This function is like `plot_data`

, but shows information about a fit model.

As `plot_data`

, this function uses a `what`

parameter
to dispatch visualization to a number of internal functions. For the fits one can visualize:

(

`what = "CNA"`

) A genome-wide plot of Copy Number Alteration profiles (inferred) per cluster;(

`what = "mixing_proportions"`

) The normalized size of each cluster, per modality;(

`what = "density"`

) The density per cluster and segment, split by modality. By default this is shown only for segments that differ for a segment CNA value among one of the inferred clusters;(

`what = "heatmap"`

) The same heatmap plot from`plot_data`

with`what = "heatmap"`

, where rows are sorted by cluster and cluster annotations reported;(

`what = "scores"`

) The scores used for model selection;(

`what = "posterior_CNA"`

) The posterior probability for CNA values;

This function has the same logic of `plot_data`

with respect to the ellipsis and
input parameters.

`plot_fit(x, what = "CNA", ...)`

- x
An object of class

`rcongasplus`

.- what
Any of

`"CNA"`

,`"density"`

,`"mixing_proportions"`

,`"heatmap"`

or`"scores"`

.- ...
Parameters forwarded to the internal plotting functions.

A `ggplot`

plot, or a more complex `cowplot`

figure.

```
data("example_object")
# Genome-wide segments plo
plot_fit(example_object, what = 'CNA')
#> ✖ No fits available, returning empty plot.
# Density plot
plot_fit(example_object, what = 'density')
#> ✖ No fits available, returning empty plot.
# Mixing proportions
plot_fit(example_object, what = 'mixing_proportions')
#> ✖ No fits available, returning empty plot.
# Fit heatmap
plot_fit(example_object, what = 'heatmap')
#> ✖ No fits available, returning empty plot.
# Scores for model selection
plot_fit(example_object, what = 'scores')
#> ✖ No fits available, returning empty plot.
# Posterior for CNAs
plot_fit(