R/00_csem.R
In M-E-Steiner/cSEM: Composite-Based Structural Equation Modeling
Defines functions
csem
Documented in
csem
#' Composite-based SEM
#'
#'\lifecycle{stable}
#'
#' Estimate linear, nonlinear, hierarchical or multigroup structural equation
#' models using a composite-based approach. In \pkg{cSEM}
#' any method or approach that involves linear compounds (scores/proxies/composites)
#' of observables (indicators/items/manifest variables) is defined as composite-based.
#' See the \href{https://floschuberth.github.io/cSEM/articles/cSEM.html}{Get started}
#' section of the \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}
#' for a general introduction to composite-based SEM and \pkg{cSEM}.
#'
#' `csem()` estimates linear, nonlinear, hierarchical or multigroup structural
#' equation models using a composite-based approach.
#'
#' \subsection{Data and model:}{
#' The `.data` and `.model` arguments are required. `.data` must be given
#' a `matrix` or a `data.frame` with column names matching
#' the indicator names used in the model description. Alternatively,
#' a `list` of data sets (matrices or data frames) may be provided
#' in which case estimation is repeated for each data set.
#' Possible column types/classes of the data provided are: "`logical`",
#' "`numeric`" ("`double`" or "`integer`"), "`factor`" ("`ordered`" and/or "`unordered`"),
#' "`character`", or a mix of several types. Character columns will be treated
#' as (unordered) factors.
#'
#' Depending on the type/class of the indicator data provided cSEM computes the indicator
#' correlation matrix in different ways. See [calculateIndicatorCor()] for details.
#'
#' In the current version `.data` must not contain missing values. Future versions
#' are likely to handle missing values as well.
#'
#' To provide a model use the [lavaan model syntax][lavaan::model.syntax].
#' Note, however, that \pkg{cSEM} currently only supports the "standard" lavaan
#' model syntax (Types 1, 2, 3, and 7 as described on the help page).
#' Therefore, specifying e.g., a threshold or scaling factors is ignored.
#' Alternatively, a standardized (possibly incomplete) [cSEMModel]-list may be supplied.
#' See [parseModel()] for details.
#' }
#'
#' \subsection{Weights and path coefficients:}{
#' By default weights are estimated using the partial least squares path modeling
#' algorithm (`"PLS-PM"`).
#' A range of alternative weighting algorithms may be supplied to
#' `.approach_weights`. Currently, the following approaches are implemented
#' \enumerate{
#' \item{(Default) Partial least squares path modeling (`"PLS-PM"`). The algorithm
#' can be customized. See [calculateWeightsPLS()] for details.}
#' \item{Generalized structured component analysis (`"GSCA"`) and generalized
#' structured component analysis with uniqueness terms (GSCAm). The algorithms
#' can be customized. See [calculateWeightsGSCA()] and [calculateWeightsGSCAm()] for details.
#' Note that GSCAm is called indirectly when the model contains constructs
#' modeled as common factors only and `.disattenuate = TRUE`. See below.}
#' \item{Generalized canonical correlation analysis (*GCCA*), including
#' `"SUMCORR"`, `"MAXVAR"`, `"SSQCORR"`, `"MINVAR"`, `"GENVAR"`.}
#' \item{Principal component analysis (`"PCA"`)}
#' \item{Factor score regression using sum scores (`"unit"`),
#' regression (`"regression"`) or bartlett scores (`"bartlett"`)}
#' }
#'
#' It is possible to supply starting values for the weighting algorithm
#' via `.starting_values`. The argument accepts 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 starting weight. See the examples section below for details.
#'
#' Composite-indicator and composite-composite correlations are properly
#' disattenuated by default to yield consistent loadings, construct correlations,
#' and path coefficients if any of the concepts are modeled as a
#' common factor.
#'
#' For *PLS-PM* disattenuation is done using *PLSc* \insertCite{Dijkstra2015}{cSEM}.
#' For *GSCA* disattenuation is done implicitly by using *GSCAm* \insertCite{Hwang2017}{cSEM}.
#' Weights obtained by *GCCA*, *unit*, *regression*, *bartlett* or *PCA* are
#' disattenuated using Croon's approach \insertCite{Croon2002}{cSEM}.
#' Disattenuation my be suppressed by setting `.disattenuate = FALSE`.
#' Note, however, that quantities in this case are inconsistent
#' estimates for their construct level counterparts if any of the constructs in
#' the structural model are modeled as a common factor!
#'
#' By default path coefficients are estimated using ordinary least squares (`.approach_path = "OLS"`).
#' For linear models, two-stage least squares (`"2SLS"`) is available, however, *only if*
#' *instruments are internal*, i.e., part of the structural model. Future versions
#' will add support for external instruments if possible. Instruments must be supplied to
#' `.instruments` as a named list where the names
#' of the list elements are the names of the dependent constructs of the structural
#' equations whose explanatory variables are believed to be endogenous.
#' The list consists of vectors of names of instruments corresponding to each equation.
#' Note that exogenous variables of a given equation **must** be supplied as
#'instruments for themselves.
#'
#' If reliabilities are known they can be supplied as `"name" = value` pairs to
#' `.reliabilities`, where `value` is a numeric value between 0 and 1.
#' Currently, only supported for "PLS-PM".
#' }
#'
#' \subsection{Nonlinear models:}{
#' If the model contains nonlinear terms `csem()` estimates a polynomial structural equation model
#' using a non-iterative method of moments approach described in
#' \insertCite{Dijkstra2014;textual}{cSEM}. Nonlinear terms include interactions and
#' exponential terms. The latter is described in model syntax as an
#' "interaction with itself", e.g., `xi^3 = xi.xi.xi`. Currently only exponential
#' terms up to a power of three (e.g., three-way interactions or cubic terms) are allowed:
#' \enumerate{
#' \item{- Single, e.g., `eta1`}
#' \item{- Quadratic, e.g., `eta1.eta1`}
#' \item{- Cubic, e.g., `eta1.eta1.eta1`}
#' \item{- Two-way interaction, e.g., `eta1.eta2`}
#' \item{- Three-way interaction, e.g., `eta1.eta2.eta3`}
#' \item{- Quadratic and two-way interaction, e.g., `eta1.eta1.eta3`}
#' }
#' The current version of the package allows two kinds of estimation:
#' estimation of the reduced form equation (`.approach_nl = "replace"`) and
#' sequential estimation (`.approach_nl = "sequential"`, the default). The latter does not
#' allow for multivariate normality of all exogenous variables, i.e.,
#' the latent variables and the error terms.
