csem_arguments: cSEMArguments

In M-E-Steiner/cSEM: Composite-Based Structural Equation Modeling

cSEMArguments

Description

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).

Arguments

.alpha	An integer or a numeric vector of significance levels. Defaults to 0.05.
.absolute	Logical. Should the absolute HTMT values be returned? Defaults to TRUE .
.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".
.approach_alpha_adjust	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-Pearson 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", "Henseler", "CI_para", or "CI_overlap". Default to "all" in which case all approaches are computed (if possible).
.approach_nl	Character string. Approach used to estimate nonlinear structural relationships. One of: "sequential" or "replace". Defaults to "sequential".
.approach_predict	Character string. Which approach should be used to predictions? One of "earliest" and "direct". If "earliest" predictions for indicators associated to endogenous constructs are performed using only indicators associated to exogenous constructs. If "direct", predictions for indicators associated to endogenous constructs are based on indicators associated to their direct antecedents. Defaults to "earliest".
.approach_p_adjust	Character string or a vector of character strings. Approach used to adjust the p-value for 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_score_benchmark	Character string. How should the aggregation of the estimates of the truncated normal distribution be done for the benchmark predictions? Ignored if not OrdPLS or OrdPLSc is used to obtain benchmark predictions. One of "mean", "median", "mode" or "round". If "round", the benchmark predictions are obtained using the traditional prediction algorithm for PLS-PM which are rounded for categorical indicators. If "mean", the mean of the estimated endogenous indicators is calculated. If "median", the mean of the estimated endogenous indicators is calculated. If "mode", the maximum empirical density on the intervals defined by the thresholds is used. If .treat_as_continuous = TRUE or if all indicators are on a continuous scale, .approach_score_benchmark is ignored. Defaults to "round".
.approach_score_target	Character string. How should the aggregation of the estimates of the truncated normal distribution for the predictions using OrdPLS/OrdPLSc be done? One of "mean", "median" or "mode". If "mean", the mean of the estimated endogenous indicators is calculated. If "median", the mean of the estimated endogenous indicators is calculated. If "mode", the maximum empirical density on the intervals defined by the thresholds is used. Defaults to "mean".
.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.
.attrbutes	Character string. Variables used as attributes in IPMA.
.benchmark	Character string. The procedure to obtain benchmark predictions. One of "lm", "unit", "PLS-PM", "GSCA", "PCA", "MAXVAR", or "NA". 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
.by_equation	Should the criteria be computed for each structural model equation separately? 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().
.ci_colnames	Internal argument used by several print helper functions.
.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.
.dependent	Character string. The name of the dependent variable.
.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_euclidean". 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.
.effect	Internal argument used by helper printEffects().
.estimate_structural	Logical. Should the structural coefficients be estimated? Defaults to TRUE.
.eval_plan	Character string. The evaluation plan to use. One of "sequential", "multicore", or "multisession". In the two latter cases all available cores will be used. Defaults to "sequential".
.filename	Character string. The file name. Defaults to "results.xlsx".
.first_resample	A list containing the .R resamples based on the original data obtained by resamplecSEMResults().
.fit_measures	Logical. (EXPERIMENTAL) Should additional fit measures be included? Defaults to FALSE.
.force	Logical. Should .object be resampled even if it contains resamples already?. Defaults to FALSE.
.full_output	Logical. Should the full output of summarize be printed. Defaults to TRUE.
.H	The (N x J) matrix of construct scores.
.handle_inadmissibles	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.
.inference	Logical. Should critical values be computed? Defaults to FALSE.
.independent	Character string. The name of the independent variable.
.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.
.level	Character. Used in plot.cSEMIPMA to indicate whether IPMA should be done for constructs or indicators.
.matrix1	A matrix to compare.
.matrix2	A matrix to compare.
.matrices	A list of at least two matrices.
.metrics	Character string or a vector of character strings. Which prediction metrics should be displayed? One of: "MAE", "RMSE", "Q2", "MER", "MAPE, "MSE2", "U1", "U2", "UM", "UR", or "UD". Default to c("MAE", "RMSE", "Q2").
.model	A model in lavaan model syntax or a cSEMModel list.
