Package 'lvnet' reference manual

Title:	Latent Variable Network Modeling
Description:	Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
Authors:	Sacha Epskamp
Maintainer:	Sacha Epskamp <[email protected]>
License:	GPL-2
Version:	0.3.5
Built:	2024-11-13 06:41:34 UTC
Source:	CRAN

Latent variable graphical LASSO using EBIC to select optimal tuning parameter

Description

This function minimizes the Extended Bayesian Information Criterion (EBIC; Chen and Chen, 2008) to choose the lvglasso tuning parameter. See lvglasso

Usage

EBIClvglasso(S, n, nLatents, gamma = 0.5, nRho = 100, lambda, ...)
EBIClvglasso(S, n, nLatents, gamma = 0.5, nRho = 100, lambda, ...)

Arguments

`S`	Sample variance-covariance matrix
`n`	Sample Size
`nLatents`	Number of latent variables
`gamma`	EBIC hyper-parameter
`nRho`	Number of tuning parameters to test
`lambda`	The lambda argument containing factor loadings, only used for starting values!
`...`	Arguments sent to `lvglasso`

Value

The optimal result of lvglasso, with two more elements:

`rho`	The selected tuning parameter
`ebic`	The optimal EBIC

Author(s)

Sacha Epskamp <[email protected]>

References

Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771.

Update lvnatLasso results to select a different model

Description

This function can be used to select a model using any fit index

Usage

lassoSelect(object, select, minimize = TRUE, refit = TRUE, lassoTol = 1e-04)
lassoSelect(object, select, minimize = TRUE, refit = TRUE, lassoTol = 1e-04)

Arguments

`object`	An `lvnetLasso` object
`select`	A raw R expression using names used in the `object$fitMeasures` part of the output of `lvnet`
`minimize`	Logical. Minimize or maximize?
`refit`	Logical. Should the new best model be refitted.
`lassoTol`	Tolerance for absolute values to be treated as zero in counting parameters.

Author(s)

Sacha Epskamp <[email protected]>

Examples

## Not run: 
# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_theta:
res <- lvnetLasso(Data, "omega_theta", lambda = Lambda)
res$best
summary(res)

# Update to use EBIC:
resEBIC <- lassoSelect(res, ebic)
summary(resEBIC)

# Update to use minimal fitting model with RMSEA < 0.05:
resMinimal <- lassoSelect(res, df * (rmsea < 0.05), minimize = FALSE)
summary(resMinimal)

## End(Not run)
## Not run: 
# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_theta:
res <- lvnetLasso(Data, "omega_theta", lambda = Lambda)
res$best
summary(res)

# Update to use EBIC:
resEBIC <- lassoSelect(res, ebic)
summary(resEBIC)

# Update to use minimal fitting model with RMSEA < 0.05:
resMinimal <- lassoSelect(res, df * (rmsea < 0.05), minimize = FALSE)
summary(resMinimal)

## End(Not run)

Convert lavaan model to lvnet model matrices

Description

This function can be used to easily generate input matrices for lvnet based on a lavaan model.

Usage

lav2lvnet(model, data, std.lv = TRUE, lavaanifyOps = list(auto = TRUE, std.lv = std.lv))
lav2lvnet(model, data, std.lv = TRUE, lavaanifyOps = list(auto = TRUE, std.lv = std.lv))

Arguments

`model`	Lavaan model syntax
`data`	The dataset. Only used to extract order of variables names from the columnnames.
`std.lv`	Should the model be identified by constraining latent variable variance to 1. Defaults to `TRUE` unlike lavaan! This is because the starting values work better for this identification.
`lavaanifyOps`	A list with other options sent to `lavaanify`

Value

A list with the model matrices for lambda, psi, theta and beta

Author(s)

Sacha Epskamp <[email protected]>

Examples

## Not run: 
library("lavaan")

# Load dataset:
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# lavaan model
HS.model <- '
visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '

# fit via lavaan:
lavFit <- cfa(HS.model, HolzingerSwineford1939[7:15],std.lv=TRUE)

# Fit via lvnet:
mod <- lav2lvnet(HS.model, HolzingerSwineford1939[7:15])
lvnetFit <- lvnet(Data, lambda = mod$lambda, psi = mod$psi)

# Compare:
Compare <- data.frame(
  lvnet = round(unlist(lvnetFit$fitMeasures)[c("npar","df","chisq","fmin","aic","bic",
                                              "rmsea","cfi","tli","nfi","logl")],3),
  lavaan = round(fitMeasures(lavFit)[c("npar","df","chisq","fmin","aic","bic","rmsea",
                                      "cfi","tli","nfi","logl")],3))

Compare

## End(Not run)
## Not run: 
library("lavaan")

# Load dataset:
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# lavaan model
HS.model <- '
visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '

# fit via lavaan:
lavFit <- cfa(HS.model, HolzingerSwineford1939[7:15],std.lv=TRUE)

# Fit via lvnet:
mod <- lav2lvnet(HS.model, HolzingerSwineford1939[7:15])
lvnetFit <- lvnet(Data, lambda = mod$lambda, psi = mod$psi)

# Compare:
Compare <- data.frame(
  lvnet = round(unlist(lvnetFit$fitMeasures)[c("npar","df","chisq","fmin","aic","bic",
                                              "rmsea","cfi","tli","nfi","logl")],3),
  lavaan = round(fitMeasures(lavFit)[c("npar","df","chisq","fmin","aic","bic","rmsea",
                                      "cfi","tli","nfi","logl")],3))

Compare

## End(Not run)

Latent variable graphical LASSO

Description

The lvglasso algorithm to estimate network structures containing latent variables, as proposed by Yuan (2012). Uses the glasso package (Friedman, Hastie and Tibshirani, 2014) and mimics input and output of the glasso function.

Usage

lvglasso(S, nLatents, rho = 0, thr = 1e-04, maxit = 10000, lambda)
lvglasso(S, nLatents, rho = 0, thr = 1e-04, maxit = 10000, lambda)

Arguments

`S`	Sample variance-covariance matrix
`nLatents`	Number of latent variables.
`rho`	The LASSO tuning parameter
`thr`	The threshold to use for convergence
`maxit`	Maximum number of iterations
`lambda`	The lambda argument containing factor loadings, only used for starting values!

Value

A list of class lvglasso containing the following elements:

`w`	The estimated variance-covariance matrix of both observed and latent variables
`wi`	The estimated inverse variance-covariance matrix of both observed and latent variables
`pcor`	Estimated partial correlation matrix of both observed and latent variables
`observed`	Logical vector indicating which elements of w, wi and pcor are observed
`niter`	The number of iterations used
`lambda`	The estimated lambda matrix, when result is transformed to EFA model
`theta`	The estimated theta matrix
`omega_theta`	The estimated omega_theta matrix
`psi`	The estimated psi matrix

Author(s)

Sacha Epskamp <[email protected]>

References

Yuan, M. (2012). Discussion: Latent variable graphical model selection via convex optimization.The Annals of Statistics,40, 1968-1972.

Jerome Friedman, Trevor Hastie and Rob Tibshirani (2014). glasso: Graphical lasso-estimation of Gaussian graphical models. R package version 1.8. http://CRAN.R-project.org/package=glasso

Confirmatory Latent Variable Network Models

Description

This function utilizes OpenMx (Boker et al., 2011, 2014) to confirmatory test latent variable network models between P manifests and M latents. See the details section for information about the modeling framework used. All the input matrices can be assigned R matrices with numbers indicating fixed values and NA indicating a value is free to estimate.

