View source: R/estimators_weights.R
calculateWeightsGSCAm | R Documentation |
Calculate composite weights using generalized structured component analysis with uniqueness terms (GSCAm) proposed by \insertCiteHwang2017;textualcSEM.
calculateWeightsGSCAm( .X = args_default()$.X, .csem_model = args_default()$.csem_model, .conv_criterion = args_default()$.conv_criterion, .iter_max = args_default()$.iter_max, .starting_values = args_default()$.starting_values, .tolerance = args_default()$.tolerance )
.X |
A matrix of processed data (scaled, cleaned and ordered). |
.csem_model |
A (possibly incomplete) cSEMModel-list. |
.conv_criterion |
Character string. The criterion to use for the convergence check. One of: "diff_absolute", "diff_squared", or "diff_relative". Defaults to "diff_absolute". |
.iter_max |
Integer. The maximum number of iterations allowed.
If |
.starting_values |
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 |
.tolerance |
Double. The tolerance criterion for convergence.
Defaults to |
If there are only constructs modeled as common factors
calling csem()
with .appraoch_weights = "GSCA"
will automatically call
calculateWeightsGSCAm()
unless .disattenuate = FALSE
.
GSCAm currently only works for pure common factor models. The reason is that the implementation
in cSEM is based on (the appendix) of \insertCiteHwang2017;textualcSEM.
Following the appendix, GSCAm fails if there is at least one construct
modeled as a composite because calculating weight estimates with GSCAm leads to a product
involving the measurement matrix. This matrix does not have full rank
if a construct modeled as a composite is present.
The reason is that the measurement matrix has a zero row for every construct
which is a pure composite (i.e. all related loadings are zero)
and, therefore, leads to a non-invertible matrix when multiplying it with its transposed.
A list with the elements
$W
A (J x K) matrix of estimated weights.
$C
The (J x K) matrix of estimated loadings.
$B
The (J x J) matrix of estimated path coefficients.
$E
NULL
$Modes
A named vector of Modes used for the outer estimation, for GSCA the mode is automatically set to 'gsca'.
$Conv_status
The convergence status. TRUE
if the algorithm has converged
and FALSE
otherwise.
$Iterations
The number of iterations required.
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