the given block will be modified.

verbose

A logical to specify whether messages made by lslx should be printed.

Details

Set-related methods include several member functions that can be used to modify the initialized model specification. Like most encapsulation objects, set-related function is used to modify the inner members of object. So far, the set-related methods are all established to modify the model. The data are protected without any modification.

$free_coefficient() / $penalize_coefficient() / $fix_coefficient() sets the coefficient named name as FREE / PENALIZED / FIXED with starting value start. In the case of single group analysis, argument name can be replaced by relations, i.e., the group name can be omitted.

$free_directed() / $penalize_directed() / $fix_directed() sets all the regression coefficients from variables in right to variables in left at groups in group as FREE / PENALIZED / FIXED.

$free_undirected() / $penalize_undirected() / $fix_undirected() sets all the covariances among variables in both at groups in group as FREE / PENALIZED / FIXED. Note that this method all always not modify the variance of variables specified in both.

$free_block() / $penalize_block() / $fix_block() sets all the parameters belonging to block at group with type as FREE / PENALIZED / FIXED.

$free_heterogeneity() / $penalize_heterogeneity() / $fix_heterogeneity() sets every target coefficient as FREE / PENALIZED / FIXED. A target coefficient should satisfy that (1) it belongs to block at group, and (2) it has either free or penalized reference component. The method is only available in the case of multi-group analysis with specified reference group.

Inside lslx object, every coefficient (or parameter) has its own name and belongs to some block.

Arguments in set-related methods may rely on the naming rule of coefficent name and block to modify model specification.

Fit-Related Methods

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$fit(penalty_method = "lasso", lambda_grid = "default", delta_grid = "default",
  step_grid = "default", loss = "default", algorithm = "default", 
  missing_method = "default", start_method = "default", 
  lambda_direction = "default", lambda_length = 50L, delta_length = 3L,
  threshold_value = 0.3, iter_out_max = 100L, iter_in_max = 50L, 
  iter_other_max = 500L, iter_armijo_max = 100L, tol_out = 1e-3, 
  tol_in = 1e-3, tol_other = 1e-7, step_size = 0.5, momentum = 0, 
  armijo = 1e-5, ridge_cov = 0, ridge_hessian = 1e-4, warm_start = TRUE, 
  positive_variance = TRUE, minimum_variance = 1e-4, enforce_cd = FALSE, 
  random_update = TRUE, weight_matrix = NULL, verbose = TRUE)
$fit_none(...)
$fit_lasso(lambda_grid = "default", ...)
$fit_ridge(lambda_grid = "default", ...)
$fit_elastic_net(lambda_grid = "default", delta_grid = "default", ...)
$fit_mcp(lambda_grid = "default", delta_grid = "default", ...)
$fit_forward(step_grid = "default", ...)
$fit_backward(step_grid = "default", ...)
Arguments
penalty_method

A character to specify the penalty method. There are two class penalty methods can be specified. For penalized estimation with a regularizer, "lasso", "ridge", "elastic_net", and "mcp" can be used. For penalized estimation with stepwise search, "forward" and "backward" can be implemented. If no penalty is considered, we can set penalty_method as "none".

lambda_grid

A non-negative numeric to specify penalty levels for the regularizer. If it is set as "default", its value will be generated automatically based on the variable scales.

delta_grid

A non-negative numeric to specify the combination weight for "elastic_net" or the convexity level for "mcp". If it is set as "default", its value will be generated automatically.

step_grid

A non-negative numeric to specify a grid for stepwise search with "forward" and "backward".

loss

A character to determine the loss function. The current version supports "ml" (maximum likelihood), "uls" (unweighted least squares), "dwls" (diagonal weighted least squres), and "wls" (weighted least squares). The maximum likelihood is only available for all continuous response variables. If the argument is set as "default", then (1) "ml" will be implemented for all continuous response variables; (2) "dwls" will be implemented for ordinal or mixed types response variables.

algorithm

A character to determine the method of optimization. The current version supports "gd" (gradient descent), "bfgs" (Broyden-Fletcher-Goldfarb-Shanno), "fisher" (Fisher scoring), and "dynamic" (an adaptive algorithm). If the argument is set as "default", then "dynamic" will be implemented.

