Visualize the log profile likelihood of a parameter fixed to values in a range.

## Arguments

- x
The output of

`loglike_compare()`

.- y
Not used.

- type
Character. If

`"ggplot2"`

, will use`ggplot2::ggplot()`

to plot the graph. If`"default"`

, will use R base graphics, The`ggplot2`

version plots more information. Default is`"ggplot2"`

.- size_label
The relative size of the labels for thetas (and

*p*-values, if requested) in the plot, determined by`ggplot2::rel()`

. Default is 4.- size_point
The relative size of the points to be added if

*p*-values are requested in the plot, determined by`ggplot2::rel()`

. Default is 4.- nd_theta
The number of decimal places for the labels of theta. Default is 3.

- nd_pvalue
The number of decimal places for the labels of

*p*-values. Default is 3.- size_theta
Deprecated. No longer used.

- size_pvalue
Deprecated. No longer used.

- add_pvalues
If

`TRUE`

, likelihood ratio test*p*-values will be included for the confidence limits. Only available if`type = "ggplot2"`

.- ...
Optional arguments. Ignored.

## Details

Given the output of `loglike_compare()`

, it plots the log
profile likelihood based on quadratic approximation and that
based on the original log-likelihood. The log profile likelihood
is scaled to have a maximum of zero (at the point estimate) as
suggested by Pawitan (2013).

## References

Pawitan, Y. (2013). *In all likelihood: Statistical
modelling and inference using likelihood*. Oxford University Press.

## Examples

```
## loglike_compare
library(lavaan)
data(simple_med)
dat <- simple_med
mod <-
"
m ~ a * x
y ~ b * m
ab := a * b
"
fit <- lavaan::sem(mod, simple_med, fixed.x = FALSE)
# Four points are used just for illustration
# At least 21 points should be used for a smooth plot
# Remove try_k_more in real applications. It is set
# to run such that this example is not too slow.
# use_pbapply can be removed or set to TRUE to show the progress.
ll_a <- loglike_compare(fit, par_i = "m ~ x", n_points = 4,
try_k_more = 0,
use_pbapply = FALSE)
plot(ll_a)
plot(ll_a, add_pvalues = TRUE)
# See the vignette "loglike" for an example for the
# indirect effect.
```