lavaan (version 0.6-1.1124)

standardizedSolution: Standardized Solution

Description

Standardized solution of a latent variable model.

Usage

standardizedSolution(object, type = "std.all", se = TRUE, 
                     zstat = TRUE, pvalue = TRUE, remove.eq = TRUE, 
                     remove.ineq = TRUE, remove.def = FALSE,
                     GLIST = NULL, est = NULL)

Arguments

object
An object of class .
type
If "std.lv", the standardized estimates are on the variances of the (continuous) latent variables only. If "std.all", the standardized estimates are based on both the variances of both (continuous) observed and latent variables. If "std.nox", the standardized estimates are based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.
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 TRUE, an extra column is added containing the so-called z-statistic, which is simply the value of the estimate divided by its standard error.
pvalue
Logical. If TRUE, an extra column is added containing the pvalues corresponding to the z-statistic, evaluated under a standard normal distribution.
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.
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.

Value

A data.frame containing standardized model parameters.

Examples

Run this code
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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