#'
#' Distributional assumptions are kept to a minimum (an i.i.d. sample from a
#' population with finite moments for the relevant order); for higher order models,
#' that go beyond interaction, we work in this version with the assumption that
#' as far as the relevant moments are concerned certain combinations of
#' measurement errors behave as if they were Gaussian.
#' For details see: \insertCite{Dijkstra2014;textual}{cSEM}.
#' }
#'
#' \subsection{Models containing second-order constructs}{
#' Second-order constructs are specified using the operators `=~` and `<~`. These
#' operators are usually used with indicators on their right-hand side. For
#' second-order constructs the right-hand side variables are constructs instead.
#' If c1, and c2 are constructs forming or measuring a higher-order
#' construct, a model would look like this:
#' \preformatted{my_model <- "
#' # Structural model
#' SAT ~ QUAL
#' VAL ~ SAT
#'
#' # Measurement/composite model
#' QUAL =~ qual1 + qual2
#' SAT =~ sat1 + sat2
#'
#' c1 =~ x11 + x12
#' c2 =~ x21 + x22
#'
#' # Second-order construct (in this case a second-order composite build by common
#' # factors)
#' VAL <~ c1 + c2
#' "
#' }
#' Currently, two approaches are explicitly implemented:
#' \itemize{
#' \item{(Default) `"2stage"`. The (disjoint) two-stage approach as proposed by \insertCite{Agarwal2000;textual}{cSEM}.
#' Note that by default a correction for attenuation is applied if common factors are
#' involved in modeling second-order constructs. For instance, the three-stage approach
#' proposed by \insertCite{VanRiel2017;textual}{cSEM} is applied in case of a second-order construct specified as a
#' composite of common factors. On the other hand, if no common factors are involved the two-stage approach
#' is applied as proposed by \insertCite{Schuberth2020;textual}{cSEM}.}
#' \item{`"mixed"`. The mixed repeated indicators/two-stage approach as proposed by \insertCite{Ringle2012;textual}{cSEM}.}
#' }
#'
#'
#' The repeated indicators approach as proposed by \insertCite{Joereskog1982b;textual}{cSEM}
#' and the extension proposed by \insertCite{Becker2012;textual}{cSEM} are
#' not directly implemented as they simply require a respecification of the model.
#' In the above example the repeated indicators approach
#' would require to change the model and to append the repeated indicators to
#' the data supplied to `.data`. Note that the indicators need to be renamed in this case as
#' `csem()` does not allow for one indicator to be attached to multiple constructs.
#' \preformatted{my_model <- "
#' # Structural model
#' SAT ~ QUAL
#' VAL ~ SAT
#'
#' VAL ~ c1 + c2
#'
#' # Measurement/composite model
#' QUAL =~ qual1 + qual2
#' SAT =~ sat1 + sat2
#' VAL =~ x11_temp + x12_temp + x21_temp + x22_temp
#'
#' c1 =~ x11 + x12
#' c2 =~ x21 + x22
#' "
#' }
#' According to the extended approach indirect effects of `QUAL` on `VAL` via `c1`
#' and `c2` would have to be specified as well.
#'}
#'
#' \subsection{Multigroup analysis}{
#' To perform a multigroup analysis provide either a list of data sets or one
#' data set containing a group-identifier-column whose column
#' name must be provided to `.id`. Values of this column are taken as levels of a
#' factor and are interpreted as group
#' identifiers. `csem()` will split the data by levels of that column and run
#' the estimation for each level separately. Note, the more levels
#' the group-identifier-column has, the more estimation runs are required.
#' This can considerably slow down estimation, especially if resampling is
#' requested. For the latter it will generally be faster to use
#' `.eval_plan = "multisession"` or `.eval_plan = "multicore"`.
#' }
#' \subsection{Inference:}{
#' Inference is done via resampling. See [resamplecSEMResults()] and [infer()] for details.
#' }
#'
#' @usage csem(
#' .data = NULL,
#' .model = NULL,
#' .approach_2ndorder = c("2stage", "mixed"),
#' .approach_cor_robust = c("none", "mcd", "spearman"),
#' .approach_nl = c("sequential", "replace"),
#' .approach_paths = c("OLS", "2SLS"),
#' .approach_weights = c("PLS-PM", "SUMCORR", "MAXVAR", "SSQCORR",
#' "MINVAR", "GENVAR","GSCA", "PCA",
#' "unit", "bartlett", "regression"),
#' .conv_criterion = c("diff_absolute", "diff_squared", "diff_relative"),
#' .disattenuate = TRUE,
#' .dominant_indicators = NULL,
#' .estimate_structural = TRUE,
#' .id = NULL,
#' .instruments = NULL,
#' .iter_max = 100,
#' .normality = FALSE,
#' .PLS_approach_cf = c("dist_squared_euclid", "dist_euclid_weighted",
#' "fisher_transformed", "mean_arithmetic",
#' "mean_geometric", "mean_harmonic",
#' "geo_of_harmonic"),
#' .PLS_ignore_structural_model = FALSE,
#' .PLS_modes = NULL,
#' .PLS_weight_scheme_inner = c("path", "centroid", "factorial"),
#' .reliabilities = NULL,
#' .starting_values = NULL,
#' .resample_method = c("none", "bootstrap", "jackknife"),
#' .resample_method2 = c("none", "bootstrap", "jackknife"),
#' .R = 499,
#' .R2 = 199,
#' .handle_inadmissibles = c("drop", "ignore", "replace"),
#' .user_funs = NULL,
#' .eval_plan = c("sequential", "multicore", "multisession"),
#' .seed = NULL,
#' .sign_change_option = c("none", "individual", "individual_reestimate",
#' "construct_reestimate"),
#' .tolerance = 1e-05
#' )
#'
#' @param .data A `data.frame` or a `matrix` of standardized or unstandardized
#' data (indicators/items/manifest variables).
