lvnetLasso: LASSO model selection

In lvnet: Latent Variable Network Modeling

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, ...)

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 <[e-mail]>

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)

lvnet documentation built on June 21, 2019, 9:06 a.m.