`plot`

method for class "chandwich". Only applicable to an object
`x`

for which `attr(x, "p_current") = 1`

, i.e. a model with
one free parameter.

- x
an object of class "chandwich", a result of a call to

`adjust_loglik`

.- y
Not used.

- type
An integer vector, a subset of the numbers

`1:4`

. Indicates which loglikelihoods to plot:`1`

for`"vertical"`

adjustment;`2`

for`"cholesky"`

(horizontal adjustment);`3`

for`"spectral"`

(horizontal adjustment);`4`

for no adjustment, i.e. based on the independence loglikelihood.- legend
A logical scalar or a character vector. If this is supplied then a legend is added to the plot. If

`legend`

is a character vector then it is used as the argument`legend`

to`legend`

. Otherwise, i.e. if`legend = TRUE`

then the argument`type`

is used.- legend_pos
The position of the legend (if required) specified using the argument

`x`

in`legend`

.- ...
Additional arguments passed to

`matplot`

or`legend`

. The arguments`col`

,`lty`

and`lwd`

will be used (in a consistent way) by both`matplot`

and`legend`

.If the argument

`xlim`

to`matplot`

is not supplied then the MLE minus (for`lower`

) or plus (for`upper`

) standard errors is used. If`type`

does not include 4 then adjusted standard errors are used. Otherwise, the larger of the adjusted and unadjusted standard errors are used.

Nothing is returned.

`adjust_loglik`

to adjust a user-supplied
loglikelihood function.

`summary.chandwich`

for maximum likelihood estimates
and unadjusted and adjusted standard errors.

`conf_intervals`

and `plot.confint`

to
plot confidence intervals for individual parameters.

`conf_region`

and `plot.confreg`

to
plot a confidence region for a pair of parameters.

```
# ------------------------- Binomial model, rats data ----------------------
# Contributions to the independence loglikelihood
binom_loglik <- function(prob, data) {
if (prob < 0 || prob > 1) {
return(-Inf)
}
return(dbinom(data[, "y"], data[, "n"], prob, log = TRUE))
}
rat_res <- adjust_loglik(loglik = binom_loglik, data = rats, par_names = "p")
# Vertically adjusted loglikelihood only
plot(rat_res)
# Three adjusted loglikelihoods and the independence loglikelihood
plot(rat_res, type = 1:4)
# Plot over (0,1) and reposition the legend
plot(rat_res, type = 1:4, xlim = c(0, 1), legend_pos = "bottom")
```