missing_method

A character to determine the method for handling missing data (or NA). The current version supports "two_stage" and "listwise_deletion". If the argument is set as "default" and a raw data set is available, the "two_stage" will be implemented. If the argument is set as "default" and only moment data is available, the "listwise_deletion" will be used (actually, in this case no missing presences).

start_method

A character to determine the method for calculating unspecified starting values. The current version supports "mh" (McDonald & Hartmann, 1992) and "heuristic". If the argument is set as "default", the "mh" will be implemented.

lambda_direction

A character to determine the "direction" of lambda_grid. "decrease" sorts lambda_grid from large to small. On the contrary, "increase" sorts lambda_grid from small to large. If the argument is set as "default", "increase" will be used when the smallest element of lambda_grid is larger than zero; otherwise, "decrease" is assumed.

lambda_length

A numeric to specify the length of automatically generated lambda_grid under lambda_grid = "default".

delta_length

A numeric to specify the length of automatically generated delta_grid under delta_grid = "default".

threshold_value

A numeric to specify the "largest threshold value" for lambda_grid initialization.

iter_out_max

A positive integer to specify the maximal iterations for outer loop of the modified glmnet algorithm.

iter_in_max

A positive integer to specify the maximal iterations for inner loop of the modified glmnet algorith.

iter_other_max

A positive integer to specify the maximal iterations for other loop.

iter_armijo_max

A positive integer to specify the maximal iterations for searching step-size via Armijo rule.

tol_out

A small positive numeric to specify the tolerance (convergence criterion) for outer loop of the modified glmnet algorithm.

tol_in

A small positive numeric to specify the tolerance (convergence criterion) for inner loop of the modified glmnet algorithm.

tol_other

A small positive numeric to specify the tolerance (convergence criterion) for other loop.

step_size

A positive numeric smaller than one to specify the step-size.

momentum

A numeric between 0 and 1 for momentum parameter.

armijo

A small positive numeric for the constant in Armijo rule.

ridge_cov

A small positive numeric for the ridge of sample covariance matrix.

ridge_hessian

A small positive numeric for the ridge of approximated hessian in optimization.

ridge_weight

A small positive numeric for the ridge of weight matrix in weighted least squares.

warm_start

A logical to specify whether the warm start approach should be used.

positive_variance

A logical to specify whether the variance estimate should be constrained to be larger than minimum_variance.

minimum_variance

A numeric to specify the minimum value of variance if positive_variance = TRUE.

enforce_cd

A logic to specify whether coordinate descent should be used when no penalty function is used. Its default value is TRUE.

random_update

A logic to specify whether coordinate descent should be conducted in a random order. Its default value is TRUE.

weight_matrix

A list with length equaling to the number of groups to specify a user-defined weight matrix for least squares loss.

verbose

A logical to specify whether messages made by lslx should be printed.

...

Other passing arguments for calling $fit().

Details

Fit-related methods are used for fitting model to data. The success of these methods may depend on the specified fitting control. For details of optimization algorithm, see the section of Optimization Algorithm.

$fit() fits the specified model to data by minimizing a penalized loss function. It is the most comprehensive fit method and hence many arguments can be specified.

$fit_none() fits the specified model to data by minimizing a loss function without penalty. It is a user convinient wrapper of $fit() with penalty_method = "none".

$fit_lasso() fits the specified model to data by minimizing a loss function with lasso penalty (Tibshirani, 1996). It is a user convinient wrapper of $fit() with penalty_method = "lasso".

$fit_ridge() fits the specified model to data by minimizing a loss function with ridge penalty (Hoerl & Kennard, 1970). It is a user convinient wrapper of $fit() with penalty_method = "ridge".

$fit_elastic_net() fits the specified model to data by minimizing a loss function with elastic net penalty (Zou & Hastie, 2005). It is a user convinient wrapper of $fit() with penalty_method = "elastic_net".

$fit_mcp() method fits the specified model to data by minimizing a loss function with mcp (Zhang, 2010). It is a user convinient wrapper of $fit() with penalty_method = "mcp".

$fit_forward() method fits the specified model to data by minimizing a loss function with forward searching. It is a user convinient wrapper of $fit() with penalty_method = "forward".

$fit_backward() method fits the specified model to data by minimizing a loss function with backward searching. It is a user convinient wrapper of $fit() with penalty_method = "backward".