#' Additionally, a `list` of data sets (data frames or matrices) is accepted in which
#' case estimation is repeated for each data set. Possible column types or classes
#' of the data provided are: "`logical`", "`numeric`" ("`double`" or "`integer`"),
#' "`factor`" ("`ordered`" and/or "`unordered`"), "`character`" (will be converted to factor),
#' or a mix of several types.
#' @inheritParams csem_arguments
#'
#' @return
#' An object of class `cSEMResults` with methods for all postestimation generics.
#' Technically, a call to [csem()] results in an object with at least
#' two class attributes. The first class attribute is always `cSEMResults`.
#' The second is one of `cSEMResults_default`, `cSEMResults_multi`, or
#' `cSEMResults_2ndorder` and depends on the estimated model and/or the type of
#' data provided to the `.model` and `.data` arguments. The third class attribute
#' `cSEMResults_resampled` is only added if resampling was conducted.
#' For a details see the [cSEMResults helpfile ][cSEMResults].
#'
#' @section Postestimation:
#' \describe{
#' \item{[assess()]}{Assess results using common quality criteria, e.g., reliability,
#' fit measures, HTMT, R2 etc.}
#' \item{[infer()]}{Calculate common inferential quantities, e.g., standard errors,
#' confidence intervals.}
#' \item{[predict()]}{Predict endogenous indicator scores and compute common prediction metrics.}
#' \item{[summarize()]}{Summarize the results. Mainly called for its side-effect the print method.}
#' \item{[verify()]}{Verify/Check admissibility of the estimates.}
#' }
#'
#' Tests are performed using the test-family of functions. Currently the following
#' tests are implemented:
#' \describe{
#' \item{[testOMF()]}{Bootstrap-based test for overall model fit based on
#' \insertCite{Beran1985;textual}{cSEM}}
#' \item{[testMICOM()]}{Permutation-based test for measurement invariance of composites
#' proposed by \insertCite{Henseler2016;textual}{cSEM}}
#' \item{[testMGD()]}{Several (mainly) permutation-based tests for multi-group comparisons.}
#' \item{[testHausman()]}{Regression-based Hausman test to test for endogeneity.}
#' }
#'
#' Other miscellaneous postestimation functions belong do the do-family of functions.
#' Currently three do functions are implemented:
#' \describe{
#' \item{[doIPMA()]}{Performs an importance-performance matrix analyis (IPMA).}
#' \item{[doNonlinearEffectsAnalysis()]}{Perform a nonlinear effects analysis as
#' described in e.g.,
#' \insertCite{Spiller2013;textual}{cSEM}}
#' \item{[doRedundancyAnalysis()]}{Perform a redundancy analysis (RA) as proposed by
#' \insertCite{Hair2016;textual}{cSEM} with reference to \insertCite{Chin1998;textual}{cSEM}}
#' }
#'
#' @references
#' \insertAllCited{}
#'
#' @seealso [args_default()], [cSEMArguments], [cSEMResults], [foreman()], [resamplecSEMResults()],
#' [assess()], [infer()], [predict()], [summarize()], [verify()], [testOMF()],
#' [testMGD()], [testMICOM()], [testHausman()]
#'
#' @example inst/examples/example_csem.R
#'
#' @export
csem <- function(
.data = NULL,
.model = NULL,
.approach_2ndorder = c("2stage", "mixed"),
.approach_cor_robust = c("none", "mcd", "spearman"),
.approach_nl = c("sequential", "replace"),
.approach_paths = c("OLS", "2SLS"),
.approach_weights = c("PLS-PM", "SUMCORR", "MAXVAR", "SSQCORR",
"MINVAR", "GENVAR","GSCA", "PCA",
"unit", "bartlett", "regression"),
.conv_criterion = c("diff_absolute", "diff_squared", "diff_relative"),
.disattenuate = TRUE,
.dominant_indicators = NULL,
.estimate_structural = TRUE,
.id = NULL,
.instruments = NULL,
.iter_max = 100,
.normality = FALSE,
.PLS_approach_cf = c("dist_squared_euclid", "dist_euclid_weighted",
"fisher_transformed", "mean_arithmetic",
"mean_geometric", "mean_harmonic",
"geo_of_harmonic"),
.PLS_ignore_structural_model = FALSE,
.PLS_modes = NULL,
.PLS_weight_scheme_inner = c("path", "centroid", "factorial"),
.reliabilities = NULL,
.starting_values = NULL,
.resample_method = c("none", "bootstrap", "jackknife"),
.resample_method2 = c("none", "bootstrap", "jackknife"),
.R = 499,
.R2 = 199,
.handle_inadmissibles = c("drop", "ignore", "replace"),
.user_funs = NULL,
.eval_plan = c("sequential", "multicore", "multisession"),
.seed = NULL,
.sign_change_option = c("none", "individual", "individual_reestimate",
"construct_reestimate"),
.tolerance = 1e-05
) {
## Match arguments
.approach_2ndorder <- match.arg(.approach_2ndorder)
.approach_cor_robust <- match.arg(.approach_cor_robust)
.approach_nl <- match.arg(.approach_nl)
.approach_paths <- match.arg(.approach_paths)
.approach_weights <- match.arg(.approach_weights)
.conv_criterion <- match.arg(.conv_criterion)
.eval_plan <- match.arg(.eval_plan)
.handle_inadmissibles <- match.arg(.handle_inadmissibles)
.PLS_approach_cf <- match.arg(.PLS_approach_cf)
.PLS_weight_scheme_inner <- match.arg(.PLS_weight_scheme_inner)
.resample_method <- match.arg(.resample_method)
.resample_method2 <- match.arg(.resample_method2)
.sign_change_option <- match.arg(.sign_change_option)
