| centered_estimates | R Documentation |
Computes centered estimates of model parameters. This is relevant when there is an
interaction term in the model, as the simple main effects depend upon the mean structure
of the structural model. Currenlty only available for
modsem_da and lavaan object.
It is not relevant for the PI approaches (excluding the "pind" method, which is not recommended),
since the indicators are centered before computing the product terms.
The centering can be applied to observed variable interactions in lavaan models
and latent interactions estimated using the sam function.
centered_estimates(object, ...)
## S3 method for class 'lavaan'
centered_estimates(
object,
monte.carlo = FALSE,
mc.reps = 10000,
tolerance.zero = 1e-10,
...
)
## S3 method for class 'modsem_da'
centered_estimates(
object,
monte.carlo = FALSE,
mc.reps = 10000,
tolerance.zero = 1e-10,
...
)
object |
An object of class |
... |
Additional arguments passed to underlying methods. See specific method documentation for supported arguments, including: |
monte.carlo |
Logical. If |
mc.reps |
Number of Monte Carlo repetitions. Default is 10000. |
tolerance.zero |
Threshold below which standard errors are set to |
A data.frame with centered estimates in the est column.
centered_estimates(lavaan): Method for lavaan objects
centered_estimates(modsem_da): Method for modsem_da objects
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + Z + X:Z
'
## Not run:
est_lms <- modsem(m1, oneInt, method = "lms")
centered_estimates(est_lms)
## End(Not run)
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