Summarize Method

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$summarize(selector, lambda, delta, step, standard_error = "default", 
  debias = "default", inference = "default", alpha_level = .05, 
  include_faulty = FALSE, style = "default", mode = "default", digit = 3, 
  interval = TRUE, output)
Arguments
selector

A character to specify a selector for determining an optimal penalty level. Its value can be any one in "aic", "aic3", "caic", "bic", "abic", "hbic", or their robust counterparts "raic", "raic3", "rcaic", "rbic", "rabic", "rhbic" if raw data is available.

lambda

A numeric to specific a chosen optimal penalty level. If the specified lambda is not in lambda_grid, a nearest legitimate value will be used.

delta

A numeric to specific a chosen optimal convexity level. If the specified delta is not in delta_grid, a nearest legitimate value will be used.

step

A numeric to specific a chosen step for stepwise searching. If the specified step is not in step_grid, a nearest legitimate value will be used.

standard_error

A character to specify the standard error to be used for hypothesis testing. The argument can be either "sandwich", "expected_information", and "observed_information". If it is specified as "default", it will be set as (1) "sandwich" when raw data is available; (2) "observed_information" when only moment data is available.

debias

A character to specify a debias method for obtaining a debiased estimator. Its value can be either "none" or "one_step". If it is specified as "default", "none" will be used unless post = "polyhedral" is used.

inference

A character to specify the method for post selection inference. The current version supports "naive", "polyhedral", and "scheffe". If it is specified as "default", "naive" will be used.

alpha_level

A numeric to specify the alpha level for constructing 1 - alpha confidence intervals.

include_faulty

A logical to specify whether non-convergence or non-convexity results should be removed for penalty level selection. Non-convergence result determined by examining the maximal elements of absolute objective gradient and the number of iterations. non-convexity result is determined by checking the minimum of univariate approximate hessian.

style

A character to specify whether the style of summary. Its value must be either "default", "manual", "minimal", or "maximal".

mode

A character to specify the mode of summary. Its value must be "print" or "return". "print" will print the summary result and "return" will return a list of the summary result. If it is specified as "default", it will be set as "print".

digit

An integer to specify the number of digits to be displayed.

interval

A logical to specify whether the confidence interval should be printed.

Details

$summarize() prints a summary for the fitting result under a selected peanlty/convexity level. It requires users to specify which selector should be used or a combination of gamma and lambda.. By default, the summary includes model information, numerical conditions, fit indices, coefficient estimates, and related statistical inference. It can be modified by the style argument. For details of evaluation and inference methods, see the sections of Model Fit Evaluation and Coefficient Evaluation.

Test-Related Methods

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$test_lr(selector, lambda, delta, step, include_faulty = FALSE)
$test_rmsea(selector, lambda, delta, step, 
  alpha_level = .05, include_faulty = FALSE)
$test_coefficient(selector, lambda, delta, step, 
  standard_error = "default", debias = "default", inference = "default", 
  alpha_level = .05, include_faulty = FALSE)
Arguments
selector

A character to specify a selector for determining an optimal penalty level. Its value can be any one in "aic", "aic3", "caic", "bic", "abic", "hbic", or their robust counterparts "raic", "raic3", "rcaic", "rbic", "rabic", "rhbic" if raw data is available.

delta

A numeric to specific a chosen optimal weight for elastic net or convexity level for mcp. If the specified delta is not in delta_grid, a nearest legitimate value will be used.

step

A numeric to specific a chosen step for stepwise searching. If the specified step is not in step_grid, a nearest legitimate value will be used.

standard_error

A character to specify the standard error to be used for hypothesis testing. The argument can be either "sandwich", "expected_information", and "observed_information". If it is specified as "default", it will be set as (1) "sandwich" when raw data is available; (2) "observed_information" when only moment data is available.

debias

A character to specify a debias method for obtaining a debiased estimator. Its value can be either "none" or "one_step". If it is specified as "default", "none" will be used unless post = "polyhedral" is used.

inference

A character to specify the method for post selection inference. The current version supports "naive", "polyhedral", and "scheffe". If it is specified as "default", "naive" will be used.

alpha_level

A numeric to specify the alpha level for constructing 1 - alpha confidence intervals.

include_faulty

A logical to specify whether non-convergence or non-convexity results should be removed for penalty level selection. Non-convergence result determined by examining the maximal elements of absolute objective gradient and the number of iteration. non-convexity result is determined by checking the minimum of univariate approximate hessian.

