ml_tsdlvm1: 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

Sacha Epskamp <mail@sachaepskamp.com>