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()