## Collect and handle arguments
# Note: all.names = TRUE is neccessary for otherwise arguments with a leading
# dot will be omitted!
args_used <- c(as.list(environment(), all.names = TRUE))
args <- handleArgs(args_used)
args_needed <- args[intersect(names(args), names(as.list(formals(foreman))))]
## Check if columnames of .data contain a "." and stop if so
## Check data
if(!any(class(.data) %in% c("data.frame", "matrix", "list"))) {
stop2(
"The following error occured in the `csem()` function:\n",
"Data must be provided as a `matrix`, a `data.frame` or a `list`. ",
".data has class: ",
paste0(class(.data), collapse = ", ")
)
}
if(inherits(.data, "list")) {
c_names <- unique(unlist(lapply(.data, colnames)))
} else {
c_names <- colnames(.data)
}
if(length(grep("\\.", c_names)) > 0) {
stop2(
"At least one variable name in your data set contain a `.` (dot).",
" Dots are a reserved special character in cSEM. Please rename these variables in your data and the model description.")
}
## Parse model
model_original <- parseModel(.model, .instruments = .instruments)
## Modify model if model contains second order constructs
if(any(model_original$construct_order == "Second order")) {
model_1stage <- convertModel(
.csem_model = model_original,
.approach_2ndorder = args$.approach_2ndorder,
.stage = "first")
## Update model
model_1stage$construct_order <- model_original$construct_order
args_needed[[".model"]] <- model_1stage
} else {
args_needed[[".model"]] <- model_original
}
## Select cases
if(!is.null(.id) && !inherits(.data, "list")) {
if(length(.id) != 1) {
stop2(
"The following error occured in the `csem()` function:\n",
"`.id` must be a character string or an integer identifying one single column."
)
}
if(is.matrix(.data)) {
.data <- as.data.frame(.data)
}
data_split <- split(.data, f = .data[, .id])
out <- lapply(data_split, function(x) {
if(is.numeric(.id)) {
args_needed[[".data"]] <- x[, -.id]
} else {
args_needed[[".data"]] <- x[, -which(names(x) == .id)]
}
## NOTE:
# 01.03.2019: Using do.call(foreman, args_needed) would be more elegant but is much
# much much! slower (especially for larger data sets).
#
# 15.05.2019: Apparently do.call(foreman, args_needed) is not that bad
# after all. I did several comparisons using microbenchmark
# but there was no speed difference (anymore?!). When I compared and
# thought that do.call is slow I fixed other parts of
# foreman as well...maybe they had been the real culprit.
# So we use do.call again, as it avoids redundancy
do.call(foreman, args_needed)
})
} else if(any(class(.data) == "list")) {
out <- lapply(.data, function(x) {
args_needed[[".data"]] <- x
do.call(foreman, args_needed)
})
if(is.null(names(.data))) {
names(out) <- paste0("Data_", 1:length(out))
} else {
names(out) <- names(.data)
}
} else {
out <- do.call(foreman, args_needed)
}
## Set class for output
# See the details section of ?UseMethod() to learn how method dispatch works
# for objects with multiple classes
if(inherits(.data, "list") | !is.null(.id)) {
# Sometimes the original (unstandardized) pooled data set is required
# (e.g. for permutation and permutation based tests).
# By convention the original, pooled dataset is therefore added to the first
# element of "out" (= results for the first group/dataset)!
# If ".data" was a list of data they are combined to one pooled dataset
# If ".data" was originally pooled and subsequently split by ".id"
# The original unsplit data is returned.
out[[1]]$Information$Data_pooled <- if(inherits(.data, "list")) {
# Clean and order columns according to the first data set
data_cleaned <- lapply(.data, function(x) {
# Order data according to the ordering of the measurement model; delete
# all columns that are not needed
x <- x[, setdiff(colnames(model_original$measurement), model_original$vars_attached_to_2nd)]
x
})
# Combine
data_pooled <- do.call(rbind, data_cleaned)
data_pooled <- as.data.frame(data_pooled)
# data_pooled[, "id"] <- rep(names(out), times = sapply(.data, nrow))
data_pooled
} else {
.data
}
## Add second order approach to $Information
if(any(model_original$construct_order == "Second order")) {
out <- lapply(out, function(x){
x$Information$Approach_2ndorder <- .approach_2ndorder
x$Information$Model_original <- model_original
x
})
} else {
out <- lapply(out, function(x){
x$Information$Approach_2ndorder <- NA
x
})
}
## Set class
class(out) <- c("cSEMResults", "cSEMResults_multi")
## Second order (2/3 stage approach)
if(any(model_original$construct_order == "Second order") &&
args$.approach_2ndorder %in% c("2stage", "mixed")) {
### Second step
out <- lapply(out, function(x) {
calculate2ndStage(model_original, x, args_needed, .approach_2ndorder)
})
## Set class
class(out) <- c("cSEMResults", "cSEMResults_multi", "cSEMResults_2ndorder")
}
} else if(any(model_original$construct_order == "Second order") &&
args$.approach_2ndorder %in% c("2stage", "mixed")) {
### Second step
out <- calculate2ndStage(model_original, out, args_needed, .approach_2ndorder)
} else {
## Add second order approach to $Information
if(any(model_original$construct_order == "Second order")) {
out$Information$Approach_2ndorder <- .approach_2ndorder
out$Information$Model_original <- model_original
} else {
out$Information$Approach_2ndorder <- NA
}
class(out) <- c("cSEMResults", "cSEMResults_default")
}
## Resample if requested:
if(.resample_method != "none") {
out <- resamplecSEMResults(
.object = out,
.resample_method = .resample_method,
.resample_method2 = .resample_method2,
.R = .R,
.R2 = .R2,
.handle_inadmissibles = .handle_inadmissibles,
.user_funs = .user_funs,
.eval_plan = .eval_plan,
.force = FALSE,
.seed = .seed,
.sign_change_option = .sign_change_option
)
}
return(out)
}
M-E-Steiner/cSEM documentation built on March 18, 2024, 12:18 p.m.
