# csem_arguments

##### 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*".- .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-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*".- .approach_p_adjust
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.

- .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`

.- .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`

.

*Documentation reproduced from package cSEM, version 0.1.0, License: GPL-3*