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Print the diagnostic information of a cibound-class object.

Usage

# S3 method for cibound
print(x, digits = 5, ...)

Arguments

x

The output of a ci_bound_xx_i function. Currently the only such function is ci_bound_wn_i().

digits

The number of digits after the decimal point. To be passed to round(). Default is 5.

...

Other arguments. They will be ignored.

Value

x is returned invisibly. Called for its side effect.

Details

This is the print method for the output of ci_bound_wn_i(), a cibound-class object. It prints the diagnostic information on the bound being found and the search process.

Examples

data(simple_med)
dat <- simple_med

mod <-
"
m ~ x
y ~ m
"

fit_med <- lavaan::sem(mod, simple_med, fixed.x = FALSE)

fn_constr0 <- set_constraint(fit_med)

out1l <- ci_bound_wn_i(i = 1,
                       npar = 5,
                       sem_out = fit_med,
                       f_constr = fn_constr0,
                       which = "lbound")

# Print the output
out1l
#> Target Parameter:	m ~ x (group = 1, block = 1)
#> Position:		1
#> Which Bound:		Lower Bound
#> Method:			Wu-Neale-2012
#> Confidence Level:	0.95
#> Achieved Level:		0.950000000054991
#> Standardized:		No
#> Likelihood-Based Bound:	0.8277
#> Wald Bound:		0.83177
#> Point Estimate:		1.67613
#> Ratio to Wald Bound:	1.00482
#> 
#> -- Check --
#> Level achieved?		Yes (Difference: 5.4991e-11; Tolerance: 5e-04)
#> Solution admissible?	Yes
#> Direction valid?	Yes
#> 
#> -- Optimization Information --
#> Solver Status:		4
#> Convergence Message:	NLOPT_XTOL_REACHED: Optimization stopped because xtol_rel or xtol_abs (above) was reached.
#> Iterations:		3
#> Termination Conditions:
#> 	xtol_rel: 1e-05
#> 	ftol_rel: 1e-05
#> 	maxeval:  500
#> 
#> -- Parameter Estimates --
#>             m~x     y~m     m~~m     y~~y    x~~x
#> Start   0.83177 0.53508 35.37699 40.11896 0.93513
#> Final   0.82770 0.53508 35.37711 40.11896 0.93513
#> Change -0.00407 0.00000  0.00011  0.00000 0.00000
#> 
#> Bound before check:	0.8277
#> Status Code:		0
#> Call: ci_bound_wn_i(i = 1, npar = 5, sem_out = fit_med, f_constr = fn_constr0, 
#>     which = "lbound")
#>