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Defines functions csem
Documented in csem
#' Composite-based SEM
#'
#'\lifecycle{stable}
#'
#' Estimate linear, nonlinear, hierarchical or multigroup structural equation
#' models using a composite-based approach. In \pkg{cSEM}
#' any method or approach that involves linear compounds (scores/proxies/composites)
#' of observables (indicators/items/manifest variables) is defined as composite-based.
#' See the \href{https://floschuberth.github.io/cSEM/articles/cSEM.html}{Get started}
#' section of the \href{https://floschuberth.github.io/cSEM/index.html}{cSEM website}
#' for a general introduction to composite-based SEM and \pkg{cSEM}.
#'
#' `csem()` estimates linear, nonlinear, hierarchical or multigroup structural
#' equation models using a composite-based approach.
#'
#' \subsection{Data and model:}{
#' The `.data` and `.model` arguments are required. `.data` must be given
#' a `matrix` or a `data.frame` with column names matching
#' the indicator names used in the model description. Alternatively,
#' a `list` of data sets (matrices or data frames) may be provided
#' in which case estimation is repeated for each data set.
#' Possible column types/classes of the data provided are: "`logical`",
#' "`numeric`" ("`double`" or "`integer`"), "`factor`" ("`ordered`" and/or "`unordered`"),
#' "`character`", or a mix of several types. Character columns will be treated
#' as (unordered) factors.
#'
#' Depending on the type/class of the indicator data provided cSEM computes the indicator
#' correlation matrix in different ways. See [calculateIndicatorCor()] for details.
#'
#' In the current version `.data` must not contain missing values. Future versions
#' are likely to handle missing values as well.
#'
#' To provide a model use the [lavaan model syntax][lavaan::model.syntax].
#' Note, however, that \pkg{cSEM} currently only supports the "standard" lavaan
#' model syntax (Types 1, 2, 3, and 7 as described on the help page).
#' Therefore, specifying e.g., a threshold or scaling factors is ignored.
#' Alternatively, a standardized (possibly incomplete) [cSEMModel]-list may be supplied.
#' See [parseModel()] for details.
#' }
#'
#' \subsection{Weights and path coefficients:}{
#' By default weights are estimated using the partial least squares path modeling
#' algorithm (`"PLS-PM"`).
#' A range of alternative weighting algorithms may be supplied to
#' `.approach_weights`. Currently, the following approaches are implemented
#' \enumerate{
#' \item{(Default) Partial least squares path modeling (`"PLS-PM"`). The algorithm
#' can be customized. See [calculateWeightsPLS()] for details.}
#' \item{Generalized structured component analysis (`"GSCA"`) and generalized
#' structured component analysis with uniqueness terms (GSCAm). The algorithms
#' can be customized. See [calculateWeightsGSCA()] and [calculateWeightsGSCAm()] for details.
#' Note that GSCAm is called indirectly when the model contains constructs
#' modeled as common factors only and `.disattenuate = TRUE`. See below.}
#' \item{Generalized canonical correlation analysis (*GCCA*), including
#' `"SUMCORR"`, `"MAXVAR"`, `"SSQCORR"`, `"MINVAR"`, `"GENVAR"`.}
#' \item{Principal component analysis (`"PCA"`)}
#' \item{Factor score regression using sum scores (`"unit"`),
#' regression (`"regression"`) or bartlett scores (`"bartlett"`)}
#' }
#'
#' It is possible to supply starting values for the weighting algorithm
#' via `.starting_values`. The argument accepts 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 starting weight. See the examples section below for details.
#'
#' Composite-indicator and composite-composite correlations are properly
#' disattenuated by default to yield consistent loadings, construct correlations,
#' and path coefficients if any of the concepts are modeled as a
#' common factor.
#'
#' For *PLS-PM* disattenuation is done using *PLSc* \insertCite{Dijkstra2015}{cSEM}.
#' For *GSCA* disattenuation is done implicitly by using *GSCAm* \insertCite{Hwang2017}{cSEM}.
#' Weights obtained by *GCCA*, *unit*, *regression*, *bartlett* or *PCA* are
#' disattenuated using Croon's approach \insertCite{Croon2002}{cSEM}.
#' Disattenuation my be suppressed by setting `.disattenuate = FALSE`.
#' Note, however, that quantities in this case are inconsistent
#' estimates for their construct level counterparts if any of the constructs in
#' the structural model are modeled as a common factor!
#'
#' By default path coefficients are estimated using ordinary least squares (`.approach_path = "OLS"`).
#' For linear models, two-stage least squares (`"2SLS"`) is available, however, *only if*
#' *instruments are internal*, i.e., part of the structural model. Future versions
#' will add support for external instruments if possible. Instruments must be supplied to
#' `.instruments` as a named list where the names
#' of the list elements are the names of the dependent constructs of the structural
#' equations whose explanatory variables are believed to be endogenous.
#' The list consists of vectors of names of instruments corresponding to each equation.
#' Note that exogenous variables of a given equation **must** be supplied as
#'instruments for themselves.
#'
#' If reliabilities are known they can be supplied as `"name" = value` pairs to
#' `.reliabilities`, where `value` is a numeric value between 0 and 1.
#' Currently, only supported for "PLS-PM".
#' }
#'
#' \subsection{Nonlinear models:}{
#' If the model contains nonlinear terms `csem()` estimates a polynomial structural equation model
#' using a non-iterative method of moments approach described in
#' \insertCite{Dijkstra2014;textual}{cSEM}. Nonlinear terms include interactions and
#' exponential terms. The latter is described in model syntax as an
#' "interaction with itself", e.g., `xi^3 = xi.xi.xi`. Currently only exponential
#' terms up to a power of three (e.g., three-way interactions or cubic terms) are allowed:
#' \enumerate{
#' \item{- Single, e.g., `eta1`}
#' \item{- Quadratic, e.g., `eta1.eta1`}
#' \item{- Cubic, e.g., `eta1.eta1.eta1`}
#' \item{- Two-way interaction, e.g., `eta1.eta2`}
#' \item{- Three-way interaction, e.g., `eta1.eta2.eta3`}
#' \item{- Quadratic and two-way interaction, e.g., `eta1.eta1.eta3`}
#' }
#' The current version of the package allows two kinds of estimation:
#' estimation of the reduced form equation (`.approach_nl = "replace"`) and
#' sequential estimation (`.approach_nl = "sequential"`, the default). The latter does not
#' allow for multivariate normality of all exogenous variables, i.e.,
#' the latent variables and the error terms.
