R/postestimate_assess.R
In M-E-Steiner/cSEM: Composite-Based Structural Equation Modeling
Defines functions
assess
Documented in
assess
#' Assess model
#'
#' \lifecycle{maturing}
#'
#' Assess a model using common quality criteria.
#' See the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website} for details.
#'
#' The function is essentially a wrapper around a number of internal functions
#' that perform an "assessment task" (called a **quality criterion** in \pkg{cSEM}
#' parlance) like computing reliability estimates,
#' the effect size (Cohen's f^2), the heterotrait-monotrait ratio of correlations (HTMT) etc.
#'
#' By default every possible quality criterion is calculated (`.quality_criterion = "all"`).
#' If only a subset of quality criteria are needed a single character string
#' or a vector of character strings naming the criteria to be computed may be
#' supplied to [assess()] via the `.quality_criterion` argument. Currently, the
#' following quality criteria are implemented (in alphabetical order):
#'
#' \describe{
#' \item{Average variance extracted (AVE); "ave"}{An estimate of the
#' amount of variation in the indicators that is due to the underlying latent variable.
#' Practically, it is calculated as the ratio of the (indicator) true score variances
#' (i.e., the sum of the squared loadings)
#' relative to the sum of the total indicator variances. The AVE is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret AVE results for constructs modeled as composites.
#' It is possible to report the AVE for constructs modeled as composites by setting
#' `.only_common_factors = FALSE`, however, result should be interpreted with caution
#' as they may not have a conceptual meaning. Calculation is done
#' by [calculateAVE()].}
#' \item{Congeneric reliability; "rho_C", "rho_C_mm", "rho_C_weighted", "rho_C_weighted_mm"}{
#' An estimate of the reliability assuming a congeneric measurement model (i.e., loadings are
#' allowed to differ) and a test score (proxy) based on unit weights.
#' There are four different versions implemented. See the
#' \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae} section
#' of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website} for details.
#' Alternative but synonymous names for `"rho_C"` are:
#' composite reliability, construct reliability, reliability coefficient,
#' Joereskog's rho, coefficient omega, or Dillon-Goldstein's rho.
#' For `"rho_C_weighted"`: (Dijkstra-Henselers) rhoA. `rho_C_mm` and `rho_C_weighted_mm`
#' have no corresponding names. The former uses unit weights scaled by (w'Sw)^(-1/2) and
#' the latter weights scaled by (w'Sigma_hat w)^(-1/2) where Sigma_hat is
#' the model-implied indicator correlation matrix.
#' The Congeneric reliability is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret congeneric reliability estimates for constructs modeled as composites.
#' It is possible to report the congeneric reliability for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateRhoC()].}
#' \item{Distance measures; "dg", "dl", "dml"}{Measures of the distance
#' between the model-implied and the empirical indicator correlation matrix.
#' Currently, the geodesic distance (`"dg"`), the squared Euclidean distance
#' (`"dl"`) and the the maximum likelihood-based distance function are implemented
#' (`"dml"`). Calculation is done by [calculateDL()], [calculateDG()],
#' and [calculateDML()].}
#' \item{Degrees of freedom, "df"}{
#' Returns the degrees of freedom. Calculation is done by [calculateDf()].
#' }
#' \item{Effects; "effects"}{Total and indirect effect estimates. Additionally,
#' the variance accounted for (VAF) is computed. The VAF is defined as the ratio of a variables
#' indirect effect to its total effect. Calculation is done
#' by [calculateEffects()].}
#' \item{Effect size; "f2"}{An index of the effect size of an independent
#' variable in a structural regression equation. This measure is commonly
#' known as Cohen's f^2. The effect size of the k'th
#' independent variable in this case
#' is defined as the ratio (R2_included - R2_excluded)/(1 - R2_included), where
#' R2_included and R2_excluded are the R squares of the
#' original structural model regression equation (R2_included) and the
#' alternative specification with the k'th variable dropped (R2_excluded).
#' Calculation is done by [calculatef2()].}
#' \item{Fit indices; "chi_square", "chi_square_df", "cfi", "cn", "gfi", "ifi", "nfi",
#' "nnfi", "rmsea", "rms_theta", "srmr"}{
#' Several absolute and incremental fit indices. Note that their suitability
#' for models containing constructs modeled as composites is still an
#' open research question. Also note that fit indices are not tests in a
#' hypothesis testing sense and
#' decisions based on common one-size-fits-all cut-offs proposed in the literature
#' suffer from serious statistical drawbacks. Calculation is done by [calculateChiSquare()],
#' [calculateChiSquareDf()], [calculateCFI()],
#' [calculateGFI()], [calculateIFI()], [calculateNFI()], [calculateNNFI()],
#' [calculateRMSEA()], [calculateRMSTheta()] and [calculateSRMR()].}
#' \item{Fornell-Larcker criterion; "fl_criterion"}{A rule suggested by \insertCite{Fornell1981;textual}{cSEM}
#' to assess discriminant validity. The Fornell-Larcker
#' criterion is a decision rule based on a comparison between the squared
#' construct correlations and the average variance extracted. FL returns
#' a matrix with the squared construct correlations on the off-diagonal and
#' the AVE's on the main diagonal. Calculation is done by `calculateFLCriterion()`.}
#' \item{Goodness of Fit (GoF); "gof"}{The GoF is defined as the square root
#' of the mean of the R squares of the structural model times the mean
#' of the variances in the indicators that are explained by their
#' related constructs (i.e., the average over all lambda^2_k).
#' For the latter, only constructs modeled as common factors are considered
#' as they explain their indicator variance in contrast to a composite where
#' indicators actually build the construct.
