Nothing
R/zz_datasets.R
In cSEM: Composite-Based Structural Equation Modeling
#' Data: satisfaction
#'
#' This dataset contains the variables from a customer satisfaction study of
#' a Spanish credit institution on 250 customers. The data is identical to
#' the dataset provided by the \href{https://github.com/gastonstat/plspm/}{plspm} package
#' but with the last column (`gender`) removed. If you are looking for the original
#' dataset use the [satisfaction_gender] dataset.
#'
#' @docType data
#'
#' @description A data frame with 250 observations and 27 variables.
#' Variables from 1 to 27 refer to six latent concepts: \code{IMAG}=Image,
#' \code{EXPE}=Expectations, \code{QUAL}=Quality, \code{VAL}=Value,
#' \code{SAT}=Satisfaction, and \code{LOY}=Loyalty.
#' \describe{
#' \item{imag1-imag5}{Indicators attached to concept `IMAG` which is supposed to
#' capture aspects such as the institutions reputation,
#' trustworthiness, seriousness, solidness, and caring
#' about customer.}
#' \item{expe1-expe5}{Indicators attached to concept `EXPE` which is supposed to
#' capture aspects concerning products and
#' services provided, customer service, providing solutions,
#' and expectations for the overall quality.}
#' \item{qual1-qual5}{Indicators attached to concept `QUAL` which is supposed to
#' capture aspects concerning reliability of products and services,
#' the range of products and services, personal advice,
#' and overall perceived quality.}
#' \item{val1-val4}{Indicators attached to concept `VAL` which is supposed to
#' capture aspects related to beneficial services and
#' products, valuable investments, quality relative to
#' price, and price relative to quality.}
#' \item{sat1-sat4}{Indicators attached to concept `SAT` which is supposed to
#' capture aspects concerning overall rating of satisfaction,
#' fulfillment of expectations, satisfaction relative to
#' other banks, and performance relative to customer's
#' ideal bank.}
#' \item{loy1-loy4}{Indicators attached to concept `LOY` which is supposed to
#' capture aspects concerning propensity to choose the
#' same bank again, propensity to switch to other bank,
#' intention to recommend the bank to friends,
#' and the sense of loyalty.}
#' }
#'
#'
#'
#' @source The \href{https://github.com/gastonstat/plspm/}{plspm} package (version 0.4.9).
#' Original source according to \pkg{plspm}:
#' "Laboratory of Information Analysis and Modeling (LIAM).
#' Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya".
"satisfaction"
#' Data: satisfaction including gender
#'
#' This data set contains the variables from a customer satisfaction study of
#' a Spanish credit institution on 250 customers. The data is taken from the
#' \href{https://github.com/gastonstat/plspm/}{plspm} package. For convenience,
#' there is a version of the dataset with the last column (`gender`) removed: [satisfaction].
#'
#' @docType data
#'
#' @description A data frame with 250 observations and 28 variables.
#' Variables from 1 to 27 refer to six latent concepts: \code{IMAG}=Image,
#' \code{EXPE}=Expectations, \code{QUAL}=Quality, \code{VAL}=Value,
#' \code{SAT}=Satisfaction, and \code{LOY}=Loyalty.
#' \describe{
#' \item{imag1-imag5}{Indicators attached to concept `IMAG` which is supposed to
#' capture aspects such as the institutions reputation,
#' trustworthiness, seriousness, solidness, and caring
#' about customer.}
#' \item{expe1-expe5}{Indicators attached to concept `EXPE` which is supposed to
#' capture aspects concerning products and
#' services provided, customer service, providing solutions,
#' and expectations for the overall quality.}
#' \item{qual1-qual5}{Indicators attached to concept `QUAL` which is supposed to
#' capture aspects concerning reliability of products and services,
#' the range of products and services, personal advice,
#' and overall perceived quality.}
#' \item{val1-val4}{Indicators attached to concept `VAL` which is supposed to
#' capture aspects related to beneficial services and
#' products, valuable investments, quality relative to
#' price, and price relative to quality.}
#' \item{sat1-sat4}{Indicators attached to concept `SAT` which is supposed to
#' capture aspects concerning overall rating of satisfaction,
#' fulfillment of expectations, satisfaction relative to
#' other banks, and performance relative to customer's
#' ideal bank.}
#' \item{loy1-loy4}{Indicators attached to concept `LOY` which is supposed to
#' capture aspects concerning propensity to choose the
#' same bank again, propensity to switch to other bank,
#' intention to recommend the bank to friends,
#' and the sense of loyalty.}
#' \item{gender}{The sex of the respondent.}
#' }
#' @source The \href{https://github.com/gastonstat/plspm/}{plspm} package (version 0.4.9).
#' Original source according to \pkg{plspm}:
#' "Laboratory of Information Analysis and Modeling (LIAM).
#' Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya".
"satisfaction_gender"
#' Data: threecommonfactors
#'
#' A dataset containing 500 standardized observations on 9 indicator generated from a
#' population model with three concepts modeled as common factors.
#'
#' @docType data
#'
#' @format A matrix with 500 rows and 9 variables:
#' \describe{
#' \item{y11-y13}{Indicators attached to the first common factor (`eta1`).
#' Population loadings are: 0.7; 0.7; 0.7}
#' \item{y21-y23}{Indicators attached to the second common factor (`eta2`).
#' Population loadings are: 0.5; 0.7; 0.8}
#' \item{y31-y33}{Indicators attached to the third common factor (`eta3`).
#' Population loadings are: 0.8; 0.75; 0.7}
#' }
#'
#' The model is:
#' \deqn{`eta2` = gamma1 * `eta1` + zeta1}
#' \deqn{`eta3` = gamma2 * `eta1` + beta * `eta2` + zeta2}
#'
#' with population values `gamma1` = 0.6, `gamma2` = 0.4 and `beta` = 0.35.
#' @examples
#' #============================================================================
#' # Correct model (the model used to generate the data)
#' #============================================================================
#' model_correct <- "
#' # Structural model
#' eta2 ~ eta1
#' eta3 ~ eta1 + eta2
#'
#' # Measurement model
#' eta1 =~ y11 + y12 + y13
#' eta2 =~ y21 + y22 + y23
#' eta3 =~ y31 + y32 + y33
#' "
#'
#' a <- csem(threecommonfactors, model_correct)
#'
#' ## The overall model fit is evidently almost perfect:
#' testOMF(a, .R = 30) # .R = 30 to speed up the example
"threecommonfactors"
#' Data: Second order common factor of composites
#'
#' A dataset containing 500 standardized observations on 19 indicator generated from a
#' population model with 6 concepts, three of which (`c1-c3`) are composites
#' forming a second order common factor (`c4`). The remaining two (`eta1`, `eta2`)
#' are concepts modeled as common factors .
