foreman

0th

Percentile

Internal: Composite-based SEM

The central hub of the cSEM package. It acts like a foreman by collecting all (estimation) tasks, distributing them to lower level package functions, and eventually recollecting all of their results. It is called by csem() to manage the actual calculations. It may be called directly by the user, however, in most cases it will likely be more convenient to use csem() instead.

Keywords
internal
Usage
foreman(
  .data                        = args_default()$.data,
  .model                       = args_default()$.model,
  .approach_cor_robust         = args_default()$.approach_cor_robust,
  .approach_nl                 = args_default()$.approach_nl,
  .approach_paths              = args_default()$.approach_paths,
  .approach_weights            = args_default()$.approach_weights,
  .conv_criterion              = args_default()$.conv_criterion,
  .disattenuate                = args_default()$.disattenuate,
  .dominant_indicators         = args_default()$.dominant_indicators,
  .estimate_structural         = args_default()$.estimate_structural,
  .id                          = args_default()$.id,
  .instruments                 = args_default()$.instruments,
  .iter_max                    = args_default()$.iter_max,
  .normality                   = args_default()$.normality,
  .PLS_approach_cf             = args_default()$.PLS_approach_cf,
  .PLS_ignore_structural_model = args_default()$.PLS_ignore_structural_model,
  .PLS_modes                   = args_default()$.PLS_modes,
  .PLS_weight_scheme_inner     = args_default()$.PLS_weight_scheme_inner,
  .reliabilities               = args_default()$.reliabilities,
  .starting_values             = args_default()$.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.

.model

A model in lavaan model syntax or a cSEMModel list.

.approach_cor_robust

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.

.approach_nl

Character string. Approach used to estimate nonlinear structural relationships. One of: "sequential" or "replace". Defaults to "sequential".

.approach_paths

Character string. Approach used to estimate the structural coefficients. One of: "OLS" or "2SLS". If "2SLS", instruments need to be supplied to .instruments. Defaults to "OLS".

.approach_weights

Character string. Approach used to obtain composite weights. One of: "PLS-PM", "SUMCORR", "MAXVAR", "SSQCORR", "MINVAR", "GENVAR", "GSCA", "PCA", "unit", "bartlett", or "regression". Defaults to "PLS-PM".

.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".

.disattenuate

Logical. Should composite/proxy correlations be disattenuated to yield consistent loadings and path estimates if at least one of the construct is modeled as a common factor? Defaults to TRUE.

.dominant_indicators

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.

.estimate_structural

Logical. Should the structural coefficients be estimated? Defaults to TRUE.

.id

Character string or integer. A character string giving the name or an integer of the position of the column of .data whose levels are used to split .data into groups. Defaults to NULL.

.instruments

A named list of vectors of instruments. The names of the list elements are the names of the dependent (LHS) constructs of the structural equation whose explanatory variables are endogenous. The vectors contain the names of the instruments corresponding to each equation. Note that exogenous variables of a given equation must be supplied as instruments for themselves. Defaults to NULL.

.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.

.normality

Logical. Should joint normality of \([\eta_{1:p}; \zeta; \epsilon]\) be assumed in the nonlinear model? See Dijkstra2014cSEM for details. Defaults to FALSE. Ignored if the model is not nonlinear.

.PLS_approach_cf

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.

.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.

.reliabilities

A character vector of "name" = value pairs, where value is a number between 0 and 1 and "name" a character string of the corresponding construct name, or NULL. Reliabilities may be given for a subset of the constructs. Defaults to NULL in which case reliabilities are estimated by csem(). Currently, only supported for .approach_weights = "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.

See Also

csem, cSEMResults

Aliases
  • foreman
Documentation reproduced from package cSEM, version 0.1.0, License: GPL-3

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