# csem_arguments

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##### cSEMArguments

An alphabetical list of all arguments used by functions of the cSEM package including their description and defaults. Mainly used for internal purposes (parameter inheritance). To list all arguments and their defaults, use args_default(). To list all arguments and their possible choices, use args_default(.choices = TRUE).

Keywords
internal
##### Arguments
.alpha

An integer or a numeric vector of significance levels. Defaults to 0.05.

.approach_gcca

Character string. The Kettenring approach to use for GCCA. One of "SUMCORR", "MAXVAR", "SSQCORR", "MINVAR" or "GENVAR". Defaults to "SUMCORR".

.approach_2ndorder

Character string. Approach used for models containing second-order constructs. One of: "2stage", or "mixed". Defaults to "2stage".

Character string. Approach used to adjust the significance level to accommodate multiple testing. One of "none" or "bonferroni". Defaults to "none".

.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_mgd

Character string or a vector of character strings. Approach used for the multi-group comparison. One of: "all", "Klesel", "Chin", "Sarstedt", "Keil, "Nitzl", or "Henseler". Default to "all" in which case all approaches are computed (if possible). Note that the output will be quite long in this case.

.approach_nl

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

Character string or a vector of character strings. Approach used to adjust the p-value in multiple testing. See the methods argument of stats::p.adjust() for a list of choices and their description. Defaults to "none".

.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".

.args_used

A list of function argument names whose value was modified by the user.

.benchmark

Character string. The procedure to obtain benchmark predictions. One of "lm", "unit", "PLS-PM", "GSCA", "PCA", or "MAXVAR". Default to "lm".

.bias_corrected

Logical. Should the standard and the tStat confidence interval be bias-corrected using the bootstrapped bias estimate? If TRUE the confidence interval for some estimated parameter theta is centered at 2*theta - theta*_hat, where theta*_hat is the average over all .R bootstrap estimates of theta. Defaults to TRUE

.C

A (J x J) composite variance-covariance matrix.

.check_errors

Logical. Should the model to parse be checked for correctness in a sense that all necessary components to estimate the model are given? Defaults to TRUE.

.choices

Logical. Should candidate values for the arguments be returned? Defaults to FALSE.

.ci

A vector of character strings naming the confidence interval to compute. For possible choices see infer().

.closed_form_ci

Logical. Should a closed-form confidence interval be computed? Defaults to FALSE.

.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".

.csem_model

A (possibly incomplete) cSEMModel-list.

.csem_resample

A list resulting from a call to resamplecSEMResults().

.cv_folds

Integer. The number of cross-validation folds to use. Setting .cv_folds to N (the number of observations) produces leave-one-out cross-validation samples. Defaults to 10.

.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.

.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.

.dist

Character string. The distribution to use for the critical value. One of "t" for Student's t-distribution or "z" for the standard normal distribution. Defaults to "z".

.distance

Character string. A distance measure. One of: "geodesic" or "squared_euclidian". Defaults to "geodesic".

.df

Character string. The method for obtaining the degrees of freedom. Choices are "type1" and "type2". Defaults to "type1" .

.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.

.E

A (J x J) matrix of inner weights.

.estimate_structural

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

.eval_plan

Character string. The evaluation plan to use. One of "sequential" or "multiprocess". In the latter case all available cores will be used. Defaults to "sequential".

.first_resample

A list containing the .R resamples based on the original data obtained by resamplecSEMResults().

.full_output

Logical. Should the full output of summarize be printed. Defaults to TRUE.

.H

The (N x J) matrix of construct scores.

Character string. How should inadmissible results be treated? One of "drop", "ignore", or "replace". If "drop", all replications/resamples yielding an inadmissible result will be dropped (i.e. the number of results returned will potentially be less than .R). For "ignore" all results are returned even if all or some of the replications yielded inadmissible results (i.e. number of results returned is equal to .R). For "replace" resampling continues until there are exactly .R admissible solutions. Depending on the frequency of inadmissible solutions this may significantly increase computing time. Defaults to "drop".

.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.

.matrix1

A matrix to compare.

.matrix2

A matrix to compare.

.matrices

A list of at least two matrices.

.model

A model in lavaan model syntax or a cSEMModel list.

.model_implied

Logical. Should the RMS_theta be computed using the model-implied construct correlation matrix (TRUE) or the construct correlation matrix based on V(eta) = WSW' divided by the square root of the respective reliabilities (FALSE). Defaults to FALSE.

.modes

A vector giving the mode for each construct in the form "name" = "mode". Only used internally.

.n

Integer. The number of observations of the original data.

.n_spotlights

Integer. A numeric value giving the number of spotlights (= values of .z) between min(.z) and max(.z) to use. 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.

.nr_comparisons

Integer. The number of comparisons. Defaults to NULL.

.null_model

Logical. Should the degrees of freedom for the null model be computed? Defaults to FALSE.

.object

An R object of class cSEMResults resulting from a call to csem().

