lavParameterEstimates: Parameter Estimates

Description

Parameter estimates of a latent variable model.

Usage

lavParameterEstimates(object, 
                   se = TRUE, zstat = TRUE, pvalue = TRUE, ci = TRUE, 
                   standardized = FALSE, 
                   fmi = FALSE, plabel = FALSE,
                   level = 0.95, boot_ci_type = "perc", 
                   cov_std = TRUE, fmi_options = list(), 
                   rsquare = FALSE, 
                   remove_system_eq = TRUE, remove_eq = TRUE, 
                   remove_ineq = TRUE, remove_def = FALSE, 
                   remove_nonfree = FALSE, remove_step1 = TRUE,
                   remove_unused = FALSE,
                   remove_aux = TRUE, add_attributes = FALSE, 
                   output = "data.frame", header = FALSE, ...)
parameterEstimates(object, 
                   se = TRUE, zstat = TRUE, pvalue = TRUE, ci = TRUE, 
                   standardized = FALSE, 
                   fmi = FALSE, plabel = FALSE,
                   level = 0.95, boot_ci_type = "perc", 
                   cov_std = TRUE, fmi_options = list(), 
                   rsquare = FALSE, 
                   remove_system_eq = TRUE, remove_eq = TRUE, 
                   remove_ineq = TRUE, remove_def = FALSE, 
                   remove_nonfree = FALSE, remove_step1 = TRUE,
                   remove_unused = FALSE,
                   remove_aux = TRUE, add_attributes = FALSE, 
                   output = "data.frame", header = FALSE, ...)

Value

A data.frame containing the estimated 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.

If the model was fitted with se = "bootstrap" and

boot_ci_type = "bca", the empirical-influence design matrix used to compute the acceleration constant is stored as the "design"

attribute of the returned data.frame (retrieve with

attr(x, "design")). The parameter estimates in the bootstrap samples themselves are available via lavInspect(object, "coef.boot").

Arguments

object: An object of class lavaan.
se: Logical. If TRUE, include column containing the standard errors. If FALSE, this implies that zstat, 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.
plabel: Logical. If TRUE, show the plabel column of the parameter table in the output.
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). Note that the p-value is still computed assuming that the z-statistic follows a standard normal distribution.
standardized: Logical or character. If TRUE, standardized estimates are added to the output. Note that SEs and tests are still based on unstandardized estimates. Use standardizedSolution to obtain SEs and test statistics for standardized estimates. If a character vector is passed with any of c("std.lv","std.all","std.nox"), only the selected standardization methods are added. Note that "std.nox" only differs from "std.all" if fixed.x = TRUE; if fixed.x = FALSE, the exogenous covariates are treated as random variables (and standardized) just like any other variable, and a warning is issued. Alternatively, a character vector of (observed) variable names may be passed (for example standardized = c("x1", "x2")); in that case only the parameters involving these variables are standardized (the other observed variables are left unstandardized), and the result is added in a std.user column. This is a generalization of "std.nox", where the (observed) exogenous x variables are the ones left unstandardized.
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.
fmi_options: List. If non-empty, arguments can be provided to alter the default options when the model is fitted with the complete(d) data; otherwise, the same options are used as the original model.
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 output by removing all rows containing parameter definitions, if any.
remove_nonfree: Logical. If TRUE, filter the output by removing all rows containing fixed (non-free) parameters.
remove_step1: Logical. Only used by sam(). If TRUE, filter the output by removing all rows corresponding to the measurement parameters that are part of the first step.
remove_unused: Logical. If TRUE, filter the output by removing all rows containing automatically added parameters (user == 0) that are nonfree, and with their final (est) values fixed to their default values (typically 1 or 0); currently only used for intercepts and scaling-factors.
remove_aux: Logical. If TRUE (the default), filter the output by removing all rows corresponding to auxiliary (aux=) variables that were added to the model as saturated correlates (only relevant when missing = "ml" and the aux= argument was used).
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 op column contains r2, to indicate that the values in the est column are rsquare values.
add_attributes: Deprecated argument. Please use output= instead.
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 formatted and displayed with subsections (as used by the summary function).
header: Logical. Only used if output = "text". If TRUE, print a header at the top of the parameter list. This header contains information about the information matrix, whether the saturated (h1) model is structured or unstructured, and which type of standard errors are shown in the output.
...: to support old argument names

References

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.

Examples

Run this code

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

fit <- cfa(HS.model, data=HolzingerSwineford1939)
lavParameterEstimates(fit)
lavParameterEstimates(fit, output = "text")

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