parameterEstimates: Parameter Estimates

Description Usage Arguments Value References Examples

Description

Parameter estimates of a latent variable model.

Usage

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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)

Arguments

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). Note that the p-value is still computed assuming that the z-statistic follows a standard normal distribution.

standardized

Logical. 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.

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.

Value

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.

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

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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")

Example output

This is lavaan 0.6-7
lavaan is BETA software! Please report any bugs.
       lhs op     rhs   est    se      z pvalue ci.lower ci.upper
1   visual =~      x1 1.000 0.000     NA     NA    1.000    1.000
2   visual =~      x2 0.554 0.100  5.554      0    0.358    0.749
3   visual =~      x3 0.729 0.109  6.685      0    0.516    0.943
4  textual =~      x4 1.000 0.000     NA     NA    1.000    1.000
5  textual =~      x5 1.113 0.065 17.014      0    0.985    1.241
6  textual =~      x6 0.926 0.055 16.703      0    0.817    1.035
7    speed =~      x7 1.000 0.000     NA     NA    1.000    1.000
8    speed =~      x8 1.180 0.165  7.152      0    0.857    1.503
9    speed =~      x9 1.082 0.151  7.155      0    0.785    1.378
10      x1 ~~      x1 0.549 0.114  4.833      0    0.326    0.772
11      x2 ~~      x2 1.134 0.102 11.146      0    0.934    1.333
12      x3 ~~      x3 0.844 0.091  9.317      0    0.667    1.022
13      x4 ~~      x4 0.371 0.048  7.779      0    0.278    0.465
14      x5 ~~      x5 0.446 0.058  7.642      0    0.332    0.561
15      x6 ~~      x6 0.356 0.043  8.277      0    0.272    0.441
16      x7 ~~      x7 0.799 0.081  9.823      0    0.640    0.959
17      x8 ~~      x8 0.488 0.074  6.573      0    0.342    0.633
18      x9 ~~      x9 0.566 0.071  8.003      0    0.427    0.705
19  visual ~~  visual 0.809 0.145  5.564      0    0.524    1.094
20 textual ~~ textual 0.979 0.112  8.737      0    0.760    1.199
21   speed ~~   speed 0.384 0.086  4.451      0    0.215    0.553
22  visual ~~ textual 0.408 0.074  5.552      0    0.264    0.552
23  visual ~~   speed 0.262 0.056  4.660      0    0.152    0.373
24 textual ~~   speed 0.173 0.049  3.518      0    0.077    0.270

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
  visual =~                                                             
    x1                1.000                               1.000    1.000
    x2                0.554    0.100    5.554    0.000    0.358    0.749
    x3                0.729    0.109    6.685    0.000    0.516    0.943
  textual =~                                                            
    x4                1.000                               1.000    1.000
    x5                1.113    0.065   17.014    0.000    0.985    1.241
    x6                0.926    0.055   16.703    0.000    0.817    1.035
  speed =~                                                              
    x7                1.000                               1.000    1.000
    x8                1.180    0.165    7.152    0.000    0.857    1.503
    x9                1.082    0.151    7.155    0.000    0.785    1.378

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
  visual ~~                                                             
    textual           0.408    0.074    5.552    0.000    0.264    0.552
    speed             0.262    0.056    4.660    0.000    0.152    0.373
  textual ~~                                                            
    speed             0.173    0.049    3.518    0.000    0.077    0.270

Variances:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
   .x1                0.549    0.114    4.833    0.000    0.326    0.772
   .x2                1.134    0.102   11.146    0.000    0.934    1.333
   .x3                0.844    0.091    9.317    0.000    0.667    1.022
   .x4                0.371    0.048    7.779    0.000    0.278    0.465
   .x5                0.446    0.058    7.642    0.000    0.332    0.561
   .x6                0.356    0.043    8.277    0.000    0.272    0.441
   .x7                0.799    0.081    9.823    0.000    0.640    0.959
   .x8                0.488    0.074    6.573    0.000    0.342    0.633
   .x9                0.566    0.071    8.003    0.000    0.427    0.705
    visual            0.809    0.145    5.564    0.000    0.524    1.094
    textual           0.979    0.112    8.737    0.000    0.760    1.199
    speed             0.384    0.086    4.451    0.000    0.215    0.553

lavaan documentation built on March 10, 2021, 5:05 p.m.