Fit Structural Equation Models using the SAM approach
Fit a Structural Equation Model (SEM) using the Structural After Measurement (SAM) approach.
sam(model = NULL, data = NULL, cmd = "sem", se = "twostep",
mm.list = NULL, mm.args = list(bounds = "standard", se = "standard"),
struc.args = list(estimator = "ML", se = "standard"),
sam.method = "local", ...,
local.options = list(M.method = "ML", veta.force.pd = TRUE,
twolevel.method = "h1"), global.options = list(), output = "lavaan")model |
A description of the user-specified model. Typically, the model
is described using the lavaan model syntax. See
|
data |
A data frame containing the observed variables used in the model. |
cmd |
Character. Which command is used to run the sem models. The possible
choices are |
se |
Character. The type of standard errors that are used in the
final (structural) model. If |
mm.list |
List. Define the measurement blocks. Each element of the list should be either a single name of a latent variable, or a vector of latent variable names. If omitted, a separate measurement block is used for each latent variable. |
mm.args |
List. Optional arguments for the fitting
function(s) of the measurement block(s) only. See |
struc.args |
List. Optional arguments for the fitting function of the
structural part only. See |
sam.method |
Character. Can be set to |
... |
Many more additional options can be defined, using 'name = value'.
See |
local.options |
List. Options specific for local SAM method (these options may change over time). |
global.options |
List. Options specific for global SAM method (not used for now). |
output |
Character. If |
The sam function tries to automate the SAM approach, by first
estimating the measurement part of the model,
and then the structural part of the model. See reference for more
details.
If output = "lavaan", an object of class
lavaan, for which several methods
are available, including a summary method. If output = "list",
a list.
Rosseel and Loh (2021). A structural-after-measurement (SAM) approach to SEM. URL https://osf.io/pekbm/.
## The industrialization and Political Democracy Example
## Bollen (1989), page 332
model <- '
# latent variable definitions
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + a*y2 + b*y3 + c*y4
dem65 =~ y5 + a*y6 + b*y7 + c*y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual correlations
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit.sam <- sam(model, data = PoliticalDemocracy,
mm.list = list(ind = "ind60", dem = c("dem60", "dem65")))
summary(fit.sam)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.