#'
#' Distributional assumptions are kept to a minimum (an i.i.d. sample from a
#' population with finite moments for the relevant order); for higher order models,
#' that go beyond interaction, we work in this version with the assumption that
#' as far as the relevant moments are concerned certain combinations of
#' measurement errors behave as if they were Gaussian.
#' For details see: \insertCite{Dijkstra2014;textual}{cSEM}.
#' }
#'
#' \subsection{Models containing second-order constructs}{
#' Second-order constructs are specified using the operators `=~` and `<~`. These
#' operators are usually used with indicators on their right-hand side. For
#' second-order constructs the right-hand side variables are constructs instead.
#' If c1, and c2 are constructs forming or measuring a higher-order
#' construct, a model would look like this:
#' \preformatted{my_model <- "
#' # Structural model
#' SAT ~ QUAL
#' VAL ~ SAT
#'
#' # Measurement/composite model
#' QUAL =~ qual1 + qual2
#' SAT =~ sat1 + sat2
#'
#' c1 =~ x11 + x12
#' c2 =~ x21 + x22
#'
#' # Second-order construct (in this case a second-order composite build by common
#' # factors)
#' VAL <~ c1 + c2
#' "
#' }
#' Currently, two approaches are explicitly implemented:
#' \itemize{
#' \item{(Default) `"2stage"`. The (disjoint) two-stage approach as proposed by \insertCite{Agarwal2000;textual}{cSEM}.
#' Note that by default a correction for attenuation is applied if common factors are
#' involved in modeling second-order constructs. For instance, the three-stage approach
#' proposed by \insertCite{VanRiel2017;textual}{cSEM} is applied in case of a second-order construct specified as a
#' composite of common factors. On the other hand, if no common factors are involved the two-stage approach
#' is applied as proposed by \insertCite{Schuberth2020;textual}{cSEM}.}
#' \item{`"mixed"`. The mixed repeated indicators/two-stage approach as proposed by \insertCite{Ringle2012;textual}{cSEM}.}
#' }
#'
#'
#' The repeated indicators approach as proposed by \insertCite{Joereskog1982b;textual}{cSEM}
#' and the extension proposed by \insertCite{Becker2012;textual}{cSEM} are
#' not directly implemented as they simply require a respecification of the model.
#' In the above example the repeated indicators approach
#' would require to change the model and to append the repeated indicators to
#' the data supplied to `.data`. Note that the indicators need to be renamed in this case as
#' `csem()` does not allow for one indicator to be attached to multiple constructs.
#' \preformatted{my_model <- "
#' # Structural model
#' SAT ~ QUAL
#' VAL ~ SAT
#'
#' VAL ~ c1 + c2
#'
#' # Measurement/composite model
#' QUAL =~ qual1 + qual2
#' SAT =~ sat1 + sat2
#' VAL =~ x11_temp + x12_temp + x21_temp + x22_temp
#'
#' c1 =~ x11 + x12
#' c2 =~ x21 + x22
#' "
#' }
#' According to the extended approach indirect effects of `QUAL` on `VAL` via `c1`
#' and `c2` would have to be specified as well.
#'}
#'
#' \subsection{Multigroup analysis}{
#' To perform a multigroup analysis provide either a list of data sets or one
#' data set containing a group-identifier-column whose column
#' name must be provided to `.id`. Values of this column are taken as levels of a
#' factor and are interpreted as group
#' identifiers. `csem()` will split the data by levels of that column and run
#' the estimation for each level separately. Note, the more levels
#' the group-identifier-column has, the more estimation runs are required.
#' This can considerably slow down estimation, especially if resampling is
#' requested. For the latter it will generally be faster to use
#' `.eval_plan = "multisession"` or `.eval_plan = "multicore"`.
#' }
#' \subsection{Inference:}{
#' Inference is done via resampling. See [resamplecSEMResults()] and [infer()] for details.
#' }
#'
#' @usage csem(
#' .data = NULL,
#' .model = NULL,
#' .approach_2ndorder = c("2stage", "mixed"),
#' .approach_cor_robust = c("none", "mcd", "spearman"),
#' .approach_nl = c("sequential", "replace"),
#' .approach_paths = c("OLS", "2SLS"),
#' .approach_weights = c("PLS-PM", "SUMCORR", "MAXVAR", "SSQCORR",
#' "MINVAR", "GENVAR","GSCA", "PCA",
#' "unit", "bartlett", "regression"),
#' .conv_criterion = c("diff_absolute", "diff_squared", "diff_relative"),
#' .disattenuate = TRUE,
#' .dominant_indicators = NULL,
#' .estimate_structural = TRUE,
#' .id = NULL,
#' .instruments = NULL,
#' .iter_max = 100,
#' .normality = FALSE,
#' .PLS_approach_cf = c("dist_squared_euclid", "dist_euclid_weighted",
#' "fisher_transformed", "mean_arithmetic",
#' "mean_geometric", "mean_harmonic",
#' "geo_of_harmonic"),
#' .PLS_ignore_structural_model = FALSE,
#' .PLS_modes = NULL,
#' .PLS_weight_scheme_inner = c("path", "centroid", "factorial"),
#' .reliabilities = NULL,
#' .starting_values = NULL,
#' .resample_method = c("none", "bootstrap", "jackknife"),
#' .resample_method2 = c("none", "bootstrap", "jackknife"),
#' .R = 499,
#' .R2 = 199,
#' .handle_inadmissibles = c("drop", "ignore", "replace"),
#' .user_funs = NULL,
#' .eval_plan = c("sequential", "multicore", "multisession"),
#' .seed = NULL,
#' .sign_change_option = c("none", "individual", "individual_reestimate",
#' "construct_reestimate"),
#' .tolerance = 1e-05
#' )
#'
#' @param .data A `data.frame` or a `matrix` of standardized or unstandardized
#' data (indicators/items/manifest variables).
