Find LBCIs with levels of confidence
different from those stored in a `semlbci`

- class
object.

## Arguments

- x
The output of

`semlbci()`

.- ciperc_levels
A numeric vector of deviations from the original level of confidence. The default is

`c(-.025, .025)`

. Therefore, if the original level is .95, the levels to be used is`c(-.025, .025) + .95`

or`c(.925, .975)`

.- ciperc_range
A numeric vector of two numbers, which are the minimum and maximum levels of confidence to be used, respectively. Default is

`c(.60, .99)`

.

## Value

A `semlbci_list`

-class object, which is
simply a named list of `semlbci`

-class object,
names being the levels of confidence.

## Details

It receives a `semlbci`

-class object, gets
the original level of confidence, generates one or
more levels of confidence different from this level
by certain amounts, and repeats the original call
to `semlbci()`

with these levels of confidence.
The results are returned as a list of class
`semlbci_list`

, with the original`semlbci`

-class
included.

## Author

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

## Examples

```
library(lavaan)
mod <-
"
m ~ x
y ~ m
"
fit_med <- sem(mod, simple_med, fixed.x = FALSE)
lbci_fit <- semlbci(fit_med)
lbci_fit_nb <- nearby_levels(lbci_fit,
ciperc_levels = c(-.050, .050))
names(lbci_fit_nb)
#> [1] "0.9" "0.95" "0.99"
# Check the order of the confidence bounds.
# A confidence interval with a higher level of confidence
# should enclose a confidence interval with
# a lower level of confidence.
ci_order(lbci_fit_nb)
#> lb_0.99 lb_0.95 lb_0.9 ub_0.9 ub_0.95 ub_0.99 Order
#> m~x 0.557 < 0.828 ! 0.965 < 2.387 ! 2.525 < 2.795 OK
#> y~m 0.345 < 0.391 ! 0.414 < 0.656 ! 0.679 < 0.725 OK
```