parameterEstimates.RdParameter estimates of a latent variable model.
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, add.attributes = FALSE,
output = "data.frame", header = FALSE)An object of class lavaan.
Logical. If TRUE, include column containing the standard
errors. If FALSE, this implies zstat and pvalue and
ci are also FALSE.
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.
Logical. If TRUE, an extra column is added containing
the pvalues corresponding to the z-statistic, evaluated under a standard
normal distribution.
If TRUE, confidence intervals are added to the output.
The confidence level required.
Logical. If TRUE, show the plabel column of the
parameter table in the output.
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.
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.
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.
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.
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.
Logical. If TRUE, filter the output by removing all
rows containing user-specified equality constraints, if any.
Logical. If TRUE, filter the output by
removing all rows containing system-generated equality constraints, if any.
Logical. If TRUE, filter the output by removing all
rows containing inequality constraints, if any.
Logical. If TRUE, filter the output by removing all
rows containing parameter definitions, if any.
Logical. If TRUE, filter the output by removing
all rows containing fixed (non-free) parameters.
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.
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.
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.
Deprecated argument. Please use output= instead.
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).
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.
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HolzingerSwineford1939)
#> Warning: lavaan->lav_model_vcov():
#> The variance-covariance matrix of the estimated parameters (vcov) does not
#> appear to be positive definite! The smallest eigenvalue (= -1.747972e-02)
#> is smaller than zero. This may be a symptom that the model is not
#> identified.
parameterEstimates(fit)
#> 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 0.094 5.876 0 0.369 0.738
#> 3 visual =~ x3 0.729 0.097 7.516 0 0.539 0.920
#> 4 textual =~ x4 1.000 0.000 NA NA 1.000 1.000
#> 5 textual =~ x5 1.113 0.062 17.821 0 0.991 1.235
#> 6 textual =~ x6 0.926 0.053 17.338 0 0.821 1.031
#> 7 speed =~ x7 1.000 0.000 NA NA 1.000 1.000
#> 8 speed =~ x8 1.180 0.079 15.008 0 1.026 1.334
#> 9 speed =~ x9 1.082 0.105 10.318 0 0.876 1.287
#> 10 x1 ~~ x1 0.549 0.095 5.758 0 0.362 0.736
#> 11 x2 ~~ x2 1.134 0.104 10.893 0 0.930 1.338
#> 12 x3 ~~ x3 0.844 0.076 11.114 0 0.695 0.993
#> 13 x4 ~~ x4 0.371 0.045 8.307 0 0.284 0.459
#> 14 x5 ~~ x5 0.446 0.053 8.371 0 0.342 0.551
#> 15 x6 ~~ x6 0.356 0.039 9.177 0 0.280 0.432
#> 16 x7 ~~ x7 0.799 0.067 12.001 0 0.669 0.930
#> 17 x8 ~~ x8 0.488 0.068 7.162 0 0.354 0.621
#> 18 x9 ~~ x9 0.566 0.070 8.096 0 0.429 0.703
#> 19 visual ~~ visual 0.809 0.108 7.479 0 0.597 1.021
#> 20 textual ~~ textual 0.979 0.109 9.012 0 0.766 1.193
#> 21 speed ~~ speed 0.384 0.077 4.987 0 0.233 0.535
#> 22 visual ~~ textual 0.408 0.074 5.551 0 0.264 0.552
#> 23 visual ~~ speed 0.262 0.058 4.551 0 0.149 0.375
#> 24 textual ~~ speed 0.173 0.049 3.549 0 0.078 0.269
parameterEstimates(fit, output = "text")
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> visual =~
#> x1 1.000 1.000 1.000
#> x2 0.554 0.094 5.876 0.000 0.369 0.738
#> x3 0.729 0.097 7.516 0.000 0.539 0.920
#> textual =~
#> x4 1.000 1.000 1.000
#> x5 1.113 0.062 17.821 0.000 0.991 1.235
#> x6 0.926 0.053 17.338 0.000 0.821 1.031
#> speed =~
#> x7 1.000 1.000 1.000
#> x8 1.180 0.079 15.008 0.000 1.026 1.334
#> x9 1.082 0.105 10.318 0.000 0.876 1.287
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> visual ~~
#> textual 0.408 0.074 5.551 0.000 0.264 0.552
#> speed 0.262 0.058 4.551 0.000 0.149 0.375
#> textual ~~
#> speed 0.173 0.049 3.549 0.000 0.078 0.269
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
#> .x1 0.549 0.095 5.758 0.000 0.362 0.736
#> .x2 1.134 0.104 10.893 0.000 0.930 1.338
#> .x3 0.844 0.076 11.114 0.000 0.695 0.993
#> .x4 0.371 0.045 8.307 0.000 0.284 0.459
#> .x5 0.446 0.053 8.371 0.000 0.342 0.551
#> .x6 0.356 0.039 9.177 0.000 0.280 0.432
#> .x7 0.799 0.067 12.001 0.000 0.669 0.930
#> .x8 0.488 0.068 7.162 0.000 0.354 0.621
#> .x9 0.566 0.070 8.096 0.000 0.429 0.703
#> visual 0.809 0.108 7.479 0.000 0.597 1.021
#> textual 0.979 0.109 9.012 0.000 0.766 1.193
#> speed 0.384 0.077 4.987 0.000 0.233 0.535
#>