Details

Test-related methods are used to obtain the result of specific statistical test. So far, only tests for likelihood ratio (LR), root mean square error of approximation (RMSEA), and coefficients are available.

$test_lr() returns a data.frame of result for likelihood ratio test. If raw data is available, it also calculates a mean-adjusted statistic. For details of significance test method for LR, see the section of Model Fit Evaluation.

$test_rmsea() returns a data.frame of result for rmsea confidence intervals. If raw data is available, it also calculates a mean-adjusted confidence interval (Brosseau-Liard, Savalei & Li, 2012; Li & Bentler, 2006). For details of confidence interval construction for RMSEA, see the section of Model Fit Evaluation.

$test_coefficient() returns a data.frame of result for coefficient significance and confidence interval. For details of standard error formula for coefficients, see the section of Coefficient Evaluation.

Plot-Related Methods

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$plot_numerical_condition(condition, x_scale = "default", 
  mode = "default")
$plot_information_criterion(criterion, x_scale = "default", 
  mode = "default")
$plot_fit_index(index, x_scale = "default", mode = "default")
$plot_coefficient(block, left, right, both, x_scale = "default", 
  mode = "default")
Arguments
condition

A character to specify which numerical conditions should be plotted. Its value must be "objective_value", "objective_gradient_abs_max", "objective_hessian_convexity", "n_iter_out", "loss_value", "n_nonzero_coefficient", or their combination.

criterion

A character to specify which information criteria should be plotted. Its value must be "aic", "aic3", "caic", "bic", "abic", "hbic", "raic", "raic3", "rcaic", "rbic", "rabic", "rhbic", or their combination.

index

A character to specify which fit indices should be plotted. Its value must be "rmsea", "cfi", "nnfi", "srmr", or their combination.

block

A character with length one to indicate a block such that the corresponding target coefficient will be reset. Its value must be one of "f<-1", "y<-1", "f<-f", "f<-y", "y<-f", "y<-y", "f<->f", "f<->y", "y<->f", or "y<->y".

left

A character to specify the variables in the left-hand side of operator in block.

right

A character to specify the variables in the right-hand side of operator in block.

both

A character to specify the variables in both sides of operator in block.

lambda_scale

A character to specify the scale of lambda (x-axis) for coord_trans() in ggplot2.

mode

A character to specify the mode of plot. Its value must be "plot" or "return". "plot" will plot the result and "return" will return a data.frame for plot. If it is specified as "default", it will be set as "plot".

Details

Plot-related methods are used for visualizing the fitting results.

$plot_numerical_condition() plots the values of selected numerical conditions. It can be used to assess the quality of optimization. By default, "n_iter_out", "objective_gradient_abs_max", and "objective_hessian_convexity" across the given penalty levels are plotted.

$plot_information_criterion() shows how the values of information criteria vary with penalty levels. By default, "aic", "aic3", "caic", "bic", "abic", and "hbic" are plotted.

$plot_fit_index() shows how the values of fit indices vary with penalty levels. By default, "rmsea", "cfi", "nnfi", and "srmr" are plotted.

$plot_coefficient() visualizes the solution paths of coefficients belonging to the intersection of block, left, right, and both arguments. By default, all of the coefficients are plotted.

Get-Related Methods

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$get_model()
$get_data()
$get_fitting()

Details

Get-related methods are defined to obtain a deep copy of members inside lslx. Note that all of the data members of lslx are set as private to protect the inner data structure. They cannot be assessed directly via $.

$get_model() returns a deep copy of model member in the current lslx object.

$get_data() returns a deep copy of data member in the current lslx object.

$get_fitting() returns a deep copy of fitting member in the current lslx object.

Extract-Related Methods

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$extract_specification()
$extract_saturated_cov()
$extract_saturated_mean()
$extract_saturated_moment_acov()

$extract_penalty_level(selector, lambda, delta, step, 
  include_faulty = FALSE)

$extract_numerical_condition(selector, lambda, delta, step, 
  include_faulty = FALSE)
$extract_information_criterion(selector, lambda, delta, step, 
  include_faulty = FALSE)
$extract_fit_index(selector, lambda, delta, step, include_faulty = FALSE)
$extract_cv_error(selector, lambda, delta, step, include_faulty = FALSE)

$extract_coefficient(selector, lambda, delta, step, type = "default", 
  include_faulty = FALSE)
$extract_debiased_coefficient(selector, lambda, delta, step, type = "default", 
  include_faulty = FALSE)

