Coefficients of a 'lav_betaselect'-Class Object

Return the betas-select in a 'lav_betaselect'-class object.

Usage

# S3 method for class 'lav_betaselect'
coef(object, drop_na = FALSE, ...)

Arguments

object

The output of lav_betaselect().

drop_na

Logical. Whether betas-select with NA are dropped. Default is FALSE.

...

Optional arguments. Not used.

Value

A numeric vector: The betas-select in the object. The names of parameters follow the convention in lavaan.

Details

It just extracts and returns the column est from the object: the betas-select, with selected variables standardized.

See also

lav_betaselect()

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples

library(lavaan)
#> This is lavaan 0.6-19
#> lavaan is FREE software! Please report any bugs.
mod <-
"
med ~ iv + mod + iv:mod
dv ~ med + iv
"
fit <- sem(mod,
           data_test_medmod,
           fixed.x = TRUE)
summary(fit)
#> lavaan 0.6-19 ended normally after 3 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         7
#> 
#>   Number of observations                           200
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 2.685
#>   Degrees of freedom                                 2
#>   P-value (Chi-square)                           0.261
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Regressions:
#>                    Estimate   Std.Err  z-value  P(>|z|)
#>   med ~                                                
#>     iv                -6.339    0.997   -6.357    0.000
#>     mod               -3.903    0.622   -6.277    0.000
#>     iv:mod             0.286    0.039    7.248    0.000
#>   dv ~                                                 
#>     med                0.093    0.011    8.298    0.000
#>     iv                 0.229    0.039    5.917    0.000
#> 
#> Variances:
#>                    Estimate   Std.Err  z-value  P(>|z|)
#>    .med               61.851    6.185   10.000    0.000
#>    .dv                 2.104    0.210   10.000    0.000
#> 
fit_beta <- lav_betaselect(fit,
                           to_standardize = c("iv", "dv"))
coef(fit_beta)
#>         med~iv        med~mod     med~iv:mod         dv~med          dv~iv 
#>   -17.69726186    -3.90320725     0.79715974     0.04874437     0.33334073 
#>       med~~med         dv~~dv         iv~~iv        iv~~mod     iv~~iv:mod 
#>    61.85098096     0.57409180     1.00000000     1.89369830    35.18916975 
#>       mod~~mod    mod~~iv:mod iv:mod~~iv:mod 
#>    23.12940750   174.84683241  1862.98287427