parameterEstimates: Parameter Estimates
In nietsnel/psindex: Parameter Stability Index

Description Usage Arguments Value References Examples

Parameter estimates of a latent variable model.

parameterEstimates(object, se = TRUE, zstat = TRUE, pvalue = TRUE, 
                   ci = TRUE, level = 0.95, boot.ci.type = "perc", 
                   standardized = FALSE, cov.std = TRUE, fmi = FALSE, 
                   remove.system.eq = TRUE, remove.eq = TRUE, 
                   remove.ineq = TRUE, remove.def = FALSE, 
                   rsquare = FALSE, add.attributes = FALSE, header = TRUE)

`object`	An object of class `lavaan`.
`se`	Logical. If `TRUE`, include column containing the standard errors. If `FALSE`, this implies `zstat` and `pvalue` and `ci` are also `FALSE`.
`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`, confidence intervals are added to the output
`level`	The confidence level required.
`boot.ci.type`	If bootstrapping was used, the type of interval required. The value should be one of `"norm"`, `"basic"`, `"perc"`, or `"bca.simple"`. For the first three options, see the help page of the `boot.ci` function in the boot package. The `"bca.simple"` option produces intervals using the adjusted bootstrap percentile (BCa) method, but with no correction for acceleration (only for bias).
`standardized`	Logical. If `TRUE`, standardized estimates are added to the output
`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.
`fmi`	Logical. If `TRUE`, an extra column is added containing the fraction of missing information for each estimated parameter. Only available if `estimator="ML"`, `missing="(fi)ml"`, and `se="standard"`. See references for more information.
`remove.eq`	Logical. If `TRUE`, filter the output by removing all rows containing user-specified equality constraints, if any.
`remove.system.eq`	Logical. If `TRUE`, filter the output by removing all rows containing system-generated 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.
`rsquare`	Logical. If `TRUE`, add additional rows containing the rsquare values (in the `est` column) of all endogenous variables in the model. Both the `lhs` and `rhs` column contain the name of the endogenous variable, while the codeop column contains `r2`, to indicate that the values in the `est` column are rsquare values.
`add.attributes`	Logical. If `TRUE`, add a class attribute (class `lavaan.parameterEstimates`) and other attributes to be used by the print function for this class (`print.lavaan.parameterEstimates`). This is used by the `summary()` function, to prettify the output.
`header`	Logical. Only used if `add.attributes = TRUE`. If `TRUE`, print a header at the top of the parameter list. This header contains information about the information matrix, if saturated (h1) model is structured or unstructured, and which type of standard errors are shown in the output.

A data.frame containing the estimated parameters, parameters, standard errors, and (by default) z-values , p-values, and the lower and upper values of the confidence intervals. If requested, extra columns are added with standardized versions of the parameter estimates.

Savalei, V. & Rhemtulla, M. (2012). On obtaining estimates of the fraction of missing information from FIML. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 477-494.

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

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