Group Iterative Multiple Model Estimation
Group-level structural equation model search.
Create tree structures for group search solutions.
Counts number of excellent fit indices
Create structure of group search solutions.
Determines subgroups.
Provides unique combinations of two vectors.
Wrapup, create output files.
Attempt to fit lavaan model.
Grabs final coefficients for each individual.
Group iterative multiple model estimation
Write MS-GIMME results to data.frame.
Group iterative multiple model estimation.
Identifies highest MI from list of MIs.
Individual-level search. Used in gimmeSEM, aggSEM, indSEM.
Individual-level search. Used in gimmeSEM, aggSEM, indSEM.
Individual-level structural equation model search.
Identifies lowest z value from list of z values.
GIMME Predicted Values.
Prunes paths. Ties together lowest.z and return.zs functions.
Recode variable names.
GIMME Residuals.
Returns MIs from lavaan fit object.
Returns z values from lavaan fit object.
Searches for paths. Ties together highest.mi and return.mis functions.
Searches for paths. Ties together highest.mi and return.mis functions.
Set up base syntax file.
Group iterative multiple model estimation.
Create a list of dataframes
Get names for bilinear effects.
Do some preliminary checks on the data.
Allows user to open and close certain paths.
Transform raw data as required.
Estimate response function for each person using smoothed Finite Impul...
Simulate data from Vector AutoRegression (VAR) models.
Solution trees for multiple solutions gimme.
Create structure of group search solutions.
Create summary matrix of path counts and subgroup plots
Create edge list from weight matrix.
Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Useful links