standardized_estimates {modsem} | R Documentation |
Get Standardized Estimates
Description
Computes standardized estimates of model parameters for various types of modsem
objects.
Usage
standardized_estimates(object, ...)
## S3 method for class 'lavaan'
standardized_estimates(
object,
monte.carlo = FALSE,
mc.reps = 10000,
tolerance.zero = 1e-10,
...
)
## S3 method for class 'modsem_da'
standardized_estimates(
object,
monte.carlo = FALSE,
mc.reps = 10000,
tolerance.zero = 1e-10,
...
)
## S3 method for class 'modsem_pi'
standardized_estimates(
object,
correction = FALSE,
std.errors = c("rescale", "delta", "monte.carlo"),
mc.reps = 10000,
colon.pi = FALSE,
...
)
Arguments
object |
An object of class |
... |
Additional arguments passed on to |
monte.carlo |
Logical. If |
mc.reps |
Integer. Number of Monte Carlo replications to use if |
tolerance.zero |
Threshold below which standard errors are set to |
correction |
Logical. Whether to apply a correction to the standardized estimates
of the interaction term. By default, Hence the scale of the interaction effect changes, such that
the standardized interaction term does not correspond to one (standardized) unit
change in the moderating variables. If |
std.errors |
Character string indicating the method used to compute standard errors
when
Ignored if |
colon.pi |
Logical. If |
Details
Standard errors can either be calculated using the delta method, or a monte.carlo simulation
(monte.carlo
is not available for modsem_pi
objects if correction == FALSE
.).
NOTE that the standard errors of the standardized paramters change the assumptions of the
model, and will in most cases yield different z and p-values, compared to the unstandardized solution.
In almost all cases, significance testing should be based on the unstandardized solution. Since,
the standardization process changes the model assumptions, it also changes what the p-statistics measure.
I.e., the test statistics for the standardized and unstandardized solutions belong to different sets of
hypothesis, which are not exactly equivalent to each other.
For modsem_da
and modsem_mplus
objects, the interaction term is not a formal
variable in the model and therefore lacks a defined variance. Under assumptions of normality
and zero-mean variables, the interaction variance is estimated as:
var(xz) = var(x) * var(z) + cov(x, z)^2
This means the standardized estimate for the interaction differs from approaches like
lavaan
, which treats the interaction as a latent variable with unit variance.
For modsem_pi
objects, the interaction term is standardized by default assuming
var(xz) = 1
, but this can be overridden using the correction
argument.
NOTE: Standardized estimates are always placed in the est
column,
not est.std
, regardless of model type.
Value
A data.frame
with standardized estimates in the est
column.
Methods (by class)
-
standardized_estimates(lavaan)
: Method forlavaan
objects -
standardized_estimates(modsem_da)
: Method formodsem_da
objects -
standardized_estimates(modsem_pi)
: Method formodsem_pi
objects
Examples
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + Z + X:Z
'
# Double centering approach
est_dca <- modsem(m1, oneInt)
std1 <- standardized_estimates(est_dca) # no correction
summarize_partable(std1)
std2 <- standardized_estimates(est_dca, correction = TRUE) # apply correction
summarize_partable(std2)
## Not run:
est_lms <- modsem(m1, oneInt, method = "lms")
standardized_estimates(est_lms) # correction not relevant for lms
## End(Not run)