ri_clpm: Random intercept cross-lagged panel models

In psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Random intercept cross-lagged panel models

Description

Function to run random intercept cross-lagged panel models under the lvm framework.

Usage

ri_clpm(data, vars, lambda, 
        type = c("cov","chol","prec","ggm"), 
        verbose = FALSE, ...)
ri_clpm_stationary(x, 
                   stationary = c("intercepts", 
                                  "contemporaneous", 
                                  "innovation", 
                                  "temporal"))

Arguments

x	A psychonetrics model.
stationary	The part of the model to implement stationarity on.
data	A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.
vars	Required argument. Different from in other psychonetrics models, this must be a *matrix* with each row indicating a variable and each column indicating a measurement. The matrix must be filled with names of the variables in the dataset corresponding to variable i at wave j. NAs can be used to indicate missing waves. The rownames of this matrix will be used as variable names.
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.
type	The type of model to model innovation
verbose	Logical, should progress be printed to the console?
...	Arguments sent to lvm

Value

A single psychonetrics object

Author(s)

Sacha Epskamp

Examples

# to be made

psychonetrics documentation built on June 8, 2025, 1:08 p.m.