#' Additionally, a `list` of data sets (data frames or matrices) is accepted in which
#' case estimation is repeated for each data set. Possible column types or classes
#' of the data provided are: "`logical`", "`numeric`" ("`double`" or "`integer`"),
#' "`factor`" ("`ordered`" and/or "`unordered`"), "`character`" (will be converted to factor),
#' or a mix of several types.
#' @inheritParams csem_arguments
#'
#' @return
#' An object of class `cSEMResults` with methods for all postestimation generics.
#' Technically, a call to [csem()] results in an object with at least
#' two class attributes. The first class attribute is always `cSEMResults`.
#' The second is one of `cSEMResults_default`, `cSEMResults_multi`, or
#' `cSEMResults_2ndorder` and depends on the estimated model and/or the type of
#' data provided to the `.model` and `.data` arguments. The third class attribute
#' `cSEMResults_resampled` is only added if resampling was conducted.
#' For a details see the [cSEMResults helpfile ][cSEMResults].
#'
#' @section Postestimation:
#' \describe{
#' \item{[assess()]}{Assess results using common quality criteria, e.g., reliability,
#' fit measures, HTMT, R2 etc.}
#' \item{[infer()]}{Calculate common inferential quantities, e.g., standard errors,
#' confidence intervals.}
#' \item{[predict()]}{Predict endogenous indicator scores and compute common prediction metrics.}
#' \item{[summarize()]}{Summarize the results. Mainly called for its side-effect the print method.}
#' \item{[verify()]}{Verify/Check admissibility of the estimates.}
#' }
#'
#' Tests are performed using the test-family of functions. Currently the following
#' tests are implemented:
#' \describe{
#' \item{[testOMF()]}{Bootstrap-based test for overall model fit based on
#' \insertCite{Beran1985;textual}{cSEM}}
#' \item{[testMICOM()]}{Permutation-based test for measurement invariance of composites
#' proposed by \insertCite{Henseler2016;textual}{cSEM}}
#' \item{[testMGD()]}{Several (mainly) permutation-based tests for multi-group comparisons.}
#' \item{[testHausman()]}{Regression-based Hausman test to test for endogeneity.}
#' }
#'
#' Other miscellaneous postestimation functions belong do the do-family of functions.
#' Currently three do functions are implemented:
#' \describe{
#' \item{[doIPMA()]}{Performs an importance-performance matrix analyis (IPMA).}
#' \item{[doNonlinearEffectsAnalysis()]}{Perform a nonlinear effects analysis as
#' described in e.g.,
#' \insertCite{Spiller2013;textual}{cSEM}}
#' \item{[doRedundancyAnalysis()]}{Perform a redundancy analysis (RA) as proposed by
#' \insertCite{Hair2016;textual}{cSEM} with reference to \insertCite{Chin1998;textual}{cSEM}}
#' }
#'
#' @references
#' \insertAllCited{}
#'
#' @seealso [args_default()], [cSEMArguments], [cSEMResults], [foreman()], [resamplecSEMResults()],
#' [assess()], [infer()], [predict()], [summarize()], [verify()], [testOMF()],
#' [testMGD()], [testMICOM()], [testHausman()]
#'
#' @example inst/examples/example_csem.R
#'
#' @export
csem <- function(
.data = NULL,
.model = NULL,
.approach_2ndorder = c("2stage", "mixed"),
.approach_cor_robust = c("none", "mcd", "spearman"),
.approach_nl = c("sequential", "replace"),
.approach_paths = c("OLS", "2SLS"),
.approach_weights = c("PLS-PM", "SUMCORR", "MAXVAR", "SSQCORR",
"MINVAR", "GENVAR","GSCA", "PCA",
"unit", "bartlett", "regression"),
.conv_criterion = c("diff_absolute", "diff_squared", "diff_relative"),
.disattenuate = TRUE,
.dominant_indicators = NULL,
.estimate_structural = TRUE,
.id = NULL,
.instruments = NULL,
.iter_max = 100,
.normality = FALSE,
.PLS_approach_cf = c("dist_squared_euclid", "dist_euclid_weighted",
"fisher_transformed", "mean_arithmetic",
"mean_geometric", "mean_harmonic",
"geo_of_harmonic"),
.PLS_ignore_structural_model = FALSE,
.PLS_modes = NULL,
.PLS_weight_scheme_inner = c("path", "centroid", "factorial"),
.reliabilities = NULL,
.starting_values = NULL,
.resample_method = c("none", "bootstrap", "jackknife"),
.resample_method2 = c("none", "bootstrap", "jackknife"),
.R = 499,
.R2 = 199,
.handle_inadmissibles = c("drop", "ignore", "replace"),
.user_funs = NULL,
.eval_plan = c("sequential", "multicore", "multisession"),
.seed = NULL,
.sign_change_option = c("none", "individual", "individual_reestimate",
"construct_reestimate"),
.tolerance = 1e-05
) {
## Match arguments
.approach_2ndorder <- match.arg(.approach_2ndorder)
.approach_cor_robust <- match.arg(.approach_cor_robust)
.approach_nl <- match.arg(.approach_nl)
.approach_paths <- match.arg(.approach_paths)
.approach_weights <- match.arg(.approach_weights)
.conv_criterion <- match.arg(.conv_criterion)
.eval_plan <- match.arg(.eval_plan)
.handle_inadmissibles <- match.arg(.handle_inadmissibles)
.PLS_approach_cf <- match.arg(.PLS_approach_cf)
.PLS_weight_scheme_inner <- match.arg(.PLS_weight_scheme_inner)
.resample_method <- match.arg(.resample_method)
.resample_method2 <- match.arg(.resample_method2)
.sign_change_option <- match.arg(.sign_change_option)
