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library(modsem)
Both the LMS
and QML
approaches work on
most models, but interaction effects with endogenous variables can be
tricky to estimate (see the vignette).
Both approaches, particularly the LMS
approach, are
computationally intensive and are partially implemented in C++ (using
Rcpp
and RcppArmadillo
). Additionally,
starting parameters are estimated using the double-centering approach,
and the means of the observed variables are used to generate good
starting parameters for faster convergence. If you want to monitor the
progress of the estimation process, you can use
verbose = TRUE
.
Here is an example of the LMS
approach for a simple
model. By default, the summary()
function calculates fit
measures compared to a null model (i.e., the same model without an
interaction term).
library(modsem)
<- '
m1 # Outer Model
X =~ x1
X =~ x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + Z
Y ~ X:Z
'
<- modsem(m1, oneInt, method = "lms")
lms1 summary(lms1, standardized = TRUE) # Standardized estimates
Here is the same example using the QML
approach:
<- modsem(m1, oneInt, method = "qml")
qml1 summary(qml1)
Below is an example of a more complex model based on the theory of
planned behavior (TPB), which includes two endogenous variables and an
interaction between an endogenous and exogenous variable. When
estimating more complex models with the LMS
approach, it is
recommended to increase the number of nodes used for numerical
integration. By default, the number of nodes is set to 16, but this can
be increased using the nodes
argument. The
nodes
argument has no effect on the QML
approach.
When there is an interaction effect between an endogenous and
exogenous variable, it is recommended to use at least 32 nodes for the
LMS
approach. You can also obtain robust standard errors by
setting robust.se = TRUE
in the modsem()
function.
Note: If you want the LMS
approach to
produce results as similar as possible to Mplus, you should increase the
number of nodes (e.g., nodes = 100
).
# ATT = Attitude
# PBC = Perceived Behavioral Control
# INT = Intention
# SN = Subjective Norms
# BEH = Behavior
<- '
tpb # Outer Model (Based on Hagger et al., 2007)
ATT =~ att1 + att2 + att3 + att4 + att5
SN =~ sn1 + sn2
PBC =~ pbc1 + pbc2 + pbc3
INT =~ int1 + int2 + int3
BEH =~ b1 + b2
# Inner Model (Based on Steinmetz et al., 2011)
INT ~ ATT + SN + PBC
BEH ~ INT + PBC
BEH ~ INT:PBC
'
<- modsem(tpb, TPB, method = "lms", nodes = 32)
lms2 summary(lms2)
<- modsem(tpb, TPB, method = "qml")
qml2 summary(qml2, standardized = TRUE) # Standardized estimates
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.