$extract_implied_cov(selector, lambda, delta, step, 
  include_faulty = FALSE)
$extract_implied_mean(selector, lambda, delta, step, 
  include_faulty = FALSE)
$extract_residual_cov(selector, lambda, delta, step, 
  include_faulty = FALSE)
$extract_residual_mean(selector, lambda, delta, step, 
  include_faulty = FALSE)

$extract_coefficient_matrix(selector, lambda, delta, step, block, 
  include_faulty = FALSE)

$extract_moment_jacobian(selector, lambda, delta, step, 
  type = "default", include_faulty = FALSE)

$extract_expected_information(selector, lambda, delta, step, 
  type = "default", include_faulty = FALSE)
$extract_observed_information(selector, lambda, delta, step, 
  type = "default", include_faulty = FALSE)

$extract_bfgs_hessian(selector, lambda, delta, step, type = "default", 
  include_faulty = FALSE)
$extract_score_acov(selector, lambda, delta, step, type = "default", 
  include_faulty = FALSE)
$extract_coefficient_acov(selector, lambda, delta, step, 
  standard_error = "default", type = "default", include_faulty = FALSE)

$extract_loss_gradient(selector, lambda, delta, step, type = "default", 
  include_faulty = FALSE)
$extract_regularizer_gradient(selector, lambda, delta, step, 
  type = "default", include_faulty = FALSE)
$extract_objective_gradient(selector, lambda, delta, step, 
  type = "default", include_faulty = FALSE)
Arguments
selector

A character to specify a selector for determining an optimal penalty level. Its value can be any one in "aic", "aic3", "caic", "bic", "abic", "hbic", or their robust counterparts "raic", "raic3", "rcaic", "rbic", "rabic", "rhbic" if raw data is available.

lambda

A numeric to specific a chosen optimal penalty level. If the specified lambda is not in lambda_grid, a nearest legitimate value will be used.

delta

A numeric to specific a chosen optimal weight for elastic net or convexity level for mcp. If the specified delta is not in delta_grid, a nearest legitimate value will be used.

step

A numeric to specific a chosen step for stepwise searching. If the specified step is not in step_grid, a nearest legitimate value will be used.

standard_error

A character to specify the standard error to be used for hypothesis testing. The argument can be either "sandwich", "expected_information", and "observed_information". If it is specified as "default", it will be set as (1) "sandwich" when raw data is available; (2) "observed_information" when only moment data is available.

block

A character with length one to indicate a block such that the corresponding target coefficient will be reset. Its value must be "f<-1", "y<-1", "f<-f", "f<-y", "y<-f", "y<-y", "f<->f", "f<->y", "y<->f", or "y<->y".

type

A character to specify the type of parameters that will be used to compute the extracted quantity. The argument can be either "all", "fixed", "free", "pen", "effective" (include "free" + "selected"), and "selected" (non-zero element of "pen"). If it is specified as "default", it will be set as all.

include_faulty

A logical to specify whether non-convergence or non-convexity results should be removed for penalty level selection. Non-convergence result determined by examining the maximal elements of absolute objective gradient and the number of iterations. non-convexity result is determined by checking the minimum of univariate approximate hessian.

Details

Many extract-related methods are defined to obtain quantities that can be used for further SEM applications or model diagnosis. Some of these quantities only depend on data (e.g., saturated sample covariance matrix), but some of them relies on a penalty level (e.g., gradient of objective function). An optimal penalty level can be determined by specifying a selector or a combination of gamma and lambda. When implementing fit-related methods, lslx only records necessary results for saving memory. Therefore, the extract-related methods not only extract objects but may possibly re-compute some of them. If the extracted quantity is a function of model coefficients, note that fixed coefficient is still considered as a valid variable. For example, the sub-gradient of objective function is calculated by considering both estimated coefficients and fixed coefficients. Hence, it is not appropriate to use all the elements of the sub-gradient to evaluate the optimality of solution because the values of fixed coefficients are not optimized. Fortunately, the type argument can be used to choose desired parameters by their types.

$extract_specification() returns a data.frame of model specification.

$extract_saturated_cov() returns a list of saturated sample covariance matrix(s).

$extract_saturated_mean() returns a list of saturated sample mean vector(s).

