Parameter Estimates
parameterEstimates.RdParameter 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, ...)Arguments
- object
An object of class
lavaan.- se
Logical. If
TRUE, include column containing the standard errors. IfFALSE, this implies thatzstat,pvalue, andciare alsoFALSE.- 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 theboot.cifunction 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. UsestandardizedSolutionto obtain SEs and test statistics for standardized estimates. If a character vector is passed with any ofc("std.lv","std.all","std.nox"), only the selected standardization methods are added. Note that"std.nox"only differs from"std.all"iffixed.x = TRUE; iffixed.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 examplestandardized = 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 astd.usercolumn. This is a generalization of"std.nox", where the (observed) exogenousxvariables 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 ifestimator="ML",missing="(fi)ml", andse="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(). IfTRUE, 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 whenmissing = "ml"and theaux=argument was used).- rsquare
Logical. If
TRUE, add additional rows containing the rsquare values (in theestcolumn) of all endogenous variables in the model. Both thelhsandrhscolumn contain the name of the endogenous variable, while theopcolumn containsr2, to indicate that the values in theestcolumn 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". IfTRUE, 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
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").
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
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HolzingerSwineford1939)
lavParameterEstimates(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 0.100 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
lavParameterEstimates(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 0.100 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
#>