psychonetrics (version 0.13)

latentgrowth: Latnet growth curve model

Description

Wrapper to lvm to specify a latent growth curve model.

Usage

latentgrowth(vars, time = seq_len(ncol(vars)) - 1, covariates =
                   character(0), covariates_as = c("regression",
                   "covariance"), ...)

Value

An object of the class psychonetrics (psychonetrics-class). See for an example https://github.com/SachaEpskamp/SEM-code-examples/tree/master/Latent_growth_examples/psychonetrics.

Arguments

vars

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.

time

A vector with the encoding of each measurement (e.g., 0, 1, 2, 3).

covariates

A vector with strings indicating names of between-person covariate variables in the data

covariates_as

Should covariates be included as regressions or actual covariates?

...

Arguments sent to lvm

Author

Sacha Epskamp

Details

See https://github.com/SachaEpskamp/SEM-code-examples/tree/master/Latent_growth_examples/psychonetrics for examples

Examples

library("dplyr")

# Smoke data cov matrix, based on LISS data panel https://www.dataarchive.lissdata.nl
smoke <- structure(c(47.2361758611759, 43.5366809116809, 41.0057465682466, 
                     43.5366809116809, 57.9789886039886, 47.6992521367521, 
                     41.0057465682466, 
                     47.6992521367521, 53.0669434731935), .Dim = c(3L, 3L), 
                   .Dimnames = list(
                       c("smoke2008", "smoke2009", "smoke2010"), c("smoke2008", 
                   "smoke2009", "smoke2010")))

# Design matrix:
design <- matrix(rownames(smoke),1,3)

# Form model:
mod <- latentgrowth(vars = design, 
                covs = smoke, nobs = 352
)

if (FALSE) {
# Run model:
mod <- mod %>% runmodel

# Evaluate fit:
mod %>% fit

# Look at parameters:
mod %>% parameters
}