Calculate composite weights using GSCA
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 = 1and
.approach_weights = "PLS-PM"one-step weights are returned. If the algorithm exceeds the specified number, weights of iteration step
.iter_max - 1will be returned with a warning. Defaults to
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" = valuepairs, where
valueis the (scaled or unscaled) starting weight. Defaults to
Double. The tolerance criterion for convergence. Defaults to
A named list. J stands for the number of constructs and K for the number of indicators.
A (J x K) matrix of estimated weights.
A named vector of Modes used for the outer estimation, for GSCA the mode is automatically set to "gsca".
The convergence status.
TRUEif the algorithm has converged and
The number of iterations required.