Title: | Generalized Structured Component Analysis Structural Equation Modeling |
---|---|
Description: | Implementing generalized structured component analysis (GSCA) and its basic extensions, including constrained single and multiple group analysis, and second order latent variable modeling. For a comprehensive overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754). |
Authors: | Heungsun Hwang [aut] |
Maintainer: | Sunmee Kim <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.0.5 |
Built: | 2025-02-20 04:59:04 UTC |
Source: | https://github.com/quantmm/gesca |
In the user-specified structural equation model, this function calculates the total and indirect effects of (1) latent variables on other latent variables in the structural model and (2) latent variables on their indicators in the measurement model.
effectmeasures(object)
effectmeasures(object)
object |
An object of class. This can be created via the |
The bootstrapped standard errors will be provided for all total and indirect effects. The user can specify the number of bootstrap samples via the gesca.run
function. If the number of bootstrap samples is equal to 0, these standard errors will not be provided.
Four matrices whose row and column names are the same as the names of indicators and latent variables.
Hwang, H., & Takane, Y. (2014). Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling (pp.119-123). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender") summary(GSCA.group) # default output effectmeasures(GSCA.group) # total and indirect effects per group GSCA.group.nbt0 <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt = 0) effectmeasures(GSCA.group.nbt0) # no bootstrapped standard error of parameters # Second-order latent model with formative indicators SndForm <- " # Measurement model OP(0) =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8 OI(0) =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6 AC_J(0) =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7 AC_L(0) =~ orgcmt5 + orgcmt6 + orgcmt8 # Second-order latents AC(0) =: AC_J + AC_L # Structural model OI ~ OP AC ~ OI " SndFormFit <- gesca.run(SndForm, gesca.rick2, "gender") summary(SndFormFit) effectmeasures(SndFormFit) # Total/Indirect effects of latent variables on indicators # will not be provided and instead, be reported as "NULL". ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender") summary(GSCA.group) # default output effectmeasures(GSCA.group) # total and indirect effects per group GSCA.group.nbt0 <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt = 0) effectmeasures(GSCA.group.nbt0) # no bootstrapped standard error of parameters # Second-order latent model with formative indicators SndForm <- " # Measurement model OP(0) =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8 OI(0) =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6 AC_J(0) =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7 AC_L(0) =~ orgcmt5 + orgcmt6 + orgcmt8 # Second-order latents AC(0) =: AC_J + AC_L # Structural model OI ~ OP AC ~ OI " SndFormFit <- gesca.run(SndForm, gesca.rick2, "gender") summary(SndFormFit) effectmeasures(SndFormFit) # Total/Indirect effects of latent variables on indicators # will not be provided and instead, be reported as "NULL". ## End(Not run)
This function provides six model fit indices (FIT, Adjusted FIT, GFI, Standardized Root Mean Square, FIT_M, and FIT_S) for the user-specified model.
fitmeasures(object)
fitmeasures(object)
object |
An object of class. This can be created via the |
FIT
indicates how much variance of both indicators and latent variables is explained by the user-specified model. AFIT
takes into account the number of parameters when calculating the explained variance. GFI
and SRMR
measure the closeness between the sample covariances and the covariances reproduced by model parameter estimates. In GSCA, separate model fit measures for the measurement and structural models are available: FIT_M
and FIT_S
. FIT_M
assesses how much variance of indicators is explained by a measurement model, whereas FIT_S
calculates how much variance of latent variables is accounted for by a structural model. All six indices are given with their bootstrapped standard errors and confidence intervals. When the number of bootstrap samples is equal to 0, the standard errors will not be reported. The user can specify the number of bootstrap samples via the gesca.run
function.
Numeric values of six model fit indices: the numbers of parameters and bootstrap samples, and six fit indices with their bootstrapped standard errors and confidence intervals.
Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A Component-Based Approach to Structural Equation Modeling (pp.26-32). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) summary(GSCA.group) # default output shows model fit indices as well, but fitmeasures(GSCA.group) # this gives bootstrapped std errors and CI ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) summary(GSCA.group) # default output shows model fit indices as well, but fitmeasures(GSCA.group) # this gives bootstrapped std errors and CI ## End(Not run)
This example is part of the organizational identification data used in Bergami and Bagozzi (2000). The data are used in Hwang and Takane (2014) (see Chapter 3). The data contain a sample of 305 employees (male = 157 and female = 148) and 22 indicators.
data(gesca.rick2)
data(gesca.rick2)
A data frame with 305 observations on the following 22 variables.
