Standardized Solution — standardizedSolution • lavaan

Standardized solution of a latent variable model.

Usage

standardizedSolution(object, type = "std.all", se = TRUE, zstat = TRUE, 
                     pvalue = TRUE, ci = TRUE, level = 0.95, cov.std = TRUE, 
                     remove.eq = TRUE, remove.ineq = TRUE, remove.def = FALSE, 
                     partable = NULL, GLIST = NULL, est = NULL,
                     output = "data.frame")

Arguments

object: An object of class lavaan.
type: If "std.lv", the standardized estimates are on the variances of the (continuous) latent variables only. If "std.all", the standardized estimates are based on both the variances of both (continuous) observed and latent variables. If "std.nox", the standardized estimates are based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.
se: Logical. If TRUE, standard errors for the standardized parameters will be computed, together with a z-statistic and a p-value.
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, simple symmetric confidence intervals are added to the output
level: The confidence level required.
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.
remove.eq: Logical. If TRUE, filter the output by removing all rows containing 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 ouitput by removing all rows containing parameter definitions, if any.
GLIST: List of model matrices. If provided, they will be used instead of the GLIST inside the object@Model slot. Only works if the est argument is also provided. See Note.
est: Numeric. Parameter values (as in the `est' column of a parameter table). If provided, they will be used instead of the parameters that can be extract from object. Only works if the GLIST argument is also provided. See Note.
partable: A custom list or data.frame in which to store the standardized parameter values. If provided, it will be used instead of the parameter table inside the object@ParTable slot.
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).

Note

The est, GLIST, and partable arguments are not meant for everyday users, but for authors of external R packages that depend on lavaan. Only to be used with great caution.

Value

A data.frame containing standardized model parameters.

Examples

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

fit <- cfa(HS.model, data=HolzingerSwineford1939)
standardizedSolution(fit)
#>        lhs op     rhs est.std    se      z pvalue ci.lower ci.upper
#> 1   visual =~      x1   0.772 0.055 14.041      0    0.664    0.880
#> 2   visual =~      x2   0.424 0.060  7.105      0    0.307    0.540
#> 3   visual =~      x3   0.581 0.055 10.539      0    0.473    0.689
#> 4  textual =~      x4   0.852 0.023 37.776      0    0.807    0.896
#> 5  textual =~      x5   0.855 0.022 38.273      0    0.811    0.899
#> 6  textual =~      x6   0.838 0.023 35.881      0    0.792    0.884
#> 7    speed =~      x7   0.570 0.053 10.714      0    0.465    0.674
#> 8    speed =~      x8   0.723 0.051 14.309      0    0.624    0.822
#> 9    speed =~      x9   0.665 0.051 13.015      0    0.565    0.765
#> 10      x1 ~~      x1   0.404 0.085  4.763      0    0.238    0.571
#> 11      x2 ~~      x2   0.821 0.051 16.246      0    0.722    0.920
#> 12      x3 ~~      x3   0.662 0.064 10.334      0    0.537    0.788
#> 13      x4 ~~      x4   0.275 0.038  7.157      0    0.200    0.350
#> 14      x5 ~~      x5   0.269 0.038  7.037      0    0.194    0.344
#> 15      x6 ~~      x6   0.298 0.039  7.606      0    0.221    0.374
#> 16      x7 ~~      x7   0.676 0.061 11.160      0    0.557    0.794
#> 17      x8 ~~      x8   0.477 0.073  6.531      0    0.334    0.620
#> 18      x9 ~~      x9   0.558 0.068  8.208      0    0.425    0.691
#> 19  visual ~~  visual   1.000 0.000     NA     NA    1.000    1.000
#> 20 textual ~~ textual   1.000 0.000     NA     NA    1.000    1.000
#> 21   speed ~~   speed   1.000 0.000     NA     NA    1.000    1.000
#> 22  visual ~~ textual   0.459 0.064  7.189      0    0.334    0.584
#> 23  visual ~~   speed   0.471 0.073  6.461      0    0.328    0.613
#> 24 textual ~~   speed   0.283 0.069  4.117      0    0.148    0.418