Usage

lvnet(data, lambda, beta, omega_theta, delta_theta, omega_psi, delta_psi, psi, theta, 
        sampleSize, fitInd, fitSat, startValues = list(), scale = FALSE, nLatents,
        lasso = 0,  lassoMatrix, lassoTol = 1e-4, ebicTuning = 0.5, 
        mimic = c("lavaan","lvnet"), fitFunction = c("penalizedML", "ML"), exogenous)
lvnet(data, lambda, beta, omega_theta, delta_theta, omega_psi, delta_psi, psi, theta, 
        sampleSize, fitInd, fitSat, startValues = list(), scale = FALSE, nLatents,
        lasso = 0,  lassoMatrix, lassoTol = 1e-4, ebicTuning = 0.5, 
        mimic = c("lavaan","lvnet"), fitFunction = c("penalizedML", "ML"), exogenous)

Arguments

`data`	An N (sample size) x P matrix or data frame containing the raw data, or a P x P variance-covariance matrix.
`lambda`	A P x M matrix indicating factor loadings. Defaults to a full NA P x M matrix if psi or omega_psi is not missing, or a P x 0 dummy matrix.
`beta`	An M x M matrix indicating linear effects between latent variables. Defaults to an M x M matrix containing only zeroes.
`omega_theta`	A P x P matrix encoding the residual network structure. By default, theta is modeled instead.
`delta_theta`	A P x P diagonal scaling matrix. Defaults to NA on all diagonal elements. Only used if omega_theta is modeled.
`omega_psi`	An M x M matrix containing the latent network structure. Dy default, psi is modeled instead.
`delta_psi`	A diagonal M x M scaling matrix. Defaults to an identity matrix. Only used if omega_psi is modeled.
`psi`	An M x M variance-covariance matrix between latents and latent residuals. Defaults to a full NA matrix.
`theta`	A P x P variance-covariance matrix of residuals of the observed variables. Defaults to a diagonal matrix containing NAs
`sampleSize`	The sample size, only used if `data` is assigned a variance-covariance matrix.
`fitInd`	The fit of the independence model. Used to speed up estimation fitting multiple models.
`fitSat`	The fit of the saturated model. Used to speed up estimation fitting multiple models.
`startValues`	An optional named list containing starting values of each model. e.g., `list(lambda = matrix(1,9,3))` would set the starting values of a 10 x 3 lambda matrix to ones.
`scale`	Logical, should data be standardized before running lvnet?
`nLatents`	The number of latents. Allows for quick specification when `lambda` is missing. Not needed is `lambda` is assigned.
`lasso`	The LASSO tuning parameter.
`lassoMatrix`	Character vector indicating the names of matrices to apply LASSO regularization on. e.g., `"omega_psi"` or `"omega_theta"`.
`lassoTol`	Tolerance for absolute values to be treated as zero in counting parameters.
`ebicTuning`	Tuning parameter used in extended Bayesian Information Criterion.
`mimic`	If set to `"lavaan"` (default), covariance matrix is rescaled and N is used rather than N - 1 in likelihood computation.
`fitFunction`	The fit function to be used. `penalizedML` will fit the penalized fit function and `ML` the maximum likelihood function.
`exogenous`	Numeric vector indicating which variables are exogenous.

Details

The modeling framework follows the all-y LISREL framework for Structural Equation Models (SEM; Hayduk, 1987) to model relationships between P observed variables and M latent variables:

sigma = lambda * (I - beta)^(-1) psi (I - beta)^(-1 T) * lambda^T + theta

Where Sigma is the P x P model-implied covariance matrix, lambda a P x M matrix of factor loadings, B an M x M matrix containing regression effects between latent variables, Psi a M x M covariance matrix of the latent variables/residuals and Theta a P x P covariance matrix of residuals of the observed indicators.

The lvnet function allows for two extensions of this modeling framework. First, psi can be chosen to be modeled as follows:

psi = delta_psi (I - omega_psi)^(-1) delta_psi

In which delta_psi is a M x M diagonal scaling matrix and omega_psi a M x M matrix containing zeroes on the diagonal and partial correlation coefficients on the offdiagonal values of two latent variables conditioned on all other latent variables. omega_psi therefore corresponds to a Gaussian Graphical Model, or a network structure.

Similarly, theta can be chosen to be modeled as follows:

theta = delta_theta (I - omega_theta)^(-1) delta_theta

In which delta_theta is a P x P diagonal scaling matrix and omega_theta a P x P matrix containing zeroes on the diagonal and partial correlation coefficients on the offdiagonal values of two residuals conditioned on all other residuals.

