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standardizedSolution

Standardized Solution


Description

Standardized solution of a latent variable model.

Usage

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")

Arguments

object

An object of class lavaan.

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.

ci

If TRUE, simple symmetric confidence intervals are added to the output

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 argument is also provided. See Note.

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 GLIST argument is also provided. See Note.

partable

A custom list or data.frame in which to store the standardized parameter values. If provided, it will be used instead of the parameter table inside the object@ParTable slot.

output

Character. If "data.frame", the parameter table is displayed as a standard (albeit lavaan-formatted) data.frame. If "text" (or alias "pretty"), the parameter table is prettyfied, and displayed with subsections (as used by the summary function).

Value

A data.frame containing standardized model parameters.

Note

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.

Examples

HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data=HolzingerSwineford1939)
standardizedSolution(fit)

lavaan

Latent Variable Analysis

v0.6-10
GPL (>= 2)
Authors
Yves Rosseel [aut, cre] (<https://orcid.org/0000-0002-4129-4477>), Terrence D. Jorgensen [aut] (<https://orcid.org/0000-0001-5111-6773>), Nicholas Rockwood [aut] (<https://orcid.org/0000-0001-5931-183X>), Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsikatsou [ctb], Mariska Barendse [ctb], Florian Scharf [ctb], Han Du [ctb]
Initial release

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