## Collect and handle arguments
# Note: all.names = TRUE is neccessary for otherwise arguments with a leading
# dot will be omitted!
args_used <- c(as.list(environment(), all.names = TRUE))
args <- handleArgs(args_used)
args_needed <- args[intersect(names(args), names(as.list(formals(foreman))))]
## Check if columnames of .data contain a "." and stop if so
## Check data
if(!any(class(.data) %in% c("data.frame", "matrix", "list"))) {
stop2(
"The following error occured in the `csem()` function:\n",
"Data must be provided as a `matrix`, a `data.frame` or a `list`. ",
".data has class: ",
paste0(class(.data), collapse = ", ")
)
}
if(inherits(.data, "list")) {
c_names <- unique(unlist(lapply(.data, colnames)))
} else {
c_names <- colnames(.data)
}
if(length(grep("\\.", c_names)) > 0) {
stop2(
"At least one variable name in your data set contain a `.` (dot).",
" Dots are a reserved special character in cSEM. Please rename these variables in your data and the model description.")
}
## Parse model
model_original <- parseModel(.model, .instruments = .instruments)
## Modify model if model contains second order constructs
if(any(model_original$construct_order == "Second order")) {
model_1stage <- convertModel(
.csem_model = model_original,
.approach_2ndorder = args$.approach_2ndorder,
.stage = "first")
## Update model
model_1stage$construct_order <- model_original$construct_order
args_needed[[".model"]] <- model_1stage
} else {
args_needed[[".model"]] <- model_original
}
## Select cases
if(!is.null(.id) && !inherits(.data, "list")) {
if(length(.id) != 1) {
stop2(
"The following error occured in the `csem()` function:\n",
"`.id` must be a character string or an integer identifying one single column."
)
}
if(is.matrix(.data)) {
.data <- as.data.frame(.data)
}
data_split <- split(.data, f = .data[, .id])
out <- lapply(data_split, function(x) {
if(is.numeric(.id)) {
args_needed[[".data"]] <- x[, -.id]
} else {
args_needed[[".data"]] <- x[, -which(names(x) == .id)]
}
## NOTE:
# 01.03.2019: Using do.call(foreman, args_needed) would be more elegant but is much
# much much! slower (especially for larger data sets).
#
# 15.05.2019: Apparently do.call(foreman, args_needed) is not that bad
# after all. I did several comparisons using microbenchmark
# but there was no speed difference (anymore?!). When I compared and
# thought that do.call is slow I fixed other parts of
# foreman as well...maybe they had been the real culprit.
# So we use do.call again, as it avoids redundancy
do.call(foreman, args_needed)
})
} else if(any(class(.data) == "list")) {
out <- lapply(.data, function(x) {
args_needed[[".data"]] <- x
do.call(foreman, args_needed)
})
if(is.null(names(.data))) {
names(out) <- paste0("Data_", 1:length(out))
} else {
names(out) <- names(.data)
}
} else {
out <- do.call(foreman, args_needed)
}
## Set class for output
# See the details section of ?UseMethod() to learn how method dispatch works
# for objects with multiple classes
if(inherits(.data, "list") | !is.null(.id)) {
# Sometimes the original (unstandardized) pooled data set is required
# (e.g. for permutation and permutation based tests).
# By convention the original, pooled dataset is therefore added to the first
# element of "out" (= results for the first group/dataset)!
# If ".data" was a list of data they are combined to one pooled dataset
# If ".data" was originally pooled and subsequently split by ".id"
# The original unsplit data is returned.
out[[1]]$Information$Data_pooled <- if(inherits(.data, "list")) {
# Clean and order columns according to the first data set
data_cleaned <- lapply(.data, function(x) {
# Order data according to the ordering of the measurement model; delete
# all columns that are not needed
x <- x[, setdiff(colnames(model_original$measurement), model_original$vars_attached_to_2nd)]
x
})
# Combine
data_pooled <- do.call(rbind, data_cleaned)
data_pooled <- as.data.frame(data_pooled)
# data_pooled[, "id"] <- rep(names(out), times = sapply(.data, nrow))
data_pooled
} else {
.data
}
## Add second order approach to $Information
if(any(model_original$construct_order == "Second order")) {
out <- lapply(out, function(x){
x$Information$Approach_2ndorder <- .approach_2ndorder
x$Information$Model_original <- model_original
x
})
} else {
out <- lapply(out, function(x){
x$Information$Approach_2ndorder <- NA
x
})
}
## Set class
class(out) <- c("cSEMResults", "cSEMResults_multi")
## Second order (2/3 stage approach)
if(any(model_original$construct_order == "Second order") &&
args$.approach_2ndorder %in% c("2stage", "mixed")) {
### Second step
out <- lapply(out, function(x) {
calculate2ndStage(model_original, x, args_needed, .approach_2ndorder)
})
## Set class
class(out) <- c("cSEMResults", "cSEMResults_multi", "cSEMResults_2ndorder")
}
} else if(any(model_original$construct_order == "Second order") &&
args$.approach_2ndorder %in% c("2stage", "mixed")) {
### Second step
out <- calculate2ndStage(model_original, out, args_needed, .approach_2ndorder)
} else {
## Add second order approach to $Information
if(any(model_original$construct_order == "Second order")) {
out$Information$Approach_2ndorder <- .approach_2ndorder
out$Information$Model_original <- model_original
} else {
out$Information$Approach_2ndorder <- NA
}
class(out) <- c("cSEMResults", "cSEMResults_default")
}
## Resample if requested:
if(.resample_method != "none") {
out <- resamplecSEMResults(
.object = out,
.resample_method = .resample_method,
.resample_method2 = .resample_method2,
.R = .R,
.R2 = .R2,
.handle_inadmissibles = .handle_inadmissibles,
.user_funs = .user_funs,
.eval_plan = .eval_plan,
.force = FALSE,
.seed = .seed,
.sign_change_option = .sign_change_option
)
}
return(out)
}
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