Modeling omega_psi is termed Latent Network Modeling (LNM) and modeling omega_theta is termed Residual Network Modeling (RNM). lvnet automatically chooses the appropriate modeling framework based on the input.

Value

An lvnet object, which is a list containing the following elements:

`matrices`	A list containing thee estimated model matrices
`sampleStats`	A list containing the covariance matrix (`covMat`) and sample size `sampleSize`
`mxResults`	The OpenMx object of the fitted model
`fitMeasures`	A named list containing the fit measures of the fitted model

Author(s)

Sacha Epskamp <[email protected]>

References

Boker, S. M., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., ... Fox, J. (2011). OpenMx: an open source extended structural equation modelingframework. Psychometrika, 76(2), 306-317

Boker, S. M., Neale, M. C., Maes, H. H., Wilde, M. J., Spiegel, M., Brick, T. R., ..., Team OpenMx. (2014). Openmx 2.0 user guide [Computer software manual].

Hayduk, L. A. (1987).Structural equation modeling with LISREL: Essentials advances. Baltimore, MD, USA: Johns Hopkins University Press.

Examples

# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Fit CFA model:
CFA <- lvnet(Data, lambda = Lambda)

# Latent network:
Omega_psi <- matrix(c(
  0,NA,NA,
  NA,0,0,
  NA,0,0
),3,3,byrow=TRUE)

# Fit model:
LNM <- lvnet(Data, lambda = Lambda, omega_psi=Omega_psi)

# Compare fit:
lvnetCompare(cfa=CFA,lnm=LNM)

# Summary:
summary(LNM)

# Plot latents:
plot(LNM, "factorStructure")
# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Fit CFA model:
CFA <- lvnet(Data, lambda = Lambda)

# Latent network:
Omega_psi <- matrix(c(
  0,NA,NA,
  NA,0,0,
  NA,0,0
),3,3,byrow=TRUE)

# Fit model:
LNM <- lvnet(Data, lambda = Lambda, omega_psi=Omega_psi)

# Compare fit:
lvnetCompare(cfa=CFA,lnm=LNM)

# Summary:
summary(LNM)

# Plot latents:
plot(LNM, "factorStructure")

Compare lvnet objects

Description

Compares several results of lvnet

Usage

lvnetCompare(...)
## S3 method for class 'lvnet'
anova(object, ...)
lvnetCompare(...)
## S3 method for class 'lvnet'
anova(object, ...)

Arguments

`object`	An `lvnet` object
`...`	Any number of `lvnet` objects. Arguments can be named to make the resulting table named.

Author(s)

Sacha Epskamp <[email protected]>

LASSO model selection

Description

This function runs lvnet for a number of different tuning parameters, selects the best model based on some criterion and refits that model to obtain accurate parameter estimates. The lassoSelect function can afterwards be used to select a different model.

Usage

lvnetLasso(data, lassoMatrix, lassoTol = 1e-04, nTuning = 20, 
  tuning.min = 0.01, tuning.max = 0.5, criterion = c("bic", "aic", 
  "ebic"), verbose = TRUE, refitFinal = TRUE, refitAll = FALSE, 
  nCores = 1, ...)
lvnetLasso(data, lassoMatrix, lassoTol = 1e-04, nTuning = 20, 
  tuning.min = 0.01, tuning.max = 0.5, criterion = c("bic", "aic", 
  "ebic"), verbose = TRUE, refitFinal = TRUE, refitAll = FALSE, 
  nCores = 1, ...)