#' Note that, contrary to what the name suggests, the GoF is **not** a
#' measure of model fit in a Chi-square fit test sense. Calculation is done
#' by [calculateGoF()].}
#' \item{Heterotrait-monotrait ratio of correlations (HTMT); "htmt"}{
#' An estimate of the correlation between latent variables assuming tau equivalent
#' measurement models. The HTMT is used
#' to assess convergent and/or discriminant validity of a construct.
#' The HTMT is inherently tied to the common factor model. If the model contains
#' less than two constructs modeled as common factors and
#' `.only_common_factors = TRUE`, `NA` is returned.
#' It is possible to report the HTMT for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateHTMT()].}
#' \item{HTMT2; "htmt2"}{
#' An estimate of the correlation between latent variables assuming congeneric
#' measurement models. The HTMT2 is used
#' to assess convergent and/or discriminant validity of a construct.
#' The HTMT is inherently tied to the common factor model. If the model contains
#' less than two constructs modeled as common factors and
#' `.only_common_factors = TRUE`, `NA` is returned.
#' It is possible to report the HTMT for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateHTMT()].}
#' \item{Model selection criteria: "aic", "aicc", "aicu", "bic", "fpe", "gm",
#' "hq", "hqc", "mallows_cp"}{
#' Several model selection criteria as suggested by \insertCite{Sharma2019;textual}{cSEM}
#' in the context of PLS. See: [calculateModelSelectionCriteria()] for details.}
#' \item{Reliability: "reliability"}{
#' As described in the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae}
#' section of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}
#' there are many different estimators for the (internal consistency) reliability.
#' Choosing `.quality_criterion = "reliability"` computes the three most common
#' measures, namely: "Cronbachs alpha" (identical to "rho_T"), "Jöreskogs rho" (identical to "rho_C_mm"),
#' and "Dijkstra-Henselers rho A" (identical to "rho_C_weighted_mm").
#' Reliability is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret reliability estimates for constructs modeled as composites.
#' It is possible to report the three common reliability estimates for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' }
#' \item{R square and R square adjusted; "r2", "r2_adj"}{The R square and the adjusted
#' R square for each structural regression equation.
#' Calculated when running [csem()].}
#' \item{Tau-equivalent reliability; "rho_T"}{An estimate of the
#' reliability assuming a tau-equivalent measurement model (i.e. a measurement
#' model with equal loadings) and a test score (proxy) based on unit weights.
#' Tau-equivalent reliability is the preferred name for reliability estimates
#' that assume a tau-equivalent measurement model such as Cronbach's alpha.
#' The tau-equivalent
#' reliability (Cronbach's alpha) is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret tau-equivalent
#' reliability estimates for constructs modeled as composites.
#' It is possible to report tau-equivalent
#' reliability estimates for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateRhoT()].}
#' \item{Variance inflation factors (VIF); "vif"}{An index for the amount of
#' (multi-)collinearity between independent variables of a regression equation. Computed
#' for each structural equation. Practically, VIF_k is defined
#' as the ratio of 1 over (1 - R2_k) where R2_k is the R squared from a regression
#' of the k'th independent variable on all remaining independent variables.
#' Calculated when running [csem()].}
#' \item{Variance inflation factors for PLS-PM mode B (VIF-ModeB); "vifmodeB"}{An index for
#' the amount of (multi-)collinearity between independent variables (indicators) in
#' mode B regression equations. Computed only if `.object` was obtained using
#' `.weight_approach = "PLS-PM"` and at least one mode was mode B.
#' Practically, VIF-ModeB_k is defined as the ratio of 1 over (1 - R2_k) where
#' R2_k is the R squared from a regression of the k'th indicator of block j on
#' all remaining indicators of the same block.
#' Calculation is done by [calculateVIFModeB()].}
#' }
#'
#' For details on the most important quality criteria see the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae} section
#' of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}.
#'
#' Some of the quality criteria are inherently tied to the classical common
#' factor model and therefore only meaningfully interpreted within a common
#' factor model (see the
#' \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article for details).
#' It is possible to force computation of all quality criteria for constructs
#' modeled as composites by setting `.only_common_factors = FALSE`, however,
#' we explicitly warn to interpret quality criteria in analogy to the common factor
#' model in this case, as the interpretation often does not carry over to composite models.
#'
#' \subsection{Resampling}{
#' To resample a given quality criterion supply the name of the function
#' that calculates the desired quality criterion to [csem()]'s `.user_funs` argument.
#' See [resamplecSEMResults()] for details.
#' }
#'
#' @usage assess(
#' .object = NULL,
#' .quality_criterion = c("all", "aic", "aicc", "aicu", "bic", "fpe", "gm", "hq",
#' "hqc", "mallows_cp", "ave",
#' "rho_C", "rho_C_mm", "rho_C_weighted",
#' "rho_C_weighted_mm", "dg", "dl", "dml", "df",
#' "effects", "f2", "fl_criterion", "chi_square", "chi_square_df",
#' "cfi", "cn", "gfi", "ifi", "nfi", "nnfi",
#' "reliability",
#' "rmsea", "rms_theta", "srmr",
#' "gof", "htmt", "htmt2", "r2", "r2_adj",
#' "rho_T", "rho_T_weighted", "vif",
#' "vifmodeB"),
#' .only_common_factors = TRUE,
#' ...
#' )
#'
#' @inheritParams csem_arguments
#' @param ... Further arguments passed to functions called by [assess()].
#' See [args_assess_dotdotdot] for a complete list of available arguments.
#'
#' @seealso [csem()], [resamplecSEMResults()], [exportToExcel()]
#'
#' @return A named list of quality criteria. Note that if only a single quality
#' criteria is computed the return value is still a list!
