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).
An integer or a numeric vector of significance levels.
Defaults to 0.05.
Logical. Should the absolute HTMT values be returned?
Defaults to TRUE .
Character string. The Kettenring approach to use for GCCA. One of "SUMCORR", "MAXVAR", "SSQCORR", "MINVAR" or "GENVAR". Defaults to "SUMCORR".
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".
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.
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).
Character string. Approach used to estimate nonlinear structural relationships. One of: "sequential" or "replace". Defaults to "sequential".
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".
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".
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".
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".
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".
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".
A list of function argument names whose value was modified by the user.
Character string. Variables used as attributes in IPMA.
Character string. The procedure to obtain benchmark predictions. One of "lm", "unit", "PLS-PM", "GSCA", "PCA", "MAXVAR", or "NA". Default to "lm".
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
Should the criteria be computed for each structural model
equation separately? Defaults to TRUE.
A (J x J) composite variance-covariance matrix.
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.
Logical. Should candidate values for the arguments be returned?
Defaults to FALSE.
A vector of character strings naming the confidence interval to compute.
For possible choices see infer().
Internal argument used by several print helper functions.
Logical. Should a closed-form confidence interval be computed?
Defaults to FALSE.
Character string. The criterion to use for the convergence check. One of: "diff_absolute", "diff_squared", or "diff_relative". Defaults to "diff_absolute".
A (possibly incomplete) cSEMModel-list.
A list resulting from a call to resamplecSEMResults().
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.
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.
Character string. The name of the dependent variable.
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.
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".
Character string. A distance measure. One of: "geodesic" or "squared_euclidean". Defaults to "geodesic".
Character string. The method for obtaining the degrees of freedom. Choices are "type1" and "type2". Defaults to "type1" .
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.
A (J x J) matrix of inner weights.
Internal argument used by helper printEffects().
Logical. Should the structural coefficients
be estimated? Defaults to TRUE.
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".
Character string. The file name. Defaults to "results.xlsx".
A list containing the .R resamples based on the original
data obtained by resamplecSEMResults().
Logical. (EXPERIMENTAL) Should additional fit measures
be included? Defaults to FALSE.
Logical. Should .object be resampled even if it contains resamples
already?. Defaults to FALSE.
Logical. Should the full output of summarize be printed.
Defaults to TRUE.
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".
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.
Logical. Should critical values be computed? Defaults to FALSE.
Character string. The name of the independent variable.
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.
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.
Character. Used in plot.cSEMIPMA to indicate whether IPMA should be done
for constructs or indicators.
A matrix to compare.
A matrix to compare.
A list of at least two matrices.
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").
A model in lavaan model syntax or a cSEMModel list.
Character string. The name of the moderator variable.
A vector giving the mode for each construct in the form "name" = "mode".
Only used internally.
Character string. Either a single character string or a vector
of character strings naming the model selection criterion to compute.
Defaults to "all".
Integer. The number of observations of the original data.
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.
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.
Integer. The number of comparisons. Defaults to NULL.
Logical. Should the degrees of freedom for the null model
be computed? Defaults to FALSE.
An R object of class cSEMResults resulting from a call to csem().
An R object of class cSEMResults resulting from a call to csem().
An R object of class cSEMResults resulting from a call to csem().
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.
Should the the log-likelihood be based on the
structural model? Ignored if .by_equation == TRUE. Defaults to TRUE.
The list of arguments used within csem().
Character string. The type of output to return. One of "complete" or "structured". See the Value section for details. Defaults to "complete".
A (J x J) construct variance-covariance matrix (possibly disattenuated).
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.
Character string. Path of the directory to save the file to. Defaults to the current working directory.
List. A list that contains the resampled and the original
path coefficient estimates. Typically a part of a cSEMResults_resampled object.
Defaults to NULL.
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.
Character string. Indicates which packages should be used for plotting.
Character string. Indicates the type of plot that is produced.
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.
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.
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.
A vector of probabilities.
An object resulting from a call to one of cSEM's
postestimation functions (e.g. summarize()).
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.
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".
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.
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".
Character string. The resampling method to use. One of: "none", "bootstrap" or "jackknife". Defaults to "none".
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".
An R object of class cSEMResults_resampled
obtained from resamplecSEMResults() or by setting .resample_method = "bootstrap"
or "jackknife" when calling csem().
A matrix containing the parameter estimates that could potentially be compared and an id column indicating the group adherence of each row.
Integer. The number of repetitions to use. Defaults to 1.
Integer. The number of bootstrap replications. Defaults to 499.
Integer. The number of bootstrap replications to use when
resampling from a resample. Defaults to 199.
Integer. The number of bootstrap runs. Ignored if .object
contains resamples. Defaults to 499
Integer. The number of permutations. Defaults to 499
The (K x K) empirical indicator correlation matrix.
Logical. Should a saturated structural model be used?
Defaults to FALSE.
A list containing .R2 resamples for each of the .R
resamples of the first run.
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!
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".
Integer. How many samples from the truncated normal distribution should be simulated to estimate the exogenous construct scores? Defaults to "100".
Character string. The stage the model is needed for. One of "first" or "second". Defaults to "first".
Logical. Should standardized scores be returned? Defaults
to TRUE.
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.
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.
A vector of construct names to be classified.
A matrix of test data with the same column names as the training data.
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
Double. The tolerance criterion for convergence.
Defaults to 1e-05.
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.
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".
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".
Character string indicating the type of HTMT that should be calculated, i.e., the original HTMT ("htmt") or the HTMT2 ("htmt2"). Defaults to "htmt"
Character string. Which model-implied correlation matrix should be calculated? One of "indicator" or "construct". Defaults to "indicator".
Logical. Should information (e.g., progress bar) be printed
to the console? Defaults to TRUE.
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.
Integer. Only required for floodlight analysis; The value of the independent variable in case that it appears as a higher-order term.
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).
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.
A vector of numeric values.
A vector of numeric values.
A (J x K) matrix of weights.
Internal argument used by several print helper functions.
A (J x K) matrix of weights.
A (J x K) matrix of weights.
Logical. Should estimation be based on a score that uses
the weights of the weight approach used to obtain .object?. Defaults to FALSE.
A matrix of processed data (scaled, cleaned and ordered).
A data.frame of processed data (cleaned and ordered). Note: X_cleaned
may not be scaled!