uSEM {pompom}

Fit a multivariate time series with uSEM (unified Structural Equation Model).

Description

Fit a multivariate time series with uSEM (unified Structural Equation Model).

Usage

uSEM(var.number,
     data,
     lag.order = 1,
     verbose = FALSE,
     trim = FALSE)

Arguments

var.number

number of variables in the time series

data

time series data, must be in long format

lag.order

lag order of the model to be fit, default value is 1. Note: Higher order (greater than 1) might not run.

verbose

print intermediate model fit (iterations), default value is FALSE

trim

to trim the insignificant betas (just one step, not iterative), default value is FALSE

Details

The purpose of uSEM is to quantify the temporal relations (both contemporaneous and lag-1) between variables. Model specification and estimation can be found in the references.

Value

model fit object generated by lavaan

References

Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified Structural Equation Modeling Approach for the Analysis of Multisubject, Multivariate Functional MRI Data. Human Brain Mapping, 93, 85–93. doi:10.1002/hbm.20259

Gates, K. M., & Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage 63(1), 310-319. doi: 10.1016/j.neuroimage.2012.06.026

Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010). Automatic search for fMRI connectivity mapping: An alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. NeuroImage, 50(3), 1118–1125. doi: 10.1016/j.neuroimage.2009.12.117

Examples

model.fit <- uSEM(var.number = 3,
                 data = simts_3node,
                 lag.order = 1,
                 verbose = FALSE,
                 trim = FALSE)
model.fit