#'
#' @example inst/examples/example_assess.R
#'
#' @export
assess <- function(
.object = NULL,
.quality_criterion = c("all", "aic", "aicc", "aicu", "bic", "fpe", "gm", "hq",
"hqc", "mallows_cp", "ave",
"rho_C", "rho_C_mm", "rho_C_weighted",
"rho_C_weighted_mm", "dg", "dl", "dml", "df",
"effects", "f2", "fl_criterion", "chi_square", "chi_square_df",
"cfi", "cn", "gfi", "ifi", "nfi", "nnfi",
"reliability",
"rmsea", "rms_theta", "srmr",
"gof", "htmt", "htmt2", "r2", "r2_adj",
"rho_T", "rho_T_weighted", "vif",
"vifmodeB"),
.only_common_factors = TRUE,
...
){
# Note to authors:
# If you add a new argument to .quality_criterion, make sure to add it
# to the exportToExcel() function as well!
## Check arguments
match.arg(.quality_criterion,
args_default(.choices = TRUE)$.quality_criterion, several.ok = TRUE)
##
if(inherits(.object, "cSEMResults_multi")) {
out <- lapply(.object, assess,
.only_common_factors = .only_common_factors,
.quality_criterion = .quality_criterion,
...
)
return(out)
} else if(inherits(.object, "cSEMResults_2ndorder")) {
x11 <- .object$First_stage$Estimates
x12 <- .object$First_stage$Information
x21 <- .object$Second_stage$Estimates
x22 <- .object$Second_stage$Information
} else if(inherits(.object, "cSEMResults_default")) {
x21 <- .object$Estimates
x22 <- .object$Information
} else {
stop2(
"The following error occured in the assess() function:\n",
"`.object` must be a `cSEMResults` object."
)
}
if(x22$Model$model_type != "Linear") {
stop2("Currently, `assess()` does not support models containing nonlinear terms.",
"Use the individual `calculateXXX()` functions instead.")
}
## Set up empty list
out <- list()
out[["Information"]] <- list()
## Select quality criteria
if(any(.quality_criterion %in% c("all", "ave"))) {
# AVE
out[["AVE"]] <- calculateAVE(
.object,
.only_common_factors = .only_common_factors
)
}
if(any(.quality_criterion %in% c("all", "aic"))) {
# Akaike information criterion (AIC)
out[["AIC"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aic",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "aicc"))) {
# Corrected AIC (AICc)
out[["AICc"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aicc",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "aicu"))) {
# Unbiased AIC (AICu)
out[["AICu"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aicu",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "bic"))) {
# Bayesian information criterion (BIC)
out[["BIC"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "bic",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "fpe"))) {
# Bayesian information criterion (BIC)
out[["FPE"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "fpe",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "gm"))) {
# Bayesian information criterion (BIC)
out[["GM"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "gm",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "hq"))) {
# Bayesian information criterion (BIC)
out[["HQ"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "hq",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "hqc"))) {
# Bayesian information criterion (BIC)
out[["HQc"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "hqc",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "mallows_cp"))) {
# Bayesian information criterion (BIC)
out[["Mallows_Cp"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "mallows_cp",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "rho_C"))) {
# RhoC
out[["RhoC"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = TRUE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_mm"))) {
# RhoC
out[["RhoC_mm"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_weighted"))) {
# RhoC weighted
out[["RhoC_weighted"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_weighted_mm"))) {
# RhoC weighted
out[["RhoC_weighted_mm"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = TRUE
)
}
if(any(.quality_criterion %in% c("all", "dg"))) {
# dG
out[["DG"]] <- calculateDG(.object, ...)
}
if(any(.quality_criterion %in% c("all", "dl"))) {
# dL
out[["DL"]] <- calculateDL(.object, ...)
}
if(any(.quality_criterion %in% c("all", "dml"))) {
# dML
out[["DML"]] <- calculateDML(.object, ...)
}
if(any(.quality_criterion %in% c("all", "df"))) {
# dML
out[["Df"]] <- calculateDf(.object, ...)
}
if(any(.quality_criterion %in% c("all", "effects")) &&
!all(x22$Model$structural == 0)) {
# Direct, total and indirect effects
out[["Effects"]] <- if(inherits(.object, "cSEMResults_2ndorder")) {
summarize(.object)$Second_stage$Estimates$Effect_estimates
} else {
summarize(.object)$Estimates$Effect_estimates
}
}
if(any(.quality_criterion %in% c("all", "f2")) && !all(x22$Model$structural == 0)
&& x22$Arguments$.approach_paths == "OLS") {
# Effect size (f2)
out[["F2"]] <- calculatef2(.object)
}
if(any(.quality_criterion %in% c("all", "chi_square"))) {
out[["Chi_square"]] <- calculateChiSquare(.object)
}
if(any(.quality_criterion %in% c("all", "chi_square_df"))) {
out[["Chi_square_df"]] <- calculateChiSquareDf(.object)
}
if(any(.quality_criterion %in% c("all", "cfi"))) {
out[["CFI"]] <- calculateCFI(.object)
}
if(any(.quality_criterion %in% c("all", "gfi"))) {
out[["GFI"]] <- calculateGFI(
.object,
...
)
}
if(any(.quality_criterion %in% c("all", "cn"))) {
# Hoelter's (critical) N (CN)
out[["CN"]] <- calculateCN(
.object,
...
)
}
if(any(.quality_criterion %in% c("all", "ifi"))) {
out[["IFI"]] <- calculateIFI(.object)
}
if(any(.quality_criterion %in% c("all", "nfi"))) {
out[["NFI"]] <- calculateNFI(.object)
}
if(any(.quality_criterion %in% c("all", "nnfi"))) {
out[["NNFI"]] <- calculateNNFI(.object)
}
if(any(.quality_criterion %in% c("all", "rmsea"))) {
out[["RMSEA"]] <- calculateRMSEA(.object)
}
if(any(.quality_criterion %in% c("all", "rms_theta"))) {
if(inherits(.object, "cSEMResults_default")) {
out[["RMS_theta"]] <- calculateRMSTheta(.object)
} else {
warning("Computation of the RMS_theta",
" not supported for models containing second-order constructs:\n",
"Argument 'rms_theta' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "srmr"))) {
out[["SRMR"]] <- calculateSRMR(.object, ...)
