Print the output of stdmod_lavaan().

# S3 method for stdmod_lavaan
print(x, conf = 0.95, nd = 3, ...)

Arguments

x

The output of stdmod_lavaan().

conf

If nonparametric bootstrapping has been conducted by stdmod_lavaan(), this is the level of confidence in proportion (.95 denotes 95%), of the confidence interval. Default is .95.

nd

The number of digits to be printed.

...

Optional arguments. Ignored.

Value

x is returned invisibly.

Examples


# Load a test data of 500 cases

dat <- test_mod1
library(lavaan)

mod <-
"
med ~ iv + mod + iv:mod + cov1
dv ~ med + cov2
"
fit <- sem(mod, dat)
coef(fit)
#>     med~iv    med~mod med~iv:mod   med~cov1     dv~med    dv~cov2   med~~med 
#>      0.221      0.104      0.257      0.104      0.246      0.191      0.201 
#>     dv~~dv 
#>      0.169 

# Compute the standardized moderation effect
out_noboot <- stdmod_lavaan(fit = fit,
                            x = "iv",
                            y = "med",
                            w = "mod",
                            x_w = "iv:mod")
out_noboot
#> 
#> Call:
#> stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod")
#> 
#>                  Variable
#> Focal Variable         iv
#> Moderator             mod
#> Outcome Variable      med
#> Product Term       iv:mod
#> 
#>              lhs op    rhs   est    se      z pvalue ci.lower ci.upper
#> Original     med  ~ iv:mod 0.257 0.025 10.169      0    0.208    0.307
#> Standardized med  ~ iv:mod 0.440    NA     NA     NA       NA       NA

# Compute the standardized moderation effect and
# its percentile confidence interval based on nonparametric bootstrapping
set.seed(8479075)
system.time(out_boot <- stdmod_lavaan(fit = fit,
                                      x = "iv",
                                      y = "med",
                                      w = "mod",
                                      x_w = "iv:mod",
                                      boot_ci = TRUE,
                                      R = 50))
#>    user  system elapsed 
#>   0.880   0.015   0.869 
# In real analysis, R should be at least 2000.

out_boot
#> 
#> Call:
#> stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod", 
#>     boot_ci = TRUE, R = 50)
#> 
#>                  Variable
#> Focal Variable         iv
#> Moderator             mod
#> Outcome Variable      med
#> Product Term       iv:mod
#> 
#>              lhs op    rhs   est    se      z pvalue ci.lower ci.upper
#> Original     med  ~ iv:mod 0.257 0.025 10.169      0    0.208    0.307
#> Standardized med  ~ iv:mod 0.440    NA     NA     NA    0.296    0.523
#> 
#> Confidence interval of standardized moderation effect:
#> - Level of confidence: 95%
#> - Bootstrapping Method: Nonparametric
#> - Type: Percentile
#> - Number of bootstrap samples requests: 50
#> - Number of bootstrap samples with valid results: 50