Calculate composite weights using the partial least squares path modeling (PLS-PM) algorithm (Wold 1975) .
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 )
data.frame or a
matrix of standardized or unstandardized
data (indicators/items/manifest variables). Possible column types or classes
of the data provided are: "
double" or "
ordered" and/or "
character" (converted to factor),
or a mix of several types.
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
Logical. Should the structural model be ignored
when calculating the inner weights of the PLS-PM algorithm? Defaults to
.approach_weights is not PLS-PM.
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
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 the appropriate mode according to the type
of construct used is chosen. Ignored if
.approach_weight is not PLS-PM.
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.
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 (J x J) matrix of inner weights.
A named vector of modes used for the outer estimation.
The convergence status.
TRUE if the algorithm has converged
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
The number of iterations required.
Wold H (1975). “Path models with latent variables: The NIPALS approach.” In Blalock HM, Aganbegian A, Borodkin FM, Boudon R, Capecchi V (eds.), Quantitative Sociology, International Perspectives on Mathematical and Statistical Modeling, 307--357. Academic Press, New York.