}
if(any(.quality_criterion %in% c("all", "fl_criterion"))) {
if(inherits(.object, "cSEMResults_default")) {
out[["Fornell-Larcker"]] <- calculateFLCriterion(
.object,
.only_common_factors = .only_common_factors)
} else {
warning("Computation of the Fornell-Larcker criterion",
" not supported for models containing second-order constructs:\n",
"Argument 'fl_criterion' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "gof")) && !all(x22$Model$structural == 0)) {
# GoF
out[["GoF"]] <- calculateGoF(.object)
}
if(any(.quality_criterion %in% c("all", "htmt", "htmt2"))) {
# HTMT
if(inherits(.object, "cSEMResults_default")) {
if(any(.quality_criterion %in% c("all", "htmt"))){
out[["HTMT"]] <- calculateHTMT(
.object,
.only_common_factors = .only_common_factors,
.type_htmt = "htmt",
...
)}
if(any(.quality_criterion %in% c("all", "htmt2"))){
out[["HTMT2"]] <- calculateHTMT(
.object,
.only_common_factors = .only_common_factors,
.type_htmt = "htmt2",
...
)}
# Get argument values
args_htmt <- list(...)
if(any(names(args_htmt) == ".inference")) {
out$Information[[".inference"]] <- args_htmt[[".inference"]]
} else {
out$Information[[".inference"]] <- formals(calculateHTMT)[[".inference"]]
}
if(any(names(args_htmt) == ".alpha")) {
out$Information[[".alpha"]] <- args_htmt[[".alpha"]]
} else {
out$Information[[".alpha"]] <- formals(calculateHTMT)[[".alpha"]]
}
if(any(names(args_htmt) == ".ci")) {
out$Information[[".ci"]] <- args_htmt[[".ci"]]
} else {
out$Information[[".ci"]] <- "CI_percentile"
}
if(any(names(args_htmt) == ".type_htmt")) {
out$Information[[".type_htmt"]] <- args_htmt[[".type_htmt"]]
} else {
# If .type_htmt is not set in the function
out$Information[[".type_htmt"]] <- "htmt"
}
} else { # 2nd_order
warning("Computation of the HTMT",
" not supported for models containing second-order constructs:\n",
"Argument 'htmt' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "r2")) && !all(x22$Model$structural == 0)) {
# R2
out[["R2"]] <- x21$R2
if(inherits(.object, "cSEMResults_2ndorder")) {
# Remove temp from the R2s of the second stage
names(out$R2) <- gsub("_temp", "", names(x21$R2))
# out$R2 <- out$R2[intersect(second_order, cfs)]
}
}
if(any(.quality_criterion %in% c("all", "r2_adj")) && !all(x22$Model$structural == 0)) {
# Adjusted R2
out[["R2_adj"]] <- x21$R2adj
if(inherits(.object, "cSEMResults_2ndorder")) {
# Remove temp from the R2s of the second stage
names(out$R2_adj) <- gsub("_temp", "", names(x21$R2adj))
}
}
if(any(.quality_criterion %in% c("all", "reliability"))) {
# RhoT
out[["Reliability"]] <- list()
# Cronbachs alpha (rho_T)
out$Reliability[["Cronbachs_alpha"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
...
)
# Joereskogs rho (rho_C_mm)
out$Reliability[["Joereskogs_rho"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = TRUE
)
# Dijkstra-Henselers rho A (rho_C_weighted_mm)
out$Reliability[["Dijkstra-Henselers_rho_A"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_T"))) {
# RhoT
out[["RhoT"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
...
)
}
if(any(.quality_criterion %in% c("all", "rho_T_weighted"))) {
# RhoC weighted
out[["RhoT_weighted"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
.weighted = TRUE,
...
)
}
if(any(.quality_criterion %in% c("all", "vif")) && !all(x22$Model$structural == 0)) {
# VIF
out[["VIF"]] <- x21$VIF
# Make output a matrix:
# Note: this is necessary to be able to bootstrap the VIFs
# via the .user_funs argument. Currently, .user_funs functions
# need to return a vector or a matrix. I may change that in the future.
m <- matrix(0, nrow = length(names(out$VIF)), ncol = length(unique(unlist(sapply(out$VIF, names)))),
dimnames = list(names(out$VIF), unique(unlist(sapply(out$VIF, names)))))
for(i in names(out$VIF)) {
m[i, match(names(out$VIF[[i]]), colnames(m))] <- out$VIF[[i]]
}
out$VIF <- m
}
if(any(.quality_criterion %in% c("all", "vifmodeB"))) {
out[["VIF_modeB"]] <- calculateVIFModeB(.object)
}
out$Information[["All"]] <- FALSE
if(any(.quality_criterion == "all")) {
out$Information$All <- TRUE
}
class(out) <- "cSEMAssess"
return(out)
}
M-E-Steiner/cSEM documentation built on March 18, 2024, 12:18 p.m.
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Defines functions assess
Documented in assess
#' Assess model
#'
#' \lifecycle{maturing}
#'
#' Assess a model using common quality criteria.
#' See the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website} for details.
#'
#' The function is essentially a wrapper around a number of internal functions
#' that perform an "assessment task" (called a **quality criterion** in \pkg{cSEM}
#' parlance) like computing reliability estimates,
#' the effect size (Cohen's f^2), the heterotrait-monotrait ratio of correlations (HTMT) etc.