.only_common_factors

Logical. Should only concepts modeled as common factors be included when calculating one of the following quality critera: AVE, the Fornell-Larcker criterion, HTMT, and all reliability estimates. Defaults to TRUE.

.original_arguments

The list of arguments used within csem().

.P

A (J x J) construct variance-covariance matrix (possibly disattenuated).

.parameters_to_compare

A model in lavaan model syntax indicating which parameters (i.e, path (~), loadings (=~), weights (<~), or correlations (~~)) should be compared across groups. Defaults to NULL in which case all parameters of the originally specified model are compared.

.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.

.probs

A vector of probabilities.

.quality_criterion

Character string. A single character string or a vector of character strings naming the quality criterion to compute. See the Details section for a list of possible candidates. Defaults to "all" in which case all possible quality criteria are computed.

.quantity

Character string. Which statistic should be returned? One of "all", "mean", "sd", "bias", "CI_standard_z", "CI_standard_t", "CI_percentile", "CI_basic", "CI_bc", "CI_bca", "CI_t_interval" Defaults to "all" in which case all quantities that do not require additional resampling are returned, i.e., all quantities but "CI_bca", "CI_t_interval".

.Q

A vector of composite-construct correlations with element names equal to the names of the J construct names used in the measurement model. Note Q^2 is also called the reliability coefficient.

.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".

.resample_method

Character string. The resampling method to use. One of: "none", "bootstrap" or "jackknife". Defaults to "none".

.resample_method2

Character string. The resampling method to use when resampling from a resample. One of: "none", "bootstrap" or "jackknife". For "bootstrap" the number of draws is provided via .R2. Currently, resampling from each resample is only required for the studentized confidence intervall ("CI_t_interval") computed by the infer() function. Defaults to "none".

.resample_object

An R object of class cSEMResults_resampled obtained from resamplecSEMResults() or by setting .resample_method = "bootstrap" or "jackknife" when calling csem().

.resample_sarstedt

A matrix containing the parameter estimates that could potentially be compared and an id column indicating the group adherence of each row.

.r

Integer. The number of repetitions to use. Defaults to 10.

.R

Integer. The number of bootstrap replications. Defaults to 499.

.R2

Integer. The number of bootstrap replications to use when resampling from a resample. Defaults to 199.

.R_bootstrap

Integer. The number of bootstrap runs. Ignored if .object contains resamples. Defaults to 499

.R_permutation

Integer. The number of permutations. Defaults to 499

.S

The (K x K) empirical indicator correlation matrix.

.saturated

Logical. Should a saturated structural model be used? Defaults to FALSE.

.second_resample

A list containing .R2 resamples for each of the .R resamples of the first run.

.seed

Integer or NULL. The random seed to use. Defaults to NULL in which case an arbitrary seed is chosen. Note that the scope of the seed is limited to the body of the function it is used in. Hence, the global seed will not be altered!

.sign_change_option

Character string. Which sign change option should be used to handle flipping signs when resampling? One of "none","individual", "individual_reestimate", "construct_reestimate". Defaults to "none".

.stage

Character string. The stage the model is needed for. One of "first" or "second". Defaults to "first".

.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.

.terms

A vector of construct names to be classified.

.test_data

A matrix of test data with the same column names as the training data.

.tolerance

Double. The tolerance criterion for convergence. Defaults to 1e-05.

.type_vcv

Character string. Which model-implied correlation matrix is calculated? One of "indicator" or "construct". Defaults to "indicator".

.verbose

Logical. Should information (e.g., progress bar) be printed to the console? Defaults to TRUE.

.user_funs

A function or a (named) list of functions to apply to every resample. The functions must take .object as its first argument (e.g., myFun <- function(.object, ...) {body-of-the-function}). Function output should preferably be a (named) vector but matrices are also accepted. However, the output will be vectorized (columnwise) in this case. See the examples section for details.

.vcv_asymptotic

Logical. Should the asymptotic variance-covariance matrix be used, i.e., VCV(b0) - VCV(b1)= VCV(b1-b0), or should VCV(b1-b0) be computed directly? Defaults to FALSE.

.vector1

A vector of numeric values.

.vector2

A vector of numeric values.

.W

A (J x K) matrix of weights.

.W_new

A (J x K) matrix of weights.

.W_old

A (J x K) matrix of weights.

.weighted

Logical. Should estimation be based on a score that uses the weights of the weight approach used to obtain .object?. Defaults to FALSE.

.x

Character string. The name of the moderator variable. Defaults to NULL.

.X

A matrix of processed data (scaled, cleaned and ordered).

.X_cleaned

A data.frame of processed data (cleaned and ordered). Note: X_cleaned may not be scaled!

.y

Character string. The name of the dependent variable. Defaults to NULL.

.z

Character string. The name of the independent variable. Defaults to NULL.

##### Aliases
• csem_arguments
• cSEMArguments
Documentation reproduced from package cSEM, version 0.1.0, License: GPL-3

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