Arguments

`data`	The data argument as used in `lvnet`
`lassoMatrix`	Vector indicating the matrix or matrices to use in LASSO optmimization
`lassoTol`	Tolerance for absolute values to be treated as zero in counting parameters.
`nTuning`	Number of tuning parameters to estimate.
`tuning.min`	Minimal tuning parameter
`tuning.max`	Maximal tuning parameter
`criterion`	Criterion to use in model selection
`verbose`	Should progress be printed to the console?
`refitFinal`	Logical, should the best fitting model be refitted without LASSO regularization?
`refitAll`	Logical, should all models be refitted without LASSO regularization (but with zeroes constrained) before evaluating fit criterium?
`nCores`	Number of cores to use in parallel computing.
`...`	Arguments sent to `lvnet`

Author(s)

Sacha Epskamp <[email protected]>

Examples

# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_theta:
## Not run: 
res <- lvnetLasso(Data, "omega_theta", lambda = Lambda)
res$best
summary(res)

## End(Not run)
# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_theta:
## Not run: 
res <- lvnetLasso(Data, "omega_theta", lambda = Lambda)
res$best
summary(res)

## End(Not run)

Refit lvnet model to new data

Description

Obtain fit indices from the estimated model parameters on a new dataset.

Usage

lvnetRefit(lvnetObject, data, sampleSize)
lvnetRefit(lvnetObject, data, sampleSize)

Arguments

`lvnetObject`	Output of `lvnet`.
`data`	New dataset or variance-covariance matrix.
`sampleSize`	Sample size (if `data` is a variance-covariance matrix).

Author(s)

Sacha Epskamp <[email protected]>

Step-wise exploratory search for optimal fitting model

Description

Performs stepwise search to optimize the structure of omega_theta, omega_psi, theta or psi. Starts at empty or full structure and iteratively adds or removes edges to optimize the criterion.

Usage

lvnetSearch(data, matrix = c("omega_theta", "omega_psi", "theta", "psi"), 
          criterion = c("bic", "ebic","chisq","aic"), 
          start =  c("default","empty","full"),  alpha = 0.05, lambda, sampleSize, 
          maxIter,  nCores = 1, maxChange = 1, ...,  verbose = TRUE, file, 
          startValues = list())
lvnetSearch(data, matrix = c("omega_theta", "omega_psi", "theta", "psi"), 
          criterion = c("bic", "ebic","chisq","aic"), 
          start =  c("default","empty","full"),  alpha = 0.05, lambda, sampleSize, 
          maxIter,  nCores = 1, maxChange = 1, ...,  verbose = TRUE, file, 
          startValues = list())

Arguments

`data`	The data argument as used in `lvnet`
`matrix`	Character string indicating the matrix to be optimized. Can be `"omega_theta"`, `"omega_psi"`, `"theta"` and `"psi"`.
`criterion`	Character string indicating the criterion to be used. `"AIC"` and `"BIC"` optimize the AIC or BIC respectively, and `"chisq"` performs chi-square tests to see if adding an edge significantly improves model fit or removing an edges does not significantly reduce model fit.
`start`	A character string indicating the structure of the matrix at the start of the algorithm. `"empty"` starts with a matrix with only zeroes and `"full"` starts with a matrix in which all elements are free to estimate. `"lvglasso"` employs the lvglasso algorithm (`EBIClvglasso` to find a starting structure for omega_theta and `"glasso"` employs the glasso algorithm to find a starting point for omega_psi (`EBICglasso`). `"default"` will lead to a full matrix if omega_psi or psi is optimized, and an empty matrix if omega_theta or theta is optimized.
`alpha`	The alpha level for chi-square significance testing.
`lambda`	The lambda argument as used in `lvnet`
`sampleSize`	The sample size, only used if `data` is a covariance matrix.
`maxIter`	The maximum number of edges to test. Defaults to M(M-1)/2
`nCores`	Number of cores to use in parallel estimation.
`maxChange`	Set to higher than one to change multiple edges in each run. Each iteration, maxChange is reset to max(number of changed edges - 1, 1). Can result in instable results when searching `"omega_theta"`.
`...`	Arguments sent to `lvnet`
`verbose`	Logical if progress should be printed to the consile.
`file`	An optional character string containing a file name to store temporary results in.
`startValues`	A list containing start values as used in `lvnet`

Value

An object of class lvnetSearch, which is a list containing:

`best`	The `lvnet` object of the best fitting model
`modList`	A list containing the chain of fitted models
`niter`	The number of iterations used

Author(s)