#'
#' By default every possible quality criterion is calculated (`.quality_criterion = "all"`).
#' If only a subset of quality criteria are needed a single character string
#' or a vector of character strings naming the criteria to be computed may be
#' supplied to [assess()] via the `.quality_criterion` argument. Currently, the
#' following quality criteria are implemented (in alphabetical order):
#'
#' \describe{
#' \item{Average variance extracted (AVE); "ave"}{An estimate of the
#' amount of variation in the indicators that is due to the underlying latent variable.
#' Practically, it is calculated as the ratio of the (indicator) true score variances
#' (i.e., the sum of the squared loadings)
#' relative to the sum of the total indicator variances. The AVE is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret AVE results for constructs modeled as composites.
#' It is possible to report the AVE for constructs modeled as composites by setting
#' `.only_common_factors = FALSE`, however, result should be interpreted with caution
#' as they may not have a conceptual meaning. Calculation is done
#' by [calculateAVE()].}
#' \item{Congeneric reliability; "rho_C", "rho_C_mm", "rho_C_weighted", "rho_C_weighted_mm"}{
#' An estimate of the reliability assuming a congeneric measurement model (i.e., loadings are
#' allowed to differ) and a test score (proxy) based on unit weights.
#' There are four different versions implemented. See the
#' \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae} section
#' of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website} for details.
#' Alternative but synonymous names for `"rho_C"` are:
#' composite reliability, construct reliability, reliability coefficient,
#' Joereskog's rho, coefficient omega, or Dillon-Goldstein's rho.
#' For `"rho_C_weighted"`: (Dijkstra-Henselers) rhoA. `rho_C_mm` and `rho_C_weighted_mm`
#' have no corresponding names. The former uses unit weights scaled by (w'Sw)^(-1/2) and
#' the latter weights scaled by (w'Sigma_hat w)^(-1/2) where Sigma_hat is
#' the model-implied indicator correlation matrix.
#' The Congeneric reliability is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret congeneric reliability estimates for constructs modeled as composites.
#' It is possible to report the congeneric reliability for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateRhoC()].}
#' \item{Distance measures; "dg", "dl", "dml"}{Measures of the distance
#' between the model-implied and the empirical indicator correlation matrix.
#' Currently, the geodesic distance (`"dg"`), the squared Euclidean distance
#' (`"dl"`) and the the maximum likelihood-based distance function are implemented
#' (`"dml"`). Calculation is done by [calculateDL()], [calculateDG()],
#' and [calculateDML()].}
#' \item{Degrees of freedom, "df"}{
#' Returns the degrees of freedom. Calculation is done by [calculateDf()].
#' }
#' \item{Effects; "effects"}{Total and indirect effect estimates. Additionally,
#' the variance accounted for (VAF) is computed. The VAF is defined as the ratio of a variables
#' indirect effect to its total effect. Calculation is done
#' by [calculateEffects()].}
#' \item{Effect size; "f2"}{An index of the effect size of an independent
#' variable in a structural regression equation. This measure is commonly
#' known as Cohen's f^2. The effect size of the k'th
#' independent variable in this case
#' is defined as the ratio (R2_included - R2_excluded)/(1 - R2_included), where
#' R2_included and R2_excluded are the R squares of the
#' original structural model regression equation (R2_included) and the
#' alternative specification with the k'th variable dropped (R2_excluded).
#' Calculation is done by [calculatef2()].}
#' \item{Fit indices; "chi_square", "chi_square_df", "cfi", "cn", "gfi", "ifi", "nfi",
#' "nnfi", "rmsea", "rms_theta", "srmr"}{
#' Several absolute and incremental fit indices. Note that their suitability
#' for models containing constructs modeled as composites is still an
#' open research question. Also note that fit indices are not tests in a
#' hypothesis testing sense and
#' decisions based on common one-size-fits-all cut-offs proposed in the literature
#' suffer from serious statistical drawbacks. Calculation is done by [calculateChiSquare()],
#' [calculateChiSquareDf()], [calculateCFI()],
#' [calculateGFI()], [calculateIFI()], [calculateNFI()], [calculateNNFI()],
#' [calculateRMSEA()], [calculateRMSTheta()] and [calculateSRMR()].}
#' \item{Fornell-Larcker criterion; "fl_criterion"}{A rule suggested by \insertCite{Fornell1981;textual}{cSEM}
#' to assess discriminant validity. The Fornell-Larcker
#' criterion is a decision rule based on a comparison between the squared
#' construct correlations and the average variance extracted. FL returns
#' a matrix with the squared construct correlations on the off-diagonal and
#' the AVE's on the main diagonal. Calculation is done by `calculateFLCriterion()`.}
#' \item{Goodness of Fit (GoF); "gof"}{The GoF is defined as the square root
#' of the mean of the R squares of the structural model times the mean
#' of the variances in the indicators that are explained by their
#' related constructs (i.e., the average over all lambda^2_k).
#' For the latter, only constructs modeled as common factors are considered
#' as they explain their indicator variance in contrast to a composite where
#' indicators actually build the construct.
#' Note that, contrary to what the name suggests, the GoF is **not** a
#' measure of model fit in a Chi-square fit test sense. Calculation is done
#' by [calculateGoF()].}
#' \item{Heterotrait-monotrait ratio of correlations (HTMT); "htmt"}{
#' An estimate of the correlation between latent variables assuming tau equivalent
#' measurement models. The HTMT is used
#' to assess convergent and/or discriminant validity of a construct.
#' The HTMT is inherently tied to the common factor model. If the model contains
#' less than two constructs modeled as common factors and
#' `.only_common_factors = TRUE`, `NA` is returned.