$extract_saturated_moment_acov() returns a list of asymptotic covariance matrix(s) of saturated moments. Note that if raw data is not available, asymptotic covariance matrix is calculated by assuming normality for data.

$extract_penalty_level() returns a character of the index name of the optimal penalty level.

$extract_numerical_condition() returns a numeric of the numerical conditions.

$extract_information_criterion() returns a numeric of the values of information criteria.

$extract_fit_indice() returns a numeric of the values of fit indices.

$extract_coefficient() returns a numeric of estimates of the coefficients.

$extract_implied_cov() returns a list of model-implied covariance matrix(s).

$extract_implied_mean() returns a list of model-implied mean vector(s).

$extract_residual_cov() returns a list of residual matrix(s) of covariance.

$extract_residual_mean() returns a list of residual vector(s) of mean.

$extract_coefficient_matrix() returns a list of coefficient matrix(s) specified by block.

$extract_moment_jacobian() returns a matrix of Jacobian of moment structure.

$extract_expected_information() returns a matrix of the expected Fisher information matrix.

$extract_observed_information() returns a matrix of the observed Fisher information matrix. Note that the observed information matrix is calculated via numerical differentiation for the gradient of loss.

$extract_bfgs_hessian() returns a matrix of the BFGS Hessian matrix.

$extract_score_acov() returns a matrix of the asymptotic covariance of scores.

$extract_coefficient_acov() returns a matrix of the asymptotic covariance of coefficients. For details of standard error formula, see the section of Coefficient Evaluation.

$extract_loss_gradient() returns a matrix of the gradient of loss function.

$extract_regularizer_gradient() returns a matrix of the sub-gradient of regularizer.

$extract_objective_gradient() returns a matrix of the sub-gradient of objective function.

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactionson Automatic Control, 19(6), 716–723.

Bates, D., & Eddelbuettel, D. (2013). Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package. Journal of Statistical Software, 52(5), 1–24.

Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.

Bentler, P. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107(2), 238–246.

Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L. (2013). Valid postselection inference. The Annals of Statistics, 41(2), 802–837.

Bozdogan, H. (1987). Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345–370.

Browne, M. W. (1984). Asymptotic distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37(1), 62–83.

Chang, W. (2017). R6: Classes with Reference Semantics.

Eddelbuettel, D., & François, R. (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1–18.

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1–22.

Haughton, D. M. A., Oud, J. H. L., & Jansen, R. A. R. G. (1997). Information and other criteria in structural equation model selection. Communications in Statistics - Simulation and Computation, 26(4), 1477–1516.

Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.

Huang, P. H. (2018). A Penalized Likelihood Method for Multi-Group Structural Equation Modeling. British Journal of Mathematical and Statistical Psychology, 71(3), 499-522.

Huang, P. H. (2020). lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood. Journal of Statistical Software. 93(7), 1-37.

Huang, P. H. (in press). Post-selection inference in Structural Equation Modeling. Multivariate Behavioral Research.

Huang, P. H., Chen, H., & Weng, L. J. (2017). A Penalized Likelihood Method for Structural Equation Modeling. Psychometrika, 82(2), 329–354.

Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA. Multivariate Behavioral Research, 47(6), 904-930.

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Examples

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## EXAMPLE: Regression Analysis with Lasso Penalty ##
# run `vignette("regression-analysis")` to see the vignette
# generate data for regression analysis
set.seed(9487)
x <- matrix(rnorm(2000), 200, 10)
colnames(x) <- paste0("x", 1:10)
y <- matrix(rnorm(200), 200, 1)
data_reg <- data.frame(y, x)

# specify regression model with penalized coefficients
model_reg <- "y <= x1 + x2 + x3 + x4
              y <~ x5 + x6 + x7 + x8 + x9 + x10"

# initialize lslx object via specified model and raw data
lslx_reg <- lslx$new(model = model_reg, 
                     data = data_reg)

# fit specified model to data with lasso under specified penalty levels
lslx_reg$fit(penalty_method = "lasso",
             lambda_grid = seq(.00, .30, .02))

# summarize fitting result under penalty level selected by 'aic'
lslx_reg$summarize(selector = "aic")


## EXAMPLE: Semi-Confirmatory Factor Analysis ##
# run `vignette("factor-analysis")` to see the vignette
# specify semi-confirmatory factor analysis model
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  <=> 1 * visual
             textual <=> 1 * textual
             speed   <=> 1 * speed"
          