Sacha Epskamp <[email protected]>

Examples

# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_psi:
## Not run: 
res <- lvnetSearch(Data, "omega_psi", lambda = Lambda)
res$best

## End(Not run)
# Load dataset:
library("lavaan")
data(HolzingerSwineford1939)
Data <- HolzingerSwineford1939[,7:15]

# Measurement model:
Lambda <- matrix(0, 9, 3)
Lambda[1:3,1] <- NA
Lambda[4:6,2] <- NA
Lambda[7:9,3] <- NA

# Search best fitting omega_psi:
## Not run: 
res <- lvnetSearch(Data, "omega_psi", lambda = Lambda)
res$best

## End(Not run)

Plot model matrices

Description

Plot method for lvnet. For lvnetSearch and lvnetLasso objects this is simply defined as plot(object$best, ...)

Usage

 ## S3 method for class 'lvnet'
plot(x, what = c("factorStructure", "residual", "latent"), partial, 
        layout = "circle", ...)
 ## S3 method for class 'lvnetLasso'
plot(x, ...)
 ## S3 method for class 'lvnetSearch'
plot(x, ...)
## S3 method for class 'lvnet'
plot(x, what = c("factorStructure", "residual", "latent"), partial, 
        layout = "circle", ...)
 ## S3 method for class 'lvnetLasso'
plot(x, ...)
 ## S3 method for class 'lvnetSearch'
plot(x, ...)

Arguments

`x`	An `lvnet` object.
`what`	What to plot? `"factorStructure"` plots the factor loadings and latent correlations or network. `"residual"` the residual correlations or network and `"latent"` the latent correlations or network.
`partial`	Plot partial correlations instead of correlations? Defaults to `TRUE` if `omega_psi` or `omega_theta` is estimated.
`layout`	The layour argument as used in `qgraph`
`...`	Arguments sent to `qgraph`

Author(s)

Sacha Epskamp <[email protected]>

Summary method for lvnet

Description

Plot method for lvnet. For lvnetSearch and lvnetLasso objects this is simply defined as summary(object$best, ...)

Usage

 ## S3 method for class 'lvnet'
summary(object, include = c("input", "chisq", "infcrit", "fitindices", 
        "rmsea", "parests"), digits = 3, ...)
  ## S3 method for class 'lvnetLasso'
summary(object, ...)
 ## S3 method for class 'lvnetSearch'
summary(object, ...)
## S3 method for class 'lvnet'
summary(object, include = c("input", "chisq", "infcrit", "fitindices", 
        "rmsea", "parests"), digits = 3, ...)
  ## S3 method for class 'lvnetLasso'
summary(object, ...)
 ## S3 method for class 'lvnetSearch'
summary(object, ...)

Arguments

`object`	An `lvnet` object
`include`	Vector indicating what to include? `"input"` for the input used, `"chisq"` for the chi-square fit, `"infcrit"` for information criteria, `"fitindices"` for fit indices, `"rmsea"` for the RMSEA, ans `"parests"` for parameter estimates.
`digits`	Number of digits to round to.
`...`	Not used.

Author(s)

Sacha Epskamp <[email protected]>

Package 'lvnet'

Help Index

Latent variable graphical LASSO using EBIC to select optimal tuning parameter

Description

Usage

Arguments

Value

Author(s)

References

See Also

Update lvnatLasso results to select a different model

Description

Usage

Arguments

Author(s)

Examples

Convert lavaan model to lvnet model matrices

Description

Usage

Arguments

Value

Author(s)

Examples

Latent variable graphical LASSO

Description

Usage

Arguments

Value

Author(s)

References

Confirmatory Latent Variable Network Models

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Compare lvnet objects

Description

Usage

Arguments

Author(s)

See Also

LASSO model selection

Description

Usage

Arguments

Author(s)

Examples

Refit lvnet model to new data

Description

Usage

Arguments

Author(s)

Step-wise exploratory search for optimal fitting model

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Plot model matrices

Description

Usage

Arguments

Author(s)

Summary method for lvnet

Description

Usage

Arguments

Author(s)