View source: R/a_models_ml_lvm.R

ml_lvm | R Documentation |

This family is the two-level random intercept variant of the `lvm`

model family. It is mostly a special case of the `dlvm1`

family, with the addition of structural effects rather than temporal effects in the `beta`

matrix.

```
ml_lnm(...)
ml_rnm(...)
ml_lrnm(...)
ml_lvm(data, lambda, clusters, within_latent = c("cov",
"chol", "prec", "ggm"), within_residual = c("cov",
"chol", "prec", "ggm"), between_latent = c("cov",
"chol", "prec", "ggm"), between_residual = c("cov",
"chol", "prec", "ggm"), beta_within = "zero",
beta_between = "zero", omega_zeta_within = "full",
delta_zeta_within = "full", kappa_zeta_within =
"full", sigma_zeta_within = "full",
lowertri_zeta_within = "full", omega_epsilon_within =
"zero", delta_epsilon_within = "diag",
kappa_epsilon_within = "diag", sigma_epsilon_within =
"diag", lowertri_epsilon_within = "diag",
omega_zeta_between = "full", delta_zeta_between =
"full", kappa_zeta_between = "full",
sigma_zeta_between = "full", lowertri_zeta_between =
"full", omega_epsilon_between = "zero",
delta_epsilon_between = "diag", kappa_epsilon_between
= "diag", sigma_epsilon_between = "diag",
lowertri_epsilon_between = "diag", nu, nu_eta,
identify = TRUE, identification = c("loadings",
"variance"), vars, latents, groups, equal = "none",
baseline_saturated = TRUE, estimator = c("FIML",
"MUML"), optimizer, storedata = FALSE, verbose =
FALSE, standardize = c("none", "z", "quantile"),
sampleStats, bootstrap = FALSE, boot_sub,
boot_resample)
```

`data` |
A data frame encoding the data used in the analysis. Must be a raw dataset. |

`lambda` |
A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Could also be the result of |

`clusters` |
A string indicating the variable in the dataset that describes group membership. |

`within_latent` |
The type of within-person latent contemporaneous model to be used. |

`within_residual` |
The type of within-person residual model to be used. |

`between_latent` |
The type of between-person latent model to be used. |

`between_residual` |
The type of between-person residual model to be used. |

`beta_within` |
A model matrix encoding the within-cluster structural. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Defaults to |

`beta_between` |
A model matrix encoding the between-cluster structural. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Defaults to |

`omega_zeta_within` |
Only used when |

`delta_zeta_within` |
Only used when |

`kappa_zeta_within` |
Only used when |

`sigma_zeta_within` |
Only used when |

`lowertri_zeta_within` |
Only used when |

`omega_epsilon_within` |
Only used when |

`delta_epsilon_within` |
Only used when |

`kappa_epsilon_within` |
Only used when |

`sigma_epsilon_within` |
Only used when |

`lowertri_epsilon_within` |
Only used when |

`omega_zeta_between` |
Only used when |

`delta_zeta_between` |
Only used when |

`kappa_zeta_between` |
Only used when |

`sigma_zeta_between` |
Only used when |

`lowertri_zeta_between` |
Only used when |

`omega_epsilon_between` |
Only used when |

`delta_epsilon_between` |
Only used when |

`kappa_epsilon_between` |
Only used when |

`sigma_epsilon_between` |
Only used when |

`lowertri_epsilon_between` |
Only used when |

`nu` |
Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector. |

`nu_eta` |
Optional vector encoding the intercepts of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector. |

`identify` |
Logical, should the model be automatically identified? |

`identification` |
Type of identification used. |

`vars` |
An optional character vector with names of the variables used. |

`latents` |
An optional character vector with names of the latent variables. |

`groups` |
An optional string indicating the name of the group variable in |

`equal` |
A character vector indicating which matrices should be constrained equal across groups. |

`baseline_saturated` |
A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually. |

`estimator` |
Estimator used. Currently only |

`optimizer` |
The optimizer to be used. Usually either |

`storedata` |
Logical, should the raw data be stored? Needed for bootstrapping (see |

`verbose` |
Logical, should progress be printed to the console? |

`standardize` |
Which standardization method should be used? |

`sampleStats` |
An optional sample statistics object. Mostly used internally. |

`bootstrap` |
Should the data be bootstrapped? If |

`boot_sub` |
Proportion of cases to be subsampled ( |

`boot_resample` |
Logical, should the bootstrap be with replacement ( |

`...` |
Arguments sent to 'ml_lvm' |

An object of the class psychonetrics (psychonetrics-class)

Sacha Epskamp <mail@sachaepskamp.com>

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