#' It is possible to report the HTMT for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateHTMT()].}
#' \item{HTMT2; "htmt2"}{
#' An estimate of the correlation between latent variables assuming congeneric
#' measurement models. The HTMT2 is used
#' to assess convergent and/or discriminant validity of a construct.
#' The HTMT is inherently tied to the common factor model. If the model contains
#' less than two constructs modeled as common factors and
#' `.only_common_factors = TRUE`, `NA` is returned.
#' It is possible to report the HTMT for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateHTMT()].}
#' \item{Model selection criteria: "aic", "aicc", "aicu", "bic", "fpe", "gm",
#' "hq", "hqc", "mallows_cp"}{
#' Several model selection criteria as suggested by \insertCite{Sharma2019;textual}{cSEM}
#' in the context of PLS. See: [calculateModelSelectionCriteria()] for details.}
#' \item{Reliability: "reliability"}{
#' As described in the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae}
#' section of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}
#' there are many different estimators for the (internal consistency) reliability.
#' Choosing `.quality_criterion = "reliability"` computes the three most common
#' measures, namely: "Cronbachs alpha" (identical to "rho_T"), "Jöreskogs rho" (identical to "rho_C_mm"),
#' and "Dijkstra-Henselers rho A" (identical to "rho_C_weighted_mm").
#' Reliability is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret reliability estimates for constructs modeled as composites.
#' It is possible to report the three common reliability estimates for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' }
#' \item{R square and R square adjusted; "r2", "r2_adj"}{The R square and the adjusted
#' R square for each structural regression equation.
#' Calculated when running [csem()].}
#' \item{Tau-equivalent reliability; "rho_T"}{An estimate of the
#' reliability assuming a tau-equivalent measurement model (i.e. a measurement
#' model with equal loadings) and a test score (proxy) based on unit weights.
#' Tau-equivalent reliability is the preferred name for reliability estimates
#' that assume a tau-equivalent measurement model such as Cronbach's alpha.
#' The tau-equivalent
#' reliability (Cronbach's alpha) is inherently
#' tied to the common factor model. It is therefore unclear how to meaningfully
#' interpret tau-equivalent
#' reliability estimates for constructs modeled as composites.
#' It is possible to report tau-equivalent
#' reliability estimates for constructs modeled as
#' composites by setting `.only_common_factors = FALSE`, however, result should be
#' interpreted with caution as they may not have a conceptual meaning.
#' Calculation is done by [calculateRhoT()].}
#' \item{Variance inflation factors (VIF); "vif"}{An index for the amount of
#' (multi-)collinearity between independent variables of a regression equation. Computed
#' for each structural equation. Practically, VIF_k is defined
#' as the ratio of 1 over (1 - R2_k) where R2_k is the R squared from a regression
#' of the k'th independent variable on all remaining independent variables.
#' Calculated when running [csem()].}
#' \item{Variance inflation factors for PLS-PM mode B (VIF-ModeB); "vifmodeB"}{An index for
#' the amount of (multi-)collinearity between independent variables (indicators) in
#' mode B regression equations. Computed only if `.object` was obtained using
#' `.weight_approach = "PLS-PM"` and at least one mode was mode B.
#' Practically, VIF-ModeB_k is defined as the ratio of 1 over (1 - R2_k) where
#' R2_k is the R squared from a regression of the k'th indicator of block j on
#' all remaining indicators of the same block.
#' Calculation is done by [calculateVIFModeB()].}
#' }
#'
#' For details on the most important quality criteria see the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html#methods}{Methods and Formulae} section
#' of the \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article on the on the
#' \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}.
#'
#' Some of the quality criteria are inherently tied to the classical common
#' factor model and therefore only meaningfully interpreted within a common
#' factor model (see the
#' \href{https://floschuberth.github.io/cSEM/articles/Using-assess.html}{Postestimation: Assessing a model}
#' article for details).
#' It is possible to force computation of all quality criteria for constructs
#' modeled as composites by setting `.only_common_factors = FALSE`, however,
#' we explicitly warn to interpret quality criteria in analogy to the common factor
#' model in this case, as the interpretation often does not carry over to composite models.
#'
#' \subsection{Resampling}{
#' To resample a given quality criterion supply the name of the function
#' that calculates the desired quality criterion to [csem()]'s `.user_funs` argument.
#' See [resamplecSEMResults()] for details.
#' }
#'
#' @usage assess(
#' .object = NULL,
#' .quality_criterion = c("all", "aic", "aicc", "aicu", "bic", "fpe", "gm", "hq",
#' "hqc", "mallows_cp", "ave",
#' "rho_C", "rho_C_mm", "rho_C_weighted",
#' "rho_C_weighted_mm", "dg", "dl", "dml", "df",
#' "effects", "f2", "fl_criterion", "chi_square", "chi_square_df",
#' "cfi", "cn", "gfi", "ifi", "nfi", "nnfi",
#' "reliability",
#' "rmsea", "rms_theta", "srmr",
#' "gof", "htmt", "htmt2", "r2", "r2_adj",
#' "rho_T", "rho_T_weighted", "vif",
#' "vifmodeB"),
#' .only_common_factors = TRUE,
#' ...
#' )
#'
#' @inheritParams csem_arguments
#' @param ... Further arguments passed to functions called by [assess()].
#' See [args_assess_dotdotdot] for a complete list of available arguments.
#'
#' @seealso [csem()], [resamplecSEMResults()], [exportToExcel()]
#'
#' @return A named list of quality criteria. Note that if only a single quality
#' criteria is computed the return value is still a list!