gender
grouping variable - gender (1 = male, 2 = female)
cei1
First indicator associated with the first latent variable - Organizational Prestige
cei2
Second indicator associated with the first latent variable - Organizational Prestige
cei3
Third indicator associated with the first latent variable - Organizational Prestige
cei4
Fourth indicator associated with the first latent variable - Organizational Prestige
cei5
Fifth indicator associated with the first latent variable - Organizational Prestige
cei6
Sixth indicator associated with the first latent variable - Organizational Prestige
cei7
Seventh indicator associated with the first latent variable - Organizational Prestige
cei8
Eighth indicator associated with the first latent variable - Organizational Prestige
ma1
First indicator associated with the second latent variable - Organizational Identification
ma2
Second indicator associated with the second latent variable - Organizational Identification
ma3
Third indicator associated with the second latent variable - Organizational Identification
ma4
Fourth indicator associated with the second latent variable - Organizational Identification
ma5
Fifth indicator associated with the second latent variable - Organizational Identification
ma6
Sixth indicator associated with the second latent variable - Organizational Identification
orgcmt1
First indicator associated with the third latent variable - Affective Commitment-Joy
orgcmt2
Second indicator associated with the third latent variable - Affective Commitment-Joy
orgcmt3
Third indicator associated with the third latent variable - Affective Commitment-Joy
orgcmt7
Fourth indicator associated with the third latent variable - Affective Commitment-Joy
orgcmt5
First indicator associated with the fourth latent variable - Affective Commitment-Love
orgcmt6
Second indicator associated with the fourth latent variable - Affective Commitment-Love
orgcmt8
Third indicator associated with the fourth latent variable - Affective Commitment-Love
Bergami, M., & Bagozzi, R. P. (2000). Self-categorization, affective commitment and group self-esteem as distinct aspects of social identity in the organization. British Journal of Social Psychology, 39(4), 555-577.
Hwang, H., & Takane, Y. (2014). Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling (pp.112-119). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: data(gesca.rick2) str(gesca.rick2) ## End(Not run)
## Not run: data(gesca.rick2) str(gesca.rick2) ## End(Not run)
Fit a generalized structured component analysis (GSCA) model. gesca 1.0 can handle various extensions of GSCA, including imposing constraints on parameters, second-order latent variable modeling, multiple-group analysis, the calculation of total and indirect effects, and missing value imputation.
gesca.run(myModel, data, group.name = NULL, group.equal = NULL, nbt = 100, itmax = 100, ceps = 1e-5, moption = 0, missingvalue = -9999)
gesca.run(myModel, data, group.name = NULL, group.equal = NULL, nbt = 100, itmax = 100, ceps = 1e-5, moption = 0, missingvalue = -9999)
myModel |
A simple description of the user-specified model using the gesca model syntax. The model can be described by strings or multiple lines of formulas. See Details and Examples below. |
data |
A data matrix or frame including the observed variables used in |
group.name |
A character string only used in a multiple group analysis. A variable name in the data frame which defines the group membership of each case. The user can specify which variable is used to provide group memberships for a multiple group analysis. If |
group.equal |
A vector of character strings only used in a multiple group analysis. It can be one or both of the following: |
nbt |
Denote the number of bootstrap samples. In gesca 1.0, the bootstrap method is employed to obtain the standard errors of parameter estimates. By default, |
itmax |
The maximum number of iterations for the alternating least-squares (ALS) algorithm. The default value is set to 100. |
ceps |
Convergence threshold for the ALS algorithm. The default is set to 0.00001. |
moption |
Options of handling missing data. By default, it is assumed that no value is missing in all variables. gesca 1.0 currently offers three options for handling missing data and the choice should be one of the following: list-wise deletion ( |
missingvalue |
A missing value identifier. Only used if |
General Overview of the gesca Model Syntax:
In gesca 1.0, the user can write one or more formula lines to specify a structural equation model. This is the gesca model syntax for the gesca.run
function. The model syntax consists of character strings or multiple lines of formulas as follows:
1) To specify the relationships between indicators and latent variables, the user can use the =~
operator. For example, the model syntax Y =~ X1 + X2 + X3
means the latent variable Y
is related to the indicators X1
, X2
, and X3
.
2) The relationships among latent variables can be expressed with the ~
operator. For example, the syntax Y1 ~ Y2 + Y3
shows that Y1
is regressed on Y2
and Y3
.
3) The user can consider a second-order latent variable that is linked to a set of first-order latent variables. This can be described by the =:
operator. For example, the syntax H1 =: Y1+Y2+Y3
indicates that a second-order latent variable, H1
, is related to its lower-order latent variables Y1
, Y2
, and Y3
.
Importantly, the variable names in the gesca model syntax should correspond to the variable names in the data frame. If the option group.name
is used for a multiple-group GSCA, the same model structure is fitted across groups by default.
