ml_tsdlvm1: Multi-level Lag-1 dynamic latent variable model family of...

In SachaEpskamp/psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Multi-level Lag-1 dynamic latent variable model family of psychonetrics models for time-series data

Description

This function is a wrapper around dlvm1 that allows for specifying the model using a long format data and similar input as the mlVAR package. The ml_ts_lvgvar simply sets within_latent = "ggm" and between_latent = "ggm" by default. The ml_gvar and ml_var are simple wrappers with different named defaults for contemporaneous and between-person effects.

Usage

ml_tsdlvm1(data, beepvar, idvar, vars, groups, estimator = "FIML", 
  standardize = c("none", "z", "quantile"), ...)

ml_ts_lvgvar(...)

ml_gvar(..., contemporaneous = c("ggm", "cov", "chol", "prec"), 
        between = c("ggm", "cov", "chol", "prec"))
             
ml_var(..., contemporaneous = c("cov", "chol", "prec", "ggm"), 
        between = c("cov", "chol", "prec", "ggm"))

Arguments

data	The data to be used. Must be raw data in long format (each row indicates one person at one time point).
beepvar	Optional string indicating assessment beep per day. Adding this argument will cause non-consecutive beeps to be treated as missing!
idvar	String indicating the subject ID
vars	Vectors of variables to include in the analysis
groups	An optional string indicating the name of the group variable in data.
estimator	Estimator to be used. Must be "FIML".
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.
contemporaneous	The type of within-person latent contemporaneous model to be used.
between	The type of between-person latent model to be used.
...	Arguments sent to dlvm1

Author(s)

Sacha Epskamp <[e-mail]>

SachaEpskamp/psychonetrics documentation built on Sept. 1, 2023, 3:40 a.m.