A complete alphabetical list of all possible arguments accepted by csem()'s ...
(dotdotdot) argument.
Character string. Approach used to obtain a robust
indicator correlation matrix. One of: "none" in which case the standard
Bravais-Person correlation is used,
"spearman" for the Spearman rank correlation, or
"mcd" via MASS::cov.rob() for a robust correlation matrix.
Defaults to "none". Note that many postestimation procedures (such as
testOMF() or fit() implicitly assume a continuous
indicator correlation matrix (e.g. Bravais-Pearson correlation matrix).
Only use if you know what you are doing.
Character string. The criterion to use for the convergence check. One of: "diff_absolute", "diff_squared", or "diff_relative". Defaults to "diff_absolute".
A character vector of "construct_name" = "indicator_name" pairs,
where "indicator_name" is a character string giving the name of the dominant indicator
and "construct_name" a character string of the corresponding construct name.
Dominant indicators may be specified for a subset of the constructs.
Default to NULL.
Logical. Should the structural coefficients
be estimated? Defaults to TRUE.
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.
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.
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.
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.
Character string. Approach used to obtain the correction
factors for PLSc. One of: "dist_squared_euclid", "dist_euclid_weighted",
"fisher_transformed", "mean_arithmetic", "mean_geometric", "mean_harmonic",
"geo_of_harmonic". Defaults to "dist_squared_euclid".
Ignored if .disattenuate = FALSE or if .approach_weights is not PLS-PM.
Double. The tolerance criterion for convergence.
Defaults to 1e-05.
Most arguments supplied to the ... argument of csem() are only
accepted by a subset of the functions called by csem(). The following
list shows which argument is passed to which (internal) function:
Accepted by/Passed down to: calculateIndicatorCor()
Accepted by/Passed down to: calculateWeightsPLS(),
calculateWeightsGSCA(), calculateWeightsGSCAm() and subsequently
checkConvergence().
Accepted by/Passed down to: setDominantIndicator()
Accepted by/Passed down to: foreman()
Accepted by/Passed down to: calculateWeightsPLS(),
calculateWeightsGSCA(), calculateWeightsGSCAm()
Accepted by/Passed down to: calculateWeightsPLS()
Accepted by/Passed down to: calculateWeightsPLS(),
calculateWeightsGSCA(), calculateWeightsGSCAm(), calculateWeightsUnit()