#'
#' @example inst/examples/example_assess.R
#'
#' @export
assess <- function(
.object = NULL,
.quality_criterion = c("all", "aic", "aicc", "aicu", "bic", "fpe", "gm", "hq",
"hqc", "mallows_cp", "ave",
"rho_C", "rho_C_mm", "rho_C_weighted",
"rho_C_weighted_mm", "dg", "dl", "dml", "df",
"effects", "f2", "fl_criterion", "chi_square", "chi_square_df",
"cfi", "cn", "gfi", "ifi", "nfi", "nnfi",
"reliability",
"rmsea", "rms_theta", "srmr",
"gof", "htmt", "htmt2", "r2", "r2_adj",
"rho_T", "rho_T_weighted", "vif",
"vifmodeB"),
.only_common_factors = TRUE,
...
){
# Note to authors:
# If you add a new argument to .quality_criterion, make sure to add it
# to the exportToExcel() function as well!
## Check arguments
match.arg(.quality_criterion,
args_default(.choices = TRUE)$.quality_criterion, several.ok = TRUE)
##
if(inherits(.object, "cSEMResults_multi")) {
out <- lapply(.object, assess,
.only_common_factors = .only_common_factors,
.quality_criterion = .quality_criterion,
...
)
return(out)
} else if(inherits(.object, "cSEMResults_2ndorder")) {
x11 <- .object$First_stage$Estimates
x12 <- .object$First_stage$Information
x21 <- .object$Second_stage$Estimates
x22 <- .object$Second_stage$Information
} else if(inherits(.object, "cSEMResults_default")) {
x21 <- .object$Estimates
x22 <- .object$Information
} else {
stop2(
"The following error occured in the assess() function:\n",
"`.object` must be a `cSEMResults` object."
)
}
if(x22$Model$model_type != "Linear") {
stop2("Currently, `assess()` does not support models containing nonlinear terms.",
"Use the individual `calculateXXX()` functions instead.")
}
## Set up empty list
out <- list()
out[["Information"]] <- list()
## Select quality criteria
if(any(.quality_criterion %in% c("all", "ave"))) {
# AVE
out[["AVE"]] <- calculateAVE(
.object,
.only_common_factors = .only_common_factors
)
}
if(any(.quality_criterion %in% c("all", "aic"))) {
# Akaike information criterion (AIC)
out[["AIC"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aic",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "aicc"))) {
# Corrected AIC (AICc)
out[["AICc"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aicc",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "aicu"))) {
# Unbiased AIC (AICu)
out[["AICu"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "aicu",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "bic"))) {
# Bayesian information criterion (BIC)
out[["BIC"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "bic",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "fpe"))) {
# Bayesian information criterion (BIC)
out[["FPE"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "fpe",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "gm"))) {
# Bayesian information criterion (BIC)
out[["GM"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "gm",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "hq"))) {
# Bayesian information criterion (BIC)
out[["HQ"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "hq",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "hqc"))) {
# Bayesian information criterion (BIC)
out[["HQc"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "hqc",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "mallows_cp"))) {
# Bayesian information criterion (BIC)
out[["Mallows_Cp"]] <- calculateModelSelectionCriteria(
.object,
.ms_criterion = "mallows_cp",
.by_equation = TRUE
)[[1]]
}
if(any(.quality_criterion %in% c("all", "rho_C"))) {
# RhoC
out[["RhoC"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = TRUE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_mm"))) {
# RhoC
out[["RhoC_mm"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_weighted"))) {
# RhoC weighted
out[["RhoC_weighted"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_C_weighted_mm"))) {
# RhoC weighted
out[["RhoC_weighted_mm"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = TRUE
)
}
if(any(.quality_criterion %in% c("all", "dg"))) {
# dG
out[["DG"]] <- calculateDG(.object, ...)
}
if(any(.quality_criterion %in% c("all", "dl"))) {
# dL
out[["DL"]] <- calculateDL(.object, ...)
}
if(any(.quality_criterion %in% c("all", "dml"))) {
# dML
out[["DML"]] <- calculateDML(.object, ...)
}
if(any(.quality_criterion %in% c("all", "df"))) {
# dML
out[["Df"]] <- calculateDf(.object, ...)
}
if(any(.quality_criterion %in% c("all", "effects")) &&
!all(x22$Model$structural == 0)) {
# Direct, total and indirect effects
out[["Effects"]] <- if(inherits(.object, "cSEMResults_2ndorder")) {
summarize(.object)$Second_stage$Estimates$Effect_estimates
} else {
summarize(.object)$Estimates$Effect_estimates
}
}
if(any(.quality_criterion %in% c("all", "f2")) && !all(x22$Model$structural == 0)
&& x22$Arguments$.approach_paths == "OLS") {
# Effect size (f2)
out[["F2"]] <- calculatef2(.object)
}
if(any(.quality_criterion %in% c("all", "chi_square"))) {
out[["Chi_square"]] <- calculateChiSquare(.object)
}
if(any(.quality_criterion %in% c("all", "chi_square_df"))) {
out[["Chi_square_df"]] <- calculateChiSquareDf(.object)
}
if(any(.quality_criterion %in% c("all", "cfi"))) {
out[["CFI"]] <- calculateCFI(.object)
}
if(any(.quality_criterion %in% c("all", "gfi"))) {
out[["GFI"]] <- calculateGFI(
.object,
...
)
}
if(any(.quality_criterion %in% c("all", "cn"))) {
# Hoelter's (critical) N (CN)
out[["CN"]] <- calculateCN(
.object,
...