Constraining Parameters in the gesca Model Syntax:
If the user wants to fix or constrain parameters to constants, it can be done in the gesca model syntax by adding values or labels followed by the *
operator. For example, in the following model syntax, the path coefficients of L1
regressed on L4
and of L4
regressed on L5
are fixed at 0.01 and 0.6, respectively:
myModel <-
"
L1 =~ z1 + z2 + z3
L2 =~ z4 + z5 + z6
L3 =~ z7 + z8
L4 =~ z9 + z10 + z11
L5 =~ z12
L2 ~ L1
L3 ~ L1 + L2
L4 ~ 0.01*L1 + L2 + L3
L5 ~ 0.6*L4
"
The above model is identical to the following model using labels (instead of values):
myModel <-
"
L1 =~ z1 + z2 + z3
L2 =~ z4 + z5 + z6
L3 =~ z7 + z8
L4 =~ z9 + z10 + z11
L5 =~ z12
L2 ~ L1
L3 ~ L1 + L2
L4 ~ c1*L1 + L2 + L3
L5 ~ c2*L4
c1 == 0.01
c2 == 0.6
"
Constraining Parameters in a Multiple-Group GSCA:
Any across-group equality constraints can be imposed on parameters in the same way as above. In addition, the group.equal
option can be used to constrain all loadings and/or all path coefficients across groups simultaneously. To demonstrate the imposition of across-group equality constraints on all loadings and path coefficients, the user can simply input group.equal = c("loadings", "paths")
, then the gesca.run
function estimates a single set of loadings and path coefficients that is fixed to be equal across groups. Similarly, group.equal = c("loadings")
treats all loadings to be fixed across all groups, and group.equal = c("paths")
returns identical path estimates in each group. See Examples for more details.
Reflective/Formative Indicators in the gesca Model Syntax:
In gesca 1.0, the relationships between indicators and latent variables can be either reflective or formative and this can be defined by the operator (1)
or (0)
, respectively. The user can input these operators in the model syntax lines that contain the =~
and/or the =:
operators. If (1)
or (0)
argument is omitted, by default, the gesca.run
function will treat all the relationships defined by =~
and/or =:
as reflective. For example, the model syntax Y =~ X1 + X2
or Y(1) =~ X1 + X2
means that X1
and X2
are reflective. Similarly, when the second-order latent variable has effects on its first-order latent variables, the relationship can be defined as H1 =: Y1+Y2
or H1(1) =: Y1+Y2
. On the other hand, if the first-order latent variables are considered formative, the model syntax is expressed as Y(0) =~ X1 + X2
, which involves no loadings for X1
and X2
.
Missing Value Imputation:
To use the missing value identifier of the gesca.run
function, missing values in the data frame should be represented by numeric codes (e.g. -9999, 99, -9) and specified by the missingvalue
option. This can be used only when the moption
argument is used. If the data have missing value codes but the user omits the missingvalue
argument, the gesca.run
function will treat the missing values as regular data values (except for the value of -9999 by default).
An object of class gesca.run
, for which a summary method and four functions in gesca 1.0 (effectmeasures
, fitmeasures
, latentmeasures
, and qualmeasures
) are available.
Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A Component-Based Approach to Structural Equation Modeling (pp.13-126). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # has the grouping variable "gender" # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # In case the user wants to apply a single-group analysis ignoring the group information: rick2.GSCA <- gesca.run(myModel, gesca.rick2) # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender") summary(GSCA.group) GSCA.group.nbt0 <- gesca.run(myModel, gesca.rick2, group.name ="gender", nbt = 0) summary(GSCA.group.nbt0) # no bootstrapped std errors effectmeasures(GSCA.group.nbt0) # Models with the imposition of constraints on parameters across groups # (1) equality constraints equl.cstr <- gesca.run(myModel, gesca.rick2, group.name ="gender", group.equal = c("loadings", "paths")) # (2) identical loadings but different path coefficients equl.loadings <- gesca.run(myModel, gesca.rick2, group.name ="gender", group.equal = c("loadings")) # (3) More constraints across groups with some formative indicators myModel.con <- " OP =~ cei1 + cei2 + cei3 OI(0) =~ ma1 + ma2 + ma3 AC_J =~ c(g,g)*orgcmt1 + c(h,h)*orgcmt2 + c(i,i)*orgcmt3 AC_L(0) =~ orgcmt5 + orgcmt6 + orgcmt8 OI ~ 0.37*OP AC_J ~ OI AC_L ~ OI " Const.Form <- gesca.run(myModel.con, gesca.rick2, group.name ="gender", nbt = 50) summary(Const.Form) # Second-order latent variable modeling myModel5 <- " # Measurement model OP =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8 OI =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Second-order latents AC =: AC_J + AC_L # Structural model OI ~ OP AC ~ OI " second.GSCA <- gesca.run(myModel5, gesca.rick2, group.name = "gender") summary(second.GSCA) second.GSCA.nbt0 <- gesca.run(myModel5, gesca.rick2, group.name = "gender", nbt=0) summary(second.GSCA.nbt0) ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # has the grouping variable "gender" # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # In case the user wants to apply a single-group analysis ignoring the group information: rick2.GSCA <- gesca.run(myModel, gesca.rick2) # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender") summary(GSCA.group) GSCA.group.nbt0 <- gesca.run(myModel, gesca.rick2, group.name ="gender", nbt = 0) summary(GSCA.group.nbt0) # no bootstrapped std errors effectmeasures(GSCA.group.nbt0) # Models with the imposition of constraints on parameters across groups # (1) equality constraints equl.cstr <- gesca.run(myModel, gesca.rick2, group.name ="gender", group.equal = c("loadings", "paths")) # (2) identical loadings but different path coefficients equl.loadings <- gesca.run(myModel, gesca.rick2, group.name ="gender", group.equal = c("loadings")) # (3) More constraints across groups with some formative indicators myModel.con <- " OP =~ cei1 + cei2 + cei3 OI(0) =~ ma1 + ma2 + ma3 AC_J =~ c(g,g)*orgcmt1 + c(h,h)*orgcmt2 + c(i,i)*orgcmt3 AC_L(0) =~ orgcmt5 + orgcmt6 + orgcmt8 OI ~ 0.37*OP AC_J ~ OI AC_L ~ OI " Const.Form <- gesca.run(myModel.con, gesca.rick2, group.name ="gender", nbt = 50) summary(Const.Form) # Second-order latent variable modeling myModel5 <- " # Measurement model OP =~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8 OI =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Second-order latents AC =: AC_J + AC_L # Structural model OI ~ OP AC ~ OI " second.GSCA <- gesca.run(myModel5, gesca.rick2, group.name = "gender") summary(second.GSCA) second.GSCA.nbt0 <- gesca.run(myModel5, gesca.rick2, group.name = "gender", nbt=0) summary(second.GSCA.nbt0) ## End(Not run)
The means and variances of latent variables and the correlations among the latent variables. In gesca 1.0, the individual scores of latent variables are calculated based on Fornell's (1992) approach.
latentmeasures(object)
latentmeasures(object)
object |
An object of class. This can be created via the |
Numeric vectors of means and variances, and correlation matrices.
Fornell, C. (1992). A national customer satisfaction barometer, the Swedish experience. Journal of Marketing, 56, 6-21.
Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A Component-Based Approach to Structural Equation Modeling (p.26). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) latentmeasures(GSCA.group) ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) latentmeasures(GSCA.group) ## End(Not run)
A variety of reliability and validity measures.
qualmeasures(object)
qualmeasures(object)
object |
An object of class. This can be created via the |
Cronbach's alpha and Dillon-Goldstein's rho (or the composite reliability) can be used for checking internal consistency of indicators for each latent variable. The average variance extracted (AVE) can be used to examine the convergent validity of a latent variable. The number of eigenvalues greater than one per block of indicators can be used to check uni-dimensionality of the indicators.
Numeric vectors of the reliability and validity measures.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) qualmeasures(GSCA.group) ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) qualmeasures(GSCA.group) ## End(Not run)
A summary of a fitted model such as model fit measures and parameter estimates obtained from GSCA.
## S3 method for class 'gesca' summary(object, ...)
## S3 method for class 'gesca' summary(object, ...)
object |
This function prints out a nice summary of the fitted model by the gesca.run function, including (1) information on convergence of the alternating least-squares (ALS) algorithm, (2) the number of observations (per group), parameters, and bootstrap samples, (3) six model fit measures, (4) GSCA parameter estimates such as weights, loadings, and path coefficients, and (5) R-squared values of each endogenous latent variable. |
... |
Further arguments passed to or from other methods. |
Object values of general information and parameter estimates of a fitted model via the gesca.run function.
Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A Component-Based Approach to Structural Equation Modeling (p.26). Boca Raton, FL: Chapman & Hall/CRC Press.
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) summary(GSCA.group) ## End(Not run)
## Not run: library(gesca) data(gesca.rick2) # Organizational identification example of Bagozzi # Model specification myModel <- " # Measurement model OP =~ cei1 + cei2 + cei3 OI =~ ma1 + ma2 + ma3 AC_J =~ orgcmt1 + orgcmt2 + orgcmt3 AC_L =~ orgcmt5 + orgcmt6 + orgcmt8 # Structural model OI ~ OP AC_J ~ OI AC_L ~ OI " # Run a multiple-group GSCA with the grouping variable gender: GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=50) summary(GSCA.group) ## End(Not run)