Parameter estimates of a latent variable model.

```
parameterEstimates(object,
se = TRUE, zstat = TRUE, pvalue = TRUE, ci = TRUE,
standardized = FALSE,
fmi = 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,
add.attributes = FALSE,
output = "data.frame", header = FALSE)
```

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. Note that *SE*s and tests are still based on
unstandardized estimates. Use `standardizedSolution`

to obtain
*SE*s and test statistics for standardized estimates.

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.

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

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
prettyfied, 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, 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.

# NOT RUN { HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' fit <- cfa(HS.model, data=HolzingerSwineford1939) parameterEstimates(fit) parameterEstimates(fit, output = "text") # }