Description Usage Arguments Author(s) Examples

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.

1 2 3 4 |

`data` |
The data argument as used in |

`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 |

Sacha Epskamp <mail@sachaepskamp.com>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# 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)
``` |

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