)
}
if(any(.quality_criterion %in% c("all", "ifi"))) {
out[["IFI"]] <- calculateIFI(.object)
}
if(any(.quality_criterion %in% c("all", "nfi"))) {
out[["NFI"]] <- calculateNFI(.object)
}
if(any(.quality_criterion %in% c("all", "nnfi"))) {
out[["NNFI"]] <- calculateNNFI(.object)
}
if(any(.quality_criterion %in% c("all", "rmsea"))) {
out[["RMSEA"]] <- calculateRMSEA(.object)
}
if(any(.quality_criterion %in% c("all", "rms_theta"))) {
if(inherits(.object, "cSEMResults_default")) {
out[["RMS_theta"]] <- calculateRMSTheta(.object)
} else {
warning("Computation of the RMS_theta",
" not supported for models containing second-order constructs:\n",
"Argument 'rms_theta' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "srmr"))) {
out[["SRMR"]] <- calculateSRMR(.object, ...)
}
if(any(.quality_criterion %in% c("all", "fl_criterion"))) {
if(inherits(.object, "cSEMResults_default")) {
out[["Fornell-Larcker"]] <- calculateFLCriterion(
.object,
.only_common_factors = .only_common_factors)
} else {
warning("Computation of the Fornell-Larcker criterion",
" not supported for models containing second-order constructs:\n",
"Argument 'fl_criterion' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "gof")) && !all(x22$Model$structural == 0)) {
# GoF
out[["GoF"]] <- calculateGoF(.object)
}
if(any(.quality_criterion %in% c("all", "htmt", "htmt2"))) {
# HTMT
if(inherits(.object, "cSEMResults_default")) {
if(any(.quality_criterion %in% c("all", "htmt"))){
out[["HTMT"]] <- calculateHTMT(
.object,
.only_common_factors = .only_common_factors,
.type_htmt = "htmt",
...
)}
if(any(.quality_criterion %in% c("all", "htmt2"))){
out[["HTMT2"]] <- calculateHTMT(
.object,
.only_common_factors = .only_common_factors,
.type_htmt = "htmt2",
...
)}
# Get argument values
args_htmt <- list(...)
if(any(names(args_htmt) == ".inference")) {
out$Information[[".inference"]] <- args_htmt[[".inference"]]
} else {
out$Information[[".inference"]] <- formals(calculateHTMT)[[".inference"]]
}
if(any(names(args_htmt) == ".alpha")) {
out$Information[[".alpha"]] <- args_htmt[[".alpha"]]
} else {
out$Information[[".alpha"]] <- formals(calculateHTMT)[[".alpha"]]
}
if(any(names(args_htmt) == ".ci")) {
out$Information[[".ci"]] <- args_htmt[[".ci"]]
} else {
out$Information[[".ci"]] <- "CI_percentile"
}
if(any(names(args_htmt) == ".type_htmt")) {
out$Information[[".type_htmt"]] <- args_htmt[[".type_htmt"]]
} else {
# If .type_htmt is not set in the function
out$Information[[".type_htmt"]] <- "htmt"
}
} else { # 2nd_order
warning("Computation of the HTMT",
" not supported for models containing second-order constructs:\n",
"Argument 'htmt' is ignored.", call. = FALSE)
}
}
if(any(.quality_criterion %in% c("all", "r2")) && !all(x22$Model$structural == 0)) {
# R2
out[["R2"]] <- x21$R2
if(inherits(.object, "cSEMResults_2ndorder")) {
# Remove temp from the R2s of the second stage
names(out$R2) <- gsub("_temp", "", names(x21$R2))
# out$R2 <- out$R2[intersect(second_order, cfs)]
}
}
if(any(.quality_criterion %in% c("all", "r2_adj")) && !all(x22$Model$structural == 0)) {
# Adjusted R2
out[["R2_adj"]] <- x21$R2adj
if(inherits(.object, "cSEMResults_2ndorder")) {
# Remove temp from the R2s of the second stage
names(out$R2_adj) <- gsub("_temp", "", names(x21$R2adj))
}
}
if(any(.quality_criterion %in% c("all", "reliability"))) {
# RhoT
out[["Reliability"]] <- list()
# Cronbachs alpha (rho_T)
out$Reliability[["Cronbachs_alpha"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
...
)
# Joereskogs rho (rho_C_mm)
out$Reliability[["Joereskogs_rho"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = FALSE,
.model_implied = TRUE
)
# Dijkstra-Henselers rho A (rho_C_weighted_mm)
out$Reliability[["Dijkstra-Henselers_rho_A"]] <- calculateRhoC(
.object,
.only_common_factors = .only_common_factors,
.weighted = TRUE,
.model_implied = FALSE
)
}
if(any(.quality_criterion %in% c("all", "rho_T"))) {
# RhoT
out[["RhoT"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
...
)
}
if(any(.quality_criterion %in% c("all", "rho_T_weighted"))) {
# RhoC weighted
out[["RhoT_weighted"]] <- calculateRhoT(
.object,
.only_common_factors = .only_common_factors,
.output_type = "vector",
.weighted = TRUE,
...
)
}
if(any(.quality_criterion %in% c("all", "vif")) && !all(x22$Model$structural == 0)) {
# VIF
out[["VIF"]] <- x21$VIF
# Make output a matrix:
# Note: this is necessary to be able to bootstrap the VIFs
# via the .user_funs argument. Currently, .user_funs functions
# need to return a vector or a matrix. I may change that in the future.
m <- matrix(0, nrow = length(names(out$VIF)), ncol = length(unique(unlist(sapply(out$VIF, names)))),
dimnames = list(names(out$VIF), unique(unlist(sapply(out$VIF, names)))))
for(i in names(out$VIF)) {
m[i, match(names(out$VIF[[i]]), colnames(m))] <- out$VIF[[i]]
}
out$VIF <- m
}
if(any(.quality_criterion %in% c("all", "vifmodeB"))) {
out[["VIF_modeB"]] <- calculateVIFModeB(.object)
}
out$Information[["All"]] <- FALSE
if(any(.quality_criterion == "all")) {
out$Information$All <- TRUE
}
class(out) <- "cSEMAssess"
return(out)
}
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