Calculate composite weights using generalized structure component analysis (GSCA). The first version of this approach was presented in Hwang and Takane (2004) . Since then, several advancements have been proposed. The latest version of GSCA can been found in Hwang and Takane (2014) . This is the version cSEMs implementation is based on.
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.
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
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
value is the (scaled or unscaled) starting weight. Defaults to
Double. The tolerance criterion for convergence.
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.
TRUE if the algorithm has converged
The number of iterations required.
Hwang H, Takane Y (2004).
“Generalized Structured Component Analysis.”
Psychometrika, 69(1), 81--99.
Hwang H, Takane Y (2014). Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling, Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences. Chapman and Hall/CRC.