Function to run random intercept cross-lagged panel models under the lvm framework.
ri_clpm(data, vars, lambda,
type = c("cov","chol","prec","ggm"),
verbose = FALSE, ...)
ri_clpm_stationary(x,
stationary = c("intercepts",
"contemporaneous",
"innovation",
"temporal"))A single psychonetrics object
A psychonetrics model.
The part of the model to implement stationarity on.
A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.
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.
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.
The type of model to model innovation
Logical, should progress be printed to the console?
Arguments sent to lvm
Sacha Epskamp