.moderator	Character string. The name of the moderator variable.
.modes	A vector giving the mode for each construct in the form "name" = "mode". Only used internally.
.ms_criterion	Character string. Either a single character string or a vector of character strings naming the model selection criterion to compute. Defaults to "all".
.n	Integer. The number of observations of the original data.
.n_steps	Integer. A value giving the number of steps (the spotlights, i.e., values of .moderator in surface analysis or floodlight analysis) between the minimum and maximum value of the moderator. Defaults to 100.
.normality	Logical. Should joint normality of [\eta_{1:p}; \zeta; \epsilon] be assumed in the nonlinear model? See \insertCiteDijkstra2014cSEM 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().
.object1	An R object of class cSEMResults resulting from a call to csem().
.object2	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 criteria: AVE, the Fornell-Larcker criterion, HTMT, and all reliability estimates. Defaults to TRUE.
.only_structural	Should the the log-likelihood be based on the structural model? Ignored if .by_equation == TRUE. Defaults to TRUE.
.original_arguments	The list of arguments used within csem().
.output_type	Character string. The type of output to return. One of "complete" or "structured". See the Value section for details. Defaults to "complete".
.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 weights, loadings and path coefficients of the originally specified model are compared.
.path	Character string. Path of the directory to save the file to. Defaults to the current working directory.
.path_coefficients	List. A list that contains the resampled and the original path coefficient estimates. Typically a part of a cSEMResults_resampled object. Defaults to NULL.
.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.
.plot_package	Character string. Indicates which packages should be used for plotting.
.plot_type	Character string. Indicates the type of plot that is produced.
.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.
.postestimation_object	An object resulting from a call to one of cSEM's postestimation functions (e.g. summarize()).
.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 interval ("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 1.
.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".
.sim_points	Integer. How many samples from the truncated normal distribution should be simulated to estimate the exogenous construct scores? Defaults to "100".
.stage	Character string. The stage the model is needed for. One of "first" or "second". Defaults to "first".
.standardized	Logical. Should standardized scores be returned? Defaults to TRUE.
.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.
.steps_mod	A numeric vector. Steps used for the moderator variable in calculating the simple effects of an independent variable on the dependent variable. 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.
.testtype	Character string. One of "twosided" (H1: The models do not perform equally in predicting indicators belonging to endogenous constructs)" and onesided" (H1: Model 1 performs better in predicting indicators belonging
.tolerance	Double. The tolerance criterion for convergence. Defaults to 1e-05.
.treat_as_continuous	Logical. Should the indicators for the benchmark predictions be treated as continuous? If TRUE all indicators are treated as continuous and PLS-PM/PLSc is applied. If FALSE OrdPLS/OrdPLSc is applied. Defaults to TRUE.
.type_gfi	Character string. Which fitting function should the GFI be based on? One of "ML" for the maximum likelihood fitting function, "GLS" for the generalized least squares fitting function or "ULS" for the unweighted least squares fitting function (same as the squared Euclidean distance). Defaults to "ML".
.type_ci	Character string. Which confidence interval should be calculated? For possible choices, see the .quantity argument of the infer() function. Only used if .approch_mgd is one of "CI_para" or "CI_overlap". Ignored otherwise. Defaults to "CI_percentile".
.type_htmt	Character string indicating the type of HTMT that should be calculated, i.e., the original HTMT ("htmt") or the HTMT2 ("htmt2"). Defaults to "htmt"
.type_vcv	Character string. Which model-implied correlation matrix should be 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.
.value_independent	Integer. Only required for floodlight analysis; The value of the independent variable in case that it appears as a higher-order term.
.values_moderator	A numeric vector. The values of the moderator in a the simple effects analysis. Typically these are difference from the mean (=0) measured in standard deviations. Defaults to c(-2, -1, 0, 1, 2).
.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.
.what	Internal argument used by several print helper functions.
.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	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!

M-E-Steiner/cSEM documentation built on March 18, 2024, 12:18 p.m.