Multi-level latent variable model family
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 simplestructure.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 "zero".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 "zero".omega_zeta_within: Only used when within_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.delta_zeta_within: Only used when within_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.kappa_zeta_within: Only used when within_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.sigma_zeta_within: Only used when within_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.lowertri_zeta_within: Only used when within_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.omega_epsilon_within: Only used when within_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.delta_epsilon_within: Only used when within_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.kappa_epsilon_within: Only used when within_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.sigma_epsilon_within: Only used when within_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.lowertri_epsilon_within: Only used when within_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.omega_zeta_between: Only used when between_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.delta_zeta_between: Only used when between_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.kappa_zeta_between: Only used when between_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.sigma_zeta_between: Only used when between_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.lowertri_zeta_between: Only used when between_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.omega_epsilon_between: Only used when between_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.delta_epsilon_between: Only used when between_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.kappa_epsilon_between: Only used when between_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.sigma_epsilon_between: Only used when between_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.lowertri_epsilon_between: Only used when between_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.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. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.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 data.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 "FIML" is supported.optimizer: The optimizer to be used. Usually either "nlminb" (with box constrains) or "ucminf" (ignoring box constrains), but any optimizer supported by optimr can be used.storedata: Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).verbose: Logical, should progress be printed to the console?standardize: Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.sampleStats: An optional sample statistics object. Mostly used internally.bootstrap: Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).boot_sub: Proportion of cases to be subsampled (round(boot_sub * N)).boot_resample: Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)...: Arguments sent to 'ml_lvm'An object of the class psychonetrics (psychonetrics-class )
Sacha Epskamp mail@sachaepskamp.com