# initialize lslx object via specified model and raw data
lslx_fa <- lslx$new(model = model_fa,
                    data = lavaan::HolzingerSwineford1939)
                    
# fit with mcp under specified penalty levels and convexity levels
lslx_fa$fit(penalty_method = "mcp", 
            lambda_grid = seq(.02, .60, .02), 
            delta_grid = c(1.5, 3.0, Inf))

# summarize fitting result under penalty level selected by 'bic'
lslx_fa$summarize(selector = "bic")


## EXAMPLE: Semi-Confirmatory Structural Equation Modeling ##
# run `vignette("structural-equation-modeling")` to see the vignette
# specify structural equation modeling model
model_sem <- "fix(1) * x1 + x2 + x3   <=: ind60
              fix(1) * y1 + y2 + y3 + y4 <=: dem60
              fix(1) * y5 + y6 + y7 + y8 <=: dem65
              dem60 <= ind60
              dem65 <= ind60 + dem60"

# initialize lslx object via specified model and sample moments
lslx_sem <- lslx$new(model = model_sem,
                     sample_cov = cov(lavaan::PoliticalDemocracy),
                     sample_size = nrow(lavaan::PoliticalDemocracy))

# set some covariances of errors as penalized 
lslx_sem$penalize_coefficient(name = c("y1<->y5",
                                       "y2<->y4",
                                       "y2<->y6",
                                       "y3<->y7",
                                       "y4<->y8",
                                       "y6<->y8"))

# fit with lasso under default penalty levels
lslx_sem$fit_lasso(lambda_length = 25)

# summarize fitting result under penalty level selected by 'abic'
lslx_sem$summarize(selector = "abic")


## EXAMPLE: Factor Analysis with Missing Data ##
# run `vignette("missing-data-analysis")` to see the vignette
# create missing values for x5 and x9 by the code in package semTools
data_miss <- lavaan::HolzingerSwineford1939
data_miss$x5 <- ifelse(data_miss$x1 <= quantile(data_miss$x1, .3), 
                       NA, data_miss$x5)
data_miss$age <- data_miss$ageyr + data_miss$agemo/12
data_miss$x9 <- ifelse(data_miss$age <= quantile(data_miss$age, .3), 
                       NA, data_miss$x9)

# specify confirmatory factor analysis model
model_miss <- "visual  :=> x1 + x2 + x3 
               textual :=> x4 + x5 + x6
               speed   :=> x7 + x8 + x9
               visual  <=> 1 * visual
               textual <=> 1 * textual
               speed   <=> 1 * speed"

# "ageyr" and "agemo" are set as auxiliary variables
lslx_miss <- lslx$new(model = model_miss,
                      data = data_miss,
                      auxiliary_variable = c("ageyr", "agemo"))
                      
# penalize all covariances among residuals
lslx_miss$penalize_block(block = "y<->y", 
                         type = "fixed",
                         verbose = FALSE)

# fit with lasso under default penalty levels
lslx_miss$fit_lasso(lambda_length = 25)

# summarize fitting result under penalty level selected by 'raic'
lslx_miss$summarize(selector = "raic")


## EXAMPLE: Multi-Group Factor Analysis ##
# run `vignette("multi-group-analysis")` to see the vignette
# specify multi-group factor analysis model
model_mgfa <- "visual  :=> 1 * x1 + x2 + x3
               textual :=> 1 * x4 + x5 + x6
               speed   :=> 1 * x7 + x8 + x9"

# "school" is set as group variable and "Pasteur" is specified as reference
lslx_mgfa <- lslx$new(model = model_mgfa,
                      data = lavaan::HolzingerSwineford1939,
                      group_variable = "school",
                      reference_group = "Pasteur")

# penalize increment components of loadings and intercepts in 'Grant-White'
lslx_mgfa$penalize_heterogeneity(block = c("y<-1", "y<-f"), 
                                 group = "Grant-White")

# free increment components of means of latent factors in 'Grant-White'
lslx_mgfa$free_block(block = "f<-1", 
                     group = "Grant-White")
                     
# fit with mcp under default penalty levels and specified convexity levels
lslx_mgfa$fit_mcp(lambda_length = 25)

# summarize fitting result under penalty level selected by 'hbic'
lslx_mgfa$summarize(selector = "hbic")

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