Calculate composite weights using generalized structure component analysis (GSCA). The first version of this approach was presented in Hwang2004;textualcSEM. Since then, several advancements have been proposed. The latest version of GSCA can been found in Hwang2014;textualcSEM. This is the version cSEMs implementation is based on.
calculateWeightsGSCA(
.X = args_default()$.X,
.S = args_default()$.S,
.csem_model = args_default()$.csem_model,
.conv_criterion = args_default()$.conv_criterion,
.iter_max = args_default()$.iter_max,
.starting_values = args_default()$.starting_values,
.tolerance = args_default()$.tolerance
)A matrix of processed data (scaled, cleaned and ordered).
The (K x K) empirical indicator correlation matrix.
A (possibly incomplete) cSEMModel-list.
Character string. The criterion to use for the convergence check. One of: "diff_absolute", "diff_squared", or "diff_relative". Defaults to "diff_absolute".
Integer. The maximum number of iterations allowed.
If iter_max = 1 and .approach_weights = "PLS-PM" one-step weights are returned.
If the algorithm exceeds the specified number, weights of iteration step
.iter_max - 1 will be returned with a warning. Defaults to 100.
A named list of vectors where the
list names are the construct names whose indicator weights the user
wishes to set. The vectors must be named vectors of "indicator_name" = value
pairs, where value is the (scaled or unscaled) starting weight. Defaults to NULL.
Double. The tolerance criterion for convergence.
Defaults to 1e-05.
A named list. J stands for the number of constructs and K for the number of indicators.
$WA (J x K) matrix of estimated weights.
$ENULL
$ModesA named vector of Modes used for the outer estimation, for GSCA the mode is automatically set to "gsca".
$Conv_statusThe convergence status. TRUE if the algorithm has converged
and FALSE otherwise.
$IterationsThe number of iterations required.