calculateWeightsPLS: Calculate composite weights using PLS-PM
In cSEM: Composite-Based Structural Equation Modeling

calculateWeightsPLS

R Documentation

Calculate composite weights using PLS-PM

Description

Calculate composite weights using the partial least squares path modeling (PLS-PM) algorithm \insertCiteWold1975cSEM.

Usage

calculateWeightsPLS(
  .data                        = args_default()$.data,
  .S                           = args_default()$.S,
  .csem_model                  = args_default()$.csem_model,
  .conv_criterion              = args_default()$.conv_criterion,
  .iter_max                    = args_default()$.iter_max,
  .PLS_ignore_structural_model = args_default()$.PLS_ignore_structural_model,
  .PLS_modes                   = args_default()$.PLS_modes,
  .PLS_weight_scheme_inner     = args_default()$.PLS_weight_scheme_inner,
  .starting_values             = args_default()$.starting_values,
  .tolerance                   = args_default()$.tolerance
   )

Arguments

`.data`	A `data.frame` or a `matrix` of standardized or unstandardized data (indicators/items/manifest variables). Possible column types or classes of the data provided are: "`logical`", "`numeric`" ("`double`" or "`integer`"), "`factor`" ("`ordered`" and/or "`unordered`"), "`character`" (converted to factor), or a mix of several types.
`.S`	The (K x K) empirical indicator correlation matrix.
`.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 `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`.
`.PLS_ignore_structural_model`	Logical. Should the structural model be ignored when calculating the inner weights of the PLS-PM algorithm? Defaults to `FALSE`. Ignored if `.approach_weights` is not PLS-PM.
`.PLS_modes`	Either a named list specifying the mode that should be used for each construct in the form `"construct_name" = mode`, a single character string giving the mode that should be used for all constructs, or `NULL`. Possible choices for `mode` are: "modeA", "modeB", "modeBNNLS", "unit", "PCA", a single integer or a vector of fixed weights of the same length as there are indicators for the construct given by `"construct_name"`. If only a single number is provided this is identical to using unit weights, as weights are rescaled such that the related composite has unit variance. Defaults to `NULL`. If `NULL` the appropriate mode according to the type of construct used is chosen. Ignored if `.approach_weight` is not PLS-PM.
`.PLS_weight_scheme_inner`	Character string. The inner weighting scheme used by PLS-PM. One of: "centroid", "factorial", or "path". Defaults to "path". Ignored if `.approach_weight` is not PLS-PM.
`.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 `"indicator_name" = value` pairs, where `value` is the (scaled or unscaled) starting weight. Defaults to `NULL`.
`.tolerance`	Double. The tolerance criterion for convergence. Defaults to `1e-05`.

Value

A named list. J stands for the number of constructs and K for the number of indicators.

$W: A (J x K) matrix of estimated weights.
$E: A (J x J) matrix of inner weights.
$Modes: A named vector of modes used for the outer estimation.
$Conv_status: The convergence status. TRUE if the algorithm has converged and FALSE otherwise. If one-step weights are used via .iter_max = 1 or a non-iterative procedure was used, the convergence status is set to NULL.
$Iterations: The number of iterations required.