calculateWeightsGSCAm {cSEM} | R Documentation |

Calculate composite weights using generalized structured component analysis with uniqueness terms (GSCAm) proposed by Hwang et al. (2017).

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: " |

`.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 Hwang et al. (2017).
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.

Hwang H, Takane Y, Jung K (2017).
“Generalized structured component analysis with uniqueness terms for accommodating measurement error.”
*Frontiers in Psychology*, **8**(2137), 1–12.

[Package *cSEM* version 0.4.0 Index]