Standardized Solution
Standardized solution of a latent variable model.
standardizedSolution(object, type = "std.all", se = TRUE, zstat = TRUE, pvalue = TRUE, ci = TRUE, level = 0.95, cov.std = TRUE, remove.eq = TRUE, remove.ineq = TRUE, remove.def = FALSE, partable = NULL, GLIST = NULL, est = NULL, output = "data.frame")
object |
An object of class |
type |
If |
se |
Logical. If TRUE, standard errors for the standardized parameters will be computed, together with a z-statistic and a p-value. |
zstat |
Logical. If |
pvalue |
Logical. If |
ci |
If |
level |
The confidence level required. |
cov.std |
Logical. If TRUE, the (residual) observed covariances are scaled by the square root of the ‘Theta’ diagonal elements, and the (residual) latent covariances are scaled by the square root of the ‘Psi’ diagonal elements. If FALSE, the (residual) observed covariances are scaled by the square root of the diagonal elements of the observed model-implied covariance matrix (Sigma), and the (residual) latent covariances are scaled by the square root of diagonal elements of the model-implied covariance matrix of the latent variables. |
remove.eq |
Logical. If TRUE, filter the output by removing all rows containing equality constraints, if any. |
remove.ineq |
Logical. If TRUE, filter the output by removing all rows containing inequality constraints, if any. |
remove.def |
Logical. If TRUE, filter the ouitput by removing all rows containing parameter definitions, if any. |
GLIST |
List of model matrices. If provided, they will be used
instead of the GLIST inside the object@Model slot. Only works if the
|
est |
Numeric. Parameter values (as in the ‘est’ column of a
parameter table). If provided, they will be used instead of
the parameters that can be extract from object. Only works if the |
partable |
A custom |
output |
Character. If |
A data.frame containing standardized model parameters.
The est
, GLIST
, and partable
arguments are not meant for
everyday users, but for authors of external R packages that depend on
lavaan
. Only to be used with great caution.
HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' fit <- cfa(HS.model, data=HolzingerSwineford1939) standardizedSolution(fit)
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