Plot results from cross-validation

```
# S3 method for TrainedOwl
plot(
x,
measure = c("auto", "mse", "mae", "deviance", "auc", "misclass"),
plot_min = TRUE,
ci_alpha = 0.2,
ci_border = FALSE,
ci_col = lattice::trellis.par.get("superpose.line")$col,
...
)
```

x

an object of class `'TrainedOwl'`

, typically from a call
to `trainOwl()`

measure

any of the measures used in the call to `trainOwl()`

. If
`measure = "auto"`

then deviance will be used for binomial and multinomial
models, whilst mean-squared error will be used for Gaussian and Poisson
models.

plot_min

whether to mark the location of the penalty corresponding to the best prediction score

ci_alpha

alpha (opacity) for fill in confidence limits

ci_border

color (or flag to turn off and on) the border of the confidence limits

ci_col

color for border of confidence limits

...

other arguments that are passed on to `lattice::xyplot()`

An object of class `'trellis'`

is returned and, if used
interactively, will most likely have its print function
`lattice::print.trellis()`

) invoked, which draws the plot on the
current display device.

# NOT RUN { # Cross-validation for a SLOPE binomial model set.seed(123) tune <- trainOwl(subset(mtcars, select = c("mpg", "drat", "wt")), mtcars$hp, q = c(0.1, 0.2), number = 10) plot(tune, ci_col = "salmon", col = "black") # }