#'
#' @docType data
#'
#' @format A matrix with 500 rows and 19 variables:
#' \describe{
#' \item{y11-y12}{Indicators attached to `c1`.
#' Population weights are: 0.8; 0.4.
#' Population loadings are: 0.925; 0.65}
#' \item{y21-y24}{Indicators attached to `c2`.
#' Population weights are: 0.5; 0.3; 0.2; 0.4.
#' Population loadings are: 0.804; 0.68; 0.554; 0.708}
#' \item{y31-y38}{Indicators attached to `c3`.
#' Population weights are: 0.3; 0.3; 0.1; 0.1; 0.2; 0.3; 0.4; 0.2.
#' Population loadings are: 0.496; 0.61; 0.535; 0.391; 0.391; 0.6; 0.5285; 0.53}
#' \item{y41-y43}{Indicators attached to `eta1`.
#' Population loadings are: 0.8; 0.7; 0.7}
#' \item{y51-y53}{Indicators attached to `eta1`.
#' Population loadings are: 0.8; 0.8; 0.7}
#' }
#'
#' The model is:
#' \deqn{`c4` = gamma1 * `eta1` + zeta1}
#' \deqn{`eta2` = gamma2 * `eta1` + beta * `c4` + zeta2}
#'
#' with population values `gamma1` = 0.6, `gamma2` = 0.4 and `beta` = 0.35.
#' The second order common factor is
#' \deqn{`c4` = lambdac1 * `c1` + lambdac2 * `c2` + lambdac3 * `c3` + epsilon}
"dgp_2ndorder_cf_of_c"
#' Data: political democracy
#'
#' The Industrialization and Political Democracy dataset. This dataset is
#' used throughout Bollen's 1989 book (see pages 12, 17, 36 in chapter 2, pages
#' 228 and following in chapter 7, pages 321 and following in chapter 8;
#' \insertCite{Bollen1989;textual}{cSEM}).
#' The dataset contains various measures of political democracy and
#' industrialization in developing countries.
#'
#' @docType data
#'
#' @format A data frame of 75 observations of 11 variables.
#' \describe{
#' \item{\code{y1}}{Expert ratings of the freedom of the press in 1960}
#' \item{\code{y2}}{The freedom of political opposition in 1960}
#' \item{\code{y3}}{The fairness of elections in 1960}
#' \item{\code{y4}}{The effectiveness of the elected legislature in 1960}
#' \item{\code{y5}}{Expert ratings of the freedom of the press in 1965}
#' \item{\code{y6}}{The freedom of political opposition in 1965}
#' \item{\code{y7}}{The fairness of elections in 1965}
#' \item{\code{y8}}{The effectiveness of the elected legislature in 1965}
#' \item{\code{x1}}{The gross national product (GNP) per capita in 1960}
#' \item{\code{x2}}{The inanimate energy consumption per capita in 1960}
#' \item{\code{x3}}{The percentage of the labor force in industry in 1960}
#' }
#'
#' @source The \href{https://lavaan.ugent.be/}{lavaan} package (version 0.6-3).
#' @references
#' \insertAllCited{}
#' @examples
#' #============================================================================
#' # Example is taken from the lavaan website
#' #============================================================================
#' # Note: example is modified. Across-block correlations are removed
#' model <- "
#' # Measurement model
#' ind60 =~ x1 + x2 + x3
#' dem60 =~ y1 + y2 + y3 + y4
#' dem65 =~ y5 + y6 + y7 + y8
#'
#' # Regressions / Path model
#' dem60 ~ ind60
#' dem65 ~ ind60 + dem60
#'
#' # residual correlations
#' y2 ~~ y4
#' y6 ~~ y8
#' "
#'
#' aa <- csem(PoliticalDemocracy, model)
"PoliticalDemocracy"
#' Data: Anime
#'
#' The data set for the example on \href{https://github.com/ISS-Analytics/pls-predict/}{github.com/ISS-Analytics/pls-predict/}
#' with irrelevant variables removed.
#'
#' @docType data
#'
#' @description A data frame with 183 observations and 13 variables.
#'
#' @source Original source: \href{https://github.com/ISS-Analytics/pls-predict/}{github.com/ISS-Analytics/pls-predict/}
"Anime"
#' Data: Russett
#'
#' The dataset was initially compiled by \insertCite{Russett1964;textual}{cSEM},
#' discussed and reprinted by \insertCite{Gifi1990;textual}{cSEM},
#' and partially transformed by \insertCite{Tenenhaus2011;textual}{cSEM}.
#' It is also used in \insertCite{Henseler2021;textual}{cSEM} for demonstration
#' purposes.
#'
#' @format A data frame containing the following variables for 47 countries:
#' \describe{
#' \item{\code{gini}}{The Gini index of concentration}
#' \item{\code{farm}}{The percentage of landholders who collectively occupy
#' one-half of all the agricultural land (starting with the farmers
#' with the smallest plots of land and working toward the largest)}
#' \item{\code{rent}}{The percentage of the total number of farms that rent all
#' their land. Transformation: ln (x + 1)}
#' \item{\code{gnpr}}{The 1955 gross national product per capita in U.S. dollars.
#' Transformation: ln (x)}
#' \item{\code{labo}}{The percentage of the labor force employed in agriculture.
#' Transformation: ln (x)}
#' \item{\code{inst}}{Instability of personnel based on the term of office of the
#' chief executive. Transformation: exp (x - 16.3)}
#' \item{\code{ecks}}{The total number of politically motivated violent incidents,
#' from plots to protracted guerrilla warfare. Transformation: ln (x + 1)}
#' \item{\code{deat}}{The number of people killed as a result of internal group
#' violence per 1,000,000 people. Transformation: ln (x + 1)}
#' \item{\code{stab}}{One if the country has a stable democracy, and zero otherwise}
#' \item{\code{dict}}{One if the country experiences a dictatorship, and zero otherwise}
#' }
#'
#' @docType data
#'
#' @description A data frame containing 10 variables with 47 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_Russett="
#' # Composite model
#' AgrIneq <~ gini + farm + rent
#' IndDev <~ gnpr + labo
#' PolInst <~ inst + ecks + deat + stab + dict
#'
#' # Structural model
#' PolInst ~ AgrIneq + IndDev
#' "
#'
#' out <- csem(.data = Russett, .model = model_Russett,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' @references
#' \insertAllCited{}
#'
#' @source From: \insertCite{Henseler2021;textual}{cSEM}
"Russett"
#' Data: ITFlex
#'
#' The dataset was studied by \insertCite{Benitez2018;textual}{cSEM} and is used in
#' \insertCite{Henseler2021;textual}{cSEM} for demonstration purposes, see the
#' corresponding tutorial.
#' All questionnaire items are measured on a 5-point scale.
#'
#' @format A data frame containing the following variables:
#' \describe{
#' \item{\code{ITCOMP1}}{Software applications can be easily transported and
#' used across multiple platforms.}
#' \item{\code{ITCOMP2}}{Our firm provides multiple interfaces or entry points
#' (e.g., web access) for external end users.}
#' \item{\code{ITCOMP3}}{Our firm establishes corporate rules and standards for
#' hardware and operating systems to ensure platform compatibility.}
#' \item{\code{ITCOMP4}}{Data captured in one part of our organization are
#' immediately available to everyone in the firm.}
#' \item{\code{ITCONN1}}{Our organization has electronic links and connections
#' throughout the entire firm.}
#' \item{\code{ITCONN2}}{Our firm is linked to business partners through
#' electronic channels (e.g., websites, e-mail, wireless devices, electronic data interchange).}
#' \item{\code{ITCONN3}}{All remote, branch, and mobile offices are connected to
#' the central office.}
#' \item{\code{ITCONN4}}{There are very few identifiable communications
#' bottlenecks within our firm.}
#' \item{\code{MOD1}}{Our firm possesses a great speed in developing new
#' business applications or modifying existing applications.}
#' \item{\code{MOD2}}{Our corporate database is able to communicate in
#' several different protocols.}
#' \item{\code{MOD3}}{Reusable software modules are widely used in new
#' systems development.}
#' \item{\code{MOD4}}{IT personnel use object-oriented and prepackaged
#' modular tools to create software applications.}
#' \item{\code{ITPSF1}}{Our IT personnel have the ability to work effectively in
#' cross-functional teams.}
#' \item{\code{ITPSF2}}{Our IT personnel are able to interpret business problems
#' and develop appropriate technical solutions.}
#' \item{\code{ITPSF3}}{Our IT personnel are self-directed and proactive.}
#' \item{\code{ITPSF4}}{Our IT personnel are knowledgeable about the key
#' success factors in our firm.}
#' }
#'
#' @docType data
#'
#' @description A data frame containing 16 variables with 100 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_IT_Fex="
#' # Composite models
#' ITComp <~ ITCOMP1 + ITCOMP2 + ITCOMP3 + ITCOMP4
#' Modul <~ MOD1 + MOD2 + MOD3 + MOD4
#' ITConn <~ ITCONN1 + ITCONN2 + ITCONN3 + ITCONN4
#' ITPers <~ ITPSF1 + ITPSF2 + ITPSF3 + ITPSF4
#'
#' # Saturated structural model
#' ITPers ~ ITComp + Modul + ITConn
#' Modul ~ ITComp + ITConn
#' ITConn ~ ITComp
#' "
#'
#'out <- csem(.data = ITFlex, .model = model_IT_Fex,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06,
#' .PLS_ignore_structural_model = TRUE)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The data was collected through a survey by \insertCite{Benitez2018;textual}{cSEM}.
"ITFlex"
#' Data: LancelotMiltgenetal2016
#'
#' The data was analysed by \insertCite{Lancelot-Miltgen2016;textual}{cSEM}
#' to study young consumers’ adoption intentions of a location tracker technology
#' in the light of privacy concerns. It is also used in
#' \insertCite{Henseler2021;textual}{cSEM} for demonstration purposes, see the
#' corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 10 variables with 1090 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_Med <- "
#' # Reflective measurement model
#' Trust =~ trust1 + trust2
#' PrCon =~ privcon1 + privcon2 + privcon3 + privcon4
#' Risk =~ risk1 + risk2 + risk3
#' Int =~ intent1 + intent2
#'
#' # Structural model
#' Int ~ Trust + PrCon + Risk
#' Risk ~ Trust + PrCon
#' Trust ~ PrCon
#' "
#'
#' out <- csem(.data = LancelotMiltgenetal2016, .model = model_Med,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' @references
#' \insertAllCited{}
#'
#' @source This data has been collected through a cooperation with the European Commission
#' Joint Research Center Institute for Prospective Technological Studies, contract
#' “Young People and Emerging Digital Services: An Exploratory Survey on Motivations,
#' Perceptions, and Acceptance of Risk” (EC JRC Contract IPTS No: 150876-2007 F1ED-FR).
"LancelotMiltgenetal2016"
#' Data: Yooetal2000
#'
#' The data is simulated and has the identical correlation matrix as the data
#' that was analysed by \insertCite{Yoo2000;textual}{cSEM}
#' to examine how five elements of the marketing mix, namely price, store
#' image, distribution intensity, advertising spending, and price deals, are
#' related to the so-called dimensions of brand equity, i.e., perceived brand
#' quality, brand loyalty, and brand awareness/associations. It is also used in
#' \insertCite{Henseler2017;textual}{cSEM} and \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 34 variables with 569 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_HOC="
#' # Measurement models FOC
#' PR =~ PR1 + PR2 + PR3
#' IM =~ IM1 + IM2 + IM3
#' DI =~ DI1 + DI2 + DI3
#' AD =~ AD1 + AD2 + AD3
#' DL =~ DL1 + DL2 + DL3
#' AA =~ AA1 + AA2 + AA3 + AA4 + AA5 + AA6
#' LO =~ LO1 + LO3
#' QL =~ QL1 + QL2 + QL3 + QL4 + QL5 + QL6
#'
#' # Composite model for SOC
#' BR <~ QL + LO + AA
#'
#' # Structural model
#' BR~ PR + IM + DI + AD + DL
#' "
#'
#' out <- csem(.data = Yooetal2000, .model = model_HOC,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source Simulated data with the same correlation matrix as the data studied
#' by \insertCite{Yoo2000;textual}{cSEM}.
"Yooetal2000"
#' Data: Switching
#'
#' The data contains variables about the consumers’ intention to switch a
#' service provider. It is also used in \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 26 variables with 767 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_Int <-"
#' # Measurement models
#' INV =~ INV1 + INV2 + INV3 +INV4
#' SAT =~ SAT1 + SAT2 + SAT3
#' INT =~ INT1 + INT2
#'
#' # Structural model containing an interaction term.
#' INT ~ INV + SAT + INV.SAT
#' "
#'
#' out <- csem(.data = Switching, .model = model_Int,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided by Jörg Henseler.
"Switching"
#' Data: Benitezetal2020
#'
#' The simulated data contains variables about the social executive and employee behavior.
#' Moreover, it contains variables about the social media capability and business performance.
#' The dataset was used as an illustrative example in \insertCite{Benitez2020;textual}{cSEM}.
#'
#' @docType data
#'
#' @description A data frame containing 22 variables with 300 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Benitez et al. (2020)
#' #============================================================================
#' model_Benitez <-"
#' # Reflective measurement models# Reflective measurement models
#' SEXB =~ SEXB1 + SEXB2 + SEXB3 +SEXB4
#' SEMB =~ SEMB1 + SEMB2 + SEMB3 + SEMB4
#'
#' # Composite models
#' SMC <~ SMC1 + SMC2 + SMC3 + SMC4
#' BPP <~ BPP1 + BPP2 + BPP3 + BPP4 + BPP5
#'
#' # Control variables
#' FS<~ FirmSize
#' Ind <~ Industry1 + Industry2 + Industry3
#'
#' # Structural model
#' SMC ~ SEXB + SEMB
#' BPP ~ SMC + Ind + FS
#' "
#'
#' out <- csem(.data = Benitezetal2020, .model = model_Benitez,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided as supplementary material by \insertCite{Benitez2020;textual}{cSEM}.
"Benitezetal2020"
#' Data: BergamiBagozzi2000
#'
#' The dataset contains 22 variables and originates
#' from a larger survey among South Korean employees conducted and
#' reported by \insertCite{Bergami2000;textual}{cSEM}. It is
#' also used in \insertCite{Hwang2004;textual}{cSEM} and
#' \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 22 variables with 305 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_Bergami_Bagozzi_Henseler="
#' # Measurement models
#' OrgPres =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8
#' OrgIden =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6
#' AffLove =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7
#' AffJoy =~ orgcmt5 + orgcmt8
#' Gender <~ gender
#'
#' # Structural model
#' OrgIden ~ OrgPres
#' AffLove ~ OrgPres + OrgIden + Gender
#' AffJoy ~ OrgPres + OrgIden + Gender
#' "
#'
#' out <- csem(.data = BergamiBagozzi2000,
#' .model = model_Bergami_Bagozzi_Henseler,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' #============================================================================
#' # Example is taken from Hwang et al. (2004)
#' #============================================================================
#'
#' model_Bergami_Bagozzi_Hwang="
#' # Measurement models
#' OrgPres =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8
#' OrgIden =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6
#' AffJoy =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7
#' AffLove =~ orgcmt5 + orgcmt6 + orgcmt8
#'
#' # Structural model
#' OrgIden ~ OrgPres
#' AffLove ~ OrgIden
#' AffJoy ~ OrgIden"
#'
#'out_Hwang <- csem(.data = BergamiBagozzi2000,
#' .model = model_Bergami_Bagozzi_Hwang,
#' .approach_weights = "GSCA",
#' .disattenuate = FALSE,
#' .id = "gender",
#' .tolerance = 1e-06)
#'
#'
#' @references
#' \insertAllCited{}
#'
#' @source Survey among South Korean employees conducted and
#' reported by \insertCite{Bergami2000;textual}{cSEM}.
"BergamiBagozzi2000"
#' Data: Summers
#'
#' The indicator correlation matrix for a modified version of \insertCite{Summers1965;textual}{cSEM}
#' model. All constructs are modeled as composites.
#'
#' @docType data
#'
#' @description A (18 x 18) indicator correlation matrix.
#'
#' @examples
#'
#' require(cSEM)
#'
#' model <- "
#' ETA1 ~ ETA2 + XI1 + XI2
#' ETA2 ~ ETA1 + XI3 +XI4
#'
#' ETA1 ~~ ETA2
#'
#' XI1 <~ x1 + x2 + x3
#' XI2 <~ x4 + x5 + x6
#' XI3 <~ x7 + x8 + x9
#' XI4 <~ x10 + x11 + x12
#' ETA1 <~ y1 + y2 + y3
#' ETA2 <~ y4 + y5 + y6
#' "
#'
#' ## Generate data
#' summers_dat <- MASS::mvrnorm(n = 300, mu = rep(0, 18),
#' Sigma = Sigma_Summers_composites, empirical = TRUE)
#'
#' ## Estimate
#' res <- csem(.data = summers_dat, .model = model) # inconsistent
#'
#' ##
#' # 2SLS
#' res_2SLS <- csem(.data = summers_dat, .model = model, .approach_paths = "2SLS",
#' .instruments = list(ETA1 = c('XI1', 'XI2', 'XI3', 'XI4'),
#' ETA2 = c('XI1', 'XI2', 'XI3', 'XI4'))
#')
#'
#' @references
#' \insertAllCited{}
#'
#' @source Own calculation based on \insertCite{Dijkstra2015;textual}{cSEM}.
"Sigma_Summers_composites"
#' Data: SQ
#'
#' The data comes from a European manufacturer of durable consumer goods and was
#' studied by \insertCite{Bliemel2004;textual}{cSEM} who focused on service quality.
#' It is also used in \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 23 variables with 411 observations. The original
#' indicators were measured on a 6-point scale. In this version of the dataset,
#' the indicators are scaled to be between 0 and 100.
#'
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided by Jörg Henseler.
"SQ"
Try the cSEM package in your browser
Any scripts or data that you put into this service are public.
cSEM documentation built on Nov. 25, 2022, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Data: satisfaction
#'
#' This dataset contains the variables from a customer satisfaction study of
#' a Spanish credit institution on 250 customers. The data is identical to
#' the dataset provided by the \href{https://github.com/gastonstat/plspm/}{plspm} package
#' but with the last column (`gender`) removed. If you are looking for the original
#' dataset use the [satisfaction_gender] dataset.
#'
#' @docType data
#'
#' @description A data frame with 250 observations and 27 variables.
#' Variables from 1 to 27 refer to six latent concepts: \code{IMAG}=Image,
#' \code{EXPE}=Expectations, \code{QUAL}=Quality, \code{VAL}=Value,
#' \code{SAT}=Satisfaction, and \code{LOY}=Loyalty.
#' \describe{
#' \item{imag1-imag5}{Indicators attached to concept `IMAG` which is supposed to
#' capture aspects such as the institutions reputation,
#' trustworthiness, seriousness, solidness, and caring
#' about customer.}
#' \item{expe1-expe5}{Indicators attached to concept `EXPE` which is supposed to
#' capture aspects concerning products and
#' services provided, customer service, providing solutions,
#' and expectations for the overall quality.}
#' \item{qual1-qual5}{Indicators attached to concept `QUAL` which is supposed to
#' capture aspects concerning reliability of products and services,
#' the range of products and services, personal advice,
#' and overall perceived quality.}
#' \item{val1-val4}{Indicators attached to concept `VAL` which is supposed to
#' capture aspects related to beneficial services and
#' products, valuable investments, quality relative to
#' price, and price relative to quality.}
#' \item{sat1-sat4}{Indicators attached to concept `SAT` which is supposed to
#' capture aspects concerning overall rating of satisfaction,
#' fulfillment of expectations, satisfaction relative to
#' other banks, and performance relative to customer's
#' ideal bank.}
#' \item{loy1-loy4}{Indicators attached to concept `LOY` which is supposed to
#' capture aspects concerning propensity to choose the
#' same bank again, propensity to switch to other bank,
#' intention to recommend the bank to friends,
#' and the sense of loyalty.}
#' }
#'
#'
#'
#' @source The \href{https://github.com/gastonstat/plspm/}{plspm} package (version 0.4.9).
#' Original source according to \pkg{plspm}:
#' "Laboratory of Information Analysis and Modeling (LIAM).
#' Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya".
"satisfaction"
#' Data: satisfaction including gender
#'
#' This data set contains the variables from a customer satisfaction study of
#' a Spanish credit institution on 250 customers. The data is taken from the
#' \href{https://github.com/gastonstat/plspm/}{plspm} package. For convenience,
#' there is a version of the dataset with the last column (`gender`) removed: [satisfaction].
#'
#' @docType data
#'
#' @description A data frame with 250 observations and 28 variables.
#' Variables from 1 to 27 refer to six latent concepts: \code{IMAG}=Image,
#' \code{EXPE}=Expectations, \code{QUAL}=Quality, \code{VAL}=Value,
#' \code{SAT}=Satisfaction, and \code{LOY}=Loyalty.
#' \describe{
#' \item{imag1-imag5}{Indicators attached to concept `IMAG` which is supposed to
#' capture aspects such as the institutions reputation,
#' trustworthiness, seriousness, solidness, and caring
#' about customer.}
#' \item{expe1-expe5}{Indicators attached to concept `EXPE` which is supposed to
#' capture aspects concerning products and
#' services provided, customer service, providing solutions,
#' and expectations for the overall quality.}
#' \item{qual1-qual5}{Indicators attached to concept `QUAL` which is supposed to
#' capture aspects concerning reliability of products and services,
#' the range of products and services, personal advice,
#' and overall perceived quality.}
#' \item{val1-val4}{Indicators attached to concept `VAL` which is supposed to
#' capture aspects related to beneficial services and
#' products, valuable investments, quality relative to
#' price, and price relative to quality.}
#' \item{sat1-sat4}{Indicators attached to concept `SAT` which is supposed to
#' capture aspects concerning overall rating of satisfaction,
#' fulfillment of expectations, satisfaction relative to
#' other banks, and performance relative to customer's
#' ideal bank.}
#' \item{loy1-loy4}{Indicators attached to concept `LOY` which is supposed to
#' capture aspects concerning propensity to choose the
#' same bank again, propensity to switch to other bank,
#' intention to recommend the bank to friends,
#' and the sense of loyalty.}
#' \item{gender}{The sex of the respondent.}
#' }
#' @source The \href{https://github.com/gastonstat/plspm/}{plspm} package (version 0.4.9).
#' Original source according to \pkg{plspm}:
#' "Laboratory of Information Analysis and Modeling (LIAM).
#' Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya".
"satisfaction_gender"
#' Data: threecommonfactors
#'
#' A dataset containing 500 standardized observations on 9 indicator generated from a
#' population model with three concepts modeled as common factors.
#'
#' @docType data
#'
#' @format A matrix with 500 rows and 9 variables:
#' \describe{
#' \item{y11-y13}{Indicators attached to the first common factor (`eta1`).
#' Population loadings are: 0.7; 0.7; 0.7}
#' \item{y21-y23}{Indicators attached to the second common factor (`eta2`).
#' Population loadings are: 0.5; 0.7; 0.8}
#' \item{y31-y33}{Indicators attached to the third common factor (`eta3`).
#' Population loadings are: 0.8; 0.75; 0.7}
#' }
#'
#' The model is:
#' \deqn{`eta2` = gamma1 * `eta1` + zeta1}
#' \deqn{`eta3` = gamma2 * `eta1` + beta * `eta2` + zeta2}
#'
#' with population values `gamma1` = 0.6, `gamma2` = 0.4 and `beta` = 0.35.
#' @examples
#' #============================================================================
#' # Correct model (the model used to generate the data)
#' #============================================================================
#' model_correct <- "
#' # Structural model
#' eta2 ~ eta1
#' eta3 ~ eta1 + eta2
#'
#' # Measurement model
#' eta1 =~ y11 + y12 + y13
#' eta2 =~ y21 + y22 + y23
#' eta3 =~ y31 + y32 + y33
#' "
#'
#' a <- csem(threecommonfactors, model_correct)
#'
#' ## The overall model fit is evidently almost perfect:
#' testOMF(a, .R = 30) # .R = 30 to speed up the example
"threecommonfactors"
#' Data: Second order common factor of composites
#'
#' A dataset containing 500 standardized observations on 19 indicator generated from a
#' population model with 6 concepts, three of which (`c1-c3`) are composites
#' forming a second order common factor (`c4`). The remaining two (`eta1`, `eta2`)
#' are concepts modeled as common factors .
#'
#' @docType data
#'
#' @format A matrix with 500 rows and 19 variables:
#' \describe{
#' \item{y11-y12}{Indicators attached to `c1`.
#' Population weights are: 0.8; 0.4.
#' Population loadings are: 0.925; 0.65}
#' \item{y21-y24}{Indicators attached to `c2`.
#' Population weights are: 0.5; 0.3; 0.2; 0.4.
#' Population loadings are: 0.804; 0.68; 0.554; 0.708}
#' \item{y31-y38}{Indicators attached to `c3`.
#' Population weights are: 0.3; 0.3; 0.1; 0.1; 0.2; 0.3; 0.4; 0.2.
#' Population loadings are: 0.496; 0.61; 0.535; 0.391; 0.391; 0.6; 0.5285; 0.53}
#' \item{y41-y43}{Indicators attached to `eta1`.
#' Population loadings are: 0.8; 0.7; 0.7}
#' \item{y51-y53}{Indicators attached to `eta1`.
#' Population loadings are: 0.8; 0.8; 0.7}
#' }
#'
#' The model is:
#' \deqn{`c4` = gamma1 * `eta1` + zeta1}
#' \deqn{`eta2` = gamma2 * `eta1` + beta * `c4` + zeta2}
#'
#' with population values `gamma1` = 0.6, `gamma2` = 0.4 and `beta` = 0.35.
#' The second order common factor is
#' \deqn{`c4` = lambdac1 * `c1` + lambdac2 * `c2` + lambdac3 * `c3` + epsilon}
"dgp_2ndorder_cf_of_c"
#' Data: political democracy
#'
#' The Industrialization and Political Democracy dataset. This dataset is
#' used throughout Bollen's 1989 book (see pages 12, 17, 36 in chapter 2, pages
#' 228 and following in chapter 7, pages 321 and following in chapter 8;
#' \insertCite{Bollen1989;textual}{cSEM}).
#' The dataset contains various measures of political democracy and
#' industrialization in developing countries.
#'
#' @docType data
#'
#' @format A data frame of 75 observations of 11 variables.
#' \describe{
#' \item{\code{y1}}{Expert ratings of the freedom of the press in 1960}
#' \item{\code{y2}}{The freedom of political opposition in 1960}
#' \item{\code{y3}}{The fairness of elections in 1960}
#' \item{\code{y4}}{The effectiveness of the elected legislature in 1960}
#' \item{\code{y5}}{Expert ratings of the freedom of the press in 1965}
#' \item{\code{y6}}{The freedom of political opposition in 1965}
#' \item{\code{y7}}{The fairness of elections in 1965}
#' \item{\code{y8}}{The effectiveness of the elected legislature in 1965}
#' \item{\code{x1}}{The gross national product (GNP) per capita in 1960}
#' \item{\code{x2}}{The inanimate energy consumption per capita in 1960}
#' \item{\code{x3}}{The percentage of the labor force in industry in 1960}
#' }
#'
#' @source The \href{https://lavaan.ugent.be/}{lavaan} package (version 0.6-3).
#' @references
#' \insertAllCited{}
#' @examples
#' #============================================================================
#' # Example is taken from the lavaan website
#' #============================================================================
#' # Note: example is modified. Across-block correlations are removed
#' model <- "
#' # Measurement model
#' ind60 =~ x1 + x2 + x3
#' dem60 =~ y1 + y2 + y3 + y4
#' dem65 =~ y5 + y6 + y7 + y8
#'
#' # Regressions / Path model
#' dem60 ~ ind60
#' dem65 ~ ind60 + dem60
#'
#' # residual correlations
#' y2 ~~ y4
#' y6 ~~ y8
#' "
#'
#' aa <- csem(PoliticalDemocracy, model)
"PoliticalDemocracy"
#' Data: Anime
#'
#' The data set for the example on \href{https://github.com/ISS-Analytics/pls-predict/}{github.com/ISS-Analytics/pls-predict/}
#' with irrelevant variables removed.
#'
#' @docType data
#'
#' @description A data frame with 183 observations and 13 variables.
#'
#' @source Original source: \href{https://github.com/ISS-Analytics/pls-predict/}{github.com/ISS-Analytics/pls-predict/}
"Anime"
#' Data: Russett
#'
#' The dataset was initially compiled by \insertCite{Russett1964;textual}{cSEM},
#' discussed and reprinted by \insertCite{Gifi1990;textual}{cSEM},
#' and partially transformed by \insertCite{Tenenhaus2011;textual}{cSEM}.
#' It is also used in \insertCite{Henseler2021;textual}{cSEM} for demonstration
#' purposes.
#'
#' @format A data frame containing the following variables for 47 countries:
#' \describe{
#' \item{\code{gini}}{The Gini index of concentration}
#' \item{\code{farm}}{The percentage of landholders who collectively occupy
#' one-half of all the agricultural land (starting with the farmers
#' with the smallest plots of land and working toward the largest)}
#' \item{\code{rent}}{The percentage of the total number of farms that rent all
#' their land. Transformation: ln (x + 1)}
#' \item{\code{gnpr}}{The 1955 gross national product per capita in U.S. dollars.
#' Transformation: ln (x)}
#' \item{\code{labo}}{The percentage of the labor force employed in agriculture.
#' Transformation: ln (x)}
#' \item{\code{inst}}{Instability of personnel based on the term of office of the
#' chief executive. Transformation: exp (x - 16.3)}
#' \item{\code{ecks}}{The total number of politically motivated violent incidents,
#' from plots to protracted guerrilla warfare. Transformation: ln (x + 1)}
#' \item{\code{deat}}{The number of people killed as a result of internal group
#' violence per 1,000,000 people. Transformation: ln (x + 1)}
#' \item{\code{stab}}{One if the country has a stable democracy, and zero otherwise}
#' \item{\code{dict}}{One if the country experiences a dictatorship, and zero otherwise}
#' }
#'
#' @docType data
#'
#' @description A data frame containing 10 variables with 47 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_Russett="
#' # Composite model
#' AgrIneq <~ gini + farm + rent
#' IndDev <~ gnpr + labo
#' PolInst <~ inst + ecks + deat + stab + dict
#'
#' # Structural model
#' PolInst ~ AgrIneq + IndDev
#' "
#'
#' out <- csem(.data = Russett, .model = model_Russett,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' @references
#' \insertAllCited{}
#'
#' @source From: \insertCite{Henseler2021;textual}{cSEM}
"Russett"
#' Data: ITFlex
#'
#' The dataset was studied by \insertCite{Benitez2018;textual}{cSEM} and is used in
#' \insertCite{Henseler2021;textual}{cSEM} for demonstration purposes, see the
#' corresponding tutorial.
#' All questionnaire items are measured on a 5-point scale.
#'
#' @format A data frame containing the following variables:
#' \describe{
#' \item{\code{ITCOMP1}}{Software applications can be easily transported and
#' used across multiple platforms.}
#' \item{\code{ITCOMP2}}{Our firm provides multiple interfaces or entry points
#' (e.g., web access) for external end users.}
#' \item{\code{ITCOMP3}}{Our firm establishes corporate rules and standards for
#' hardware and operating systems to ensure platform compatibility.}
#' \item{\code{ITCOMP4}}{Data captured in one part of our organization are
#' immediately available to everyone in the firm.}
#' \item{\code{ITCONN1}}{Our organization has electronic links and connections
#' throughout the entire firm.}
#' \item{\code{ITCONN2}}{Our firm is linked to business partners through
#' electronic channels (e.g., websites, e-mail, wireless devices, electronic data interchange).}
#' \item{\code{ITCONN3}}{All remote, branch, and mobile offices are connected to
#' the central office.}
#' \item{\code{ITCONN4}}{There are very few identifiable communications
#' bottlenecks within our firm.}
#' \item{\code{MOD1}}{Our firm possesses a great speed in developing new
#' business applications or modifying existing applications.}
#' \item{\code{MOD2}}{Our corporate database is able to communicate in
#' several different protocols.}
#' \item{\code{MOD3}}{Reusable software modules are widely used in new
#' systems development.}
#' \item{\code{MOD4}}{IT personnel use object-oriented and prepackaged
#' modular tools to create software applications.}
#' \item{\code{ITPSF1}}{Our IT personnel have the ability to work effectively in
#' cross-functional teams.}
#' \item{\code{ITPSF2}}{Our IT personnel are able to interpret business problems
#' and develop appropriate technical solutions.}
#' \item{\code{ITPSF3}}{Our IT personnel are self-directed and proactive.}
#' \item{\code{ITPSF4}}{Our IT personnel are knowledgeable about the key
#' success factors in our firm.}
#' }
#'
#' @docType data
#'
#' @description A data frame containing 16 variables with 100 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_IT_Fex="
#' # Composite models
#' ITComp <~ ITCOMP1 + ITCOMP2 + ITCOMP3 + ITCOMP4
#' Modul <~ MOD1 + MOD2 + MOD3 + MOD4
#' ITConn <~ ITCONN1 + ITCONN2 + ITCONN3 + ITCONN4
#' ITPers <~ ITPSF1 + ITPSF2 + ITPSF3 + ITPSF4
#'
#' # Saturated structural model
#' ITPers ~ ITComp + Modul + ITConn
#' Modul ~ ITComp + ITConn
#' ITConn ~ ITComp
#' "
#'
#'out <- csem(.data = ITFlex, .model = model_IT_Fex,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06,
#' .PLS_ignore_structural_model = TRUE)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The data was collected through a survey by \insertCite{Benitez2018;textual}{cSEM}.
"ITFlex"
#' Data: LancelotMiltgenetal2016
#'
#' The data was analysed by \insertCite{Lancelot-Miltgen2016;textual}{cSEM}
#' to study young consumers’ adoption intentions of a location tracker technology
#' in the light of privacy concerns. It is also used in
#' \insertCite{Henseler2021;textual}{cSEM} for demonstration purposes, see the
#' corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 10 variables with 1090 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2020)
#' #============================================================================
#' model_Med <- "
#' # Reflective measurement model
#' Trust =~ trust1 + trust2
#' PrCon =~ privcon1 + privcon2 + privcon3 + privcon4
#' Risk =~ risk1 + risk2 + risk3
#' Int =~ intent1 + intent2
#'
#' # Structural model
#' Int ~ Trust + PrCon + Risk
#' Risk ~ Trust + PrCon
#' Trust ~ PrCon
#' "
#'
#' out <- csem(.data = LancelotMiltgenetal2016, .model = model_Med,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' @references
#' \insertAllCited{}
#'
#' @source This data has been collected through a cooperation with the European Commission
#' Joint Research Center Institute for Prospective Technological Studies, contract
#' “Young People and Emerging Digital Services: An Exploratory Survey on Motivations,
#' Perceptions, and Acceptance of Risk” (EC JRC Contract IPTS No: 150876-2007 F1ED-FR).
"LancelotMiltgenetal2016"
#' Data: Yooetal2000
#'
#' The data is simulated and has the identical correlation matrix as the data
#' that was analysed by \insertCite{Yoo2000;textual}{cSEM}
#' to examine how five elements of the marketing mix, namely price, store
#' image, distribution intensity, advertising spending, and price deals, are
#' related to the so-called dimensions of brand equity, i.e., perceived brand
#' quality, brand loyalty, and brand awareness/associations. It is also used in
#' \insertCite{Henseler2017;textual}{cSEM} and \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 34 variables with 569 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_HOC="
#' # Measurement models FOC
#' PR =~ PR1 + PR2 + PR3
#' IM =~ IM1 + IM2 + IM3
#' DI =~ DI1 + DI2 + DI3
#' AD =~ AD1 + AD2 + AD3
#' DL =~ DL1 + DL2 + DL3
#' AA =~ AA1 + AA2 + AA3 + AA4 + AA5 + AA6
#' LO =~ LO1 + LO3
#' QL =~ QL1 + QL2 + QL3 + QL4 + QL5 + QL6
#'
#' # Composite model for SOC
#' BR <~ QL + LO + AA
#'
#' # Structural model
#' BR~ PR + IM + DI + AD + DL
#' "
#'
#' out <- csem(.data = Yooetal2000, .model = model_HOC,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source Simulated data with the same correlation matrix as the data studied
#' by \insertCite{Yoo2000;textual}{cSEM}.
"Yooetal2000"
#' Data: Switching
#'
#' The data contains variables about the consumers’ intention to switch a
#' service provider. It is also used in \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 26 variables with 767 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_Int <-"
#' # Measurement models
#' INV =~ INV1 + INV2 + INV3 +INV4
#' SAT =~ SAT1 + SAT2 + SAT3
#' INT =~ INT1 + INT2
#'
#' # Structural model containing an interaction term.
#' INT ~ INV + SAT + INV.SAT
#' "
#'
#' out <- csem(.data = Switching, .model = model_Int,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided by Jörg Henseler.
"Switching"
#' Data: Benitezetal2020
#'
#' The simulated data contains variables about the social executive and employee behavior.
#' Moreover, it contains variables about the social media capability and business performance.
#' The dataset was used as an illustrative example in \insertCite{Benitez2020;textual}{cSEM}.
#'
#' @docType data
#'
#' @description A data frame containing 22 variables with 300 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Benitez et al. (2020)
#' #============================================================================
#' model_Benitez <-"
#' # Reflective measurement models# Reflective measurement models
#' SEXB =~ SEXB1 + SEXB2 + SEXB3 +SEXB4
#' SEMB =~ SEMB1 + SEMB2 + SEMB3 + SEMB4
#'
#' # Composite models
#' SMC <~ SMC1 + SMC2 + SMC3 + SMC4
#' BPP <~ BPP1 + BPP2 + BPP3 + BPP4 + BPP5
#'
#' # Control variables
#' FS<~ FirmSize
#' Ind <~ Industry1 + Industry2 + Industry3
#'
#' # Structural model
#' SMC ~ SEXB + SEMB
#' BPP ~ SMC + Ind + FS
#' "
#'
#' out <- csem(.data = Benitezetal2020, .model = model_Benitez,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06)
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided as supplementary material by \insertCite{Benitez2020;textual}{cSEM}.
"Benitezetal2020"
#' Data: BergamiBagozzi2000
#'
#' The dataset contains 22 variables and originates
#' from a larger survey among South Korean employees conducted and
#' reported by \insertCite{Bergami2000;textual}{cSEM}. It is
#' also used in \insertCite{Hwang2004;textual}{cSEM} and
#' \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 22 variables with 305 observations.
#'
#' @examples
#' #============================================================================
#' # Example is taken from Henseler (2021)
#' #============================================================================
#' model_Bergami_Bagozzi_Henseler="
#' # Measurement models
#' OrgPres =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8
#' OrgIden =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6
#' AffLove =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7
#' AffJoy =~ orgcmt5 + orgcmt8
#' Gender <~ gender
#'
#' # Structural model
#' OrgIden ~ OrgPres
#' AffLove ~ OrgPres + OrgIden + Gender
#' AffJoy ~ OrgPres + OrgIden + Gender
#' "
#'
#' out <- csem(.data = BergamiBagozzi2000,
#' .model = model_Bergami_Bagozzi_Henseler,
#' .PLS_weight_scheme_inner = 'factorial',
#' .tolerance = 1e-06
#' )
#'
#' #============================================================================
#' # Example is taken from Hwang et al. (2004)
#' #============================================================================
#'
#' model_Bergami_Bagozzi_Hwang="
#' # Measurement models
#' OrgPres =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8
#' OrgIden =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6
#' AffJoy =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7
#' AffLove =~ orgcmt5 + orgcmt6 + orgcmt8
#'
#' # Structural model
#' OrgIden ~ OrgPres
#' AffLove ~ OrgIden
#' AffJoy ~ OrgIden"
#'
#'out_Hwang <- csem(.data = BergamiBagozzi2000,
#' .model = model_Bergami_Bagozzi_Hwang,
#' .approach_weights = "GSCA",
#' .disattenuate = FALSE,
#' .id = "gender",
#' .tolerance = 1e-06)
#'
#'
#' @references
#' \insertAllCited{}
#'
#' @source Survey among South Korean employees conducted and
#' reported by \insertCite{Bergami2000;textual}{cSEM}.
"BergamiBagozzi2000"
#' Data: Summers
#'
#' The indicator correlation matrix for a modified version of \insertCite{Summers1965;textual}{cSEM}
#' model. All constructs are modeled as composites.
#'
#' @docType data
#'
#' @description A (18 x 18) indicator correlation matrix.
#'
#' @examples
#'
#' require(cSEM)
#'
#' model <- "
#' ETA1 ~ ETA2 + XI1 + XI2
#' ETA2 ~ ETA1 + XI3 +XI4
#'
#' ETA1 ~~ ETA2
#'
#' XI1 <~ x1 + x2 + x3
#' XI2 <~ x4 + x5 + x6
#' XI3 <~ x7 + x8 + x9
#' XI4 <~ x10 + x11 + x12
#' ETA1 <~ y1 + y2 + y3
#' ETA2 <~ y4 + y5 + y6
#' "
#'
#' ## Generate data
#' summers_dat <- MASS::mvrnorm(n = 300, mu = rep(0, 18),
#' Sigma = Sigma_Summers_composites, empirical = TRUE)
#'
#' ## Estimate
#' res <- csem(.data = summers_dat, .model = model) # inconsistent
#'
#' ##
#' # 2SLS
#' res_2SLS <- csem(.data = summers_dat, .model = model, .approach_paths = "2SLS",
#' .instruments = list(ETA1 = c('XI1', 'XI2', 'XI3', 'XI4'),
#' ETA2 = c('XI1', 'XI2', 'XI3', 'XI4'))
#')
#'
#' @references
#' \insertAllCited{}
#'
#' @source Own calculation based on \insertCite{Dijkstra2015;textual}{cSEM}.
"Sigma_Summers_composites"
#' Data: SQ
#'
#' The data comes from a European manufacturer of durable consumer goods and was
#' studied by \insertCite{Bliemel2004;textual}{cSEM} who focused on service quality.
#' It is also used in \insertCite{Henseler2021;textual}{cSEM}
#' for demonstration purposes, see the corresponding tutorial.
#'
#' @docType data
#'
#' @description A data frame containing 23 variables with 411 observations. The original
#' indicators were measured on a 6-point scale. In this version of the dataset,
#' the indicators are scaled to be between 0 and 100.
#'
#'
#' @references
#' \insertAllCited{}
#'
#' @source The dataset is provided by Jörg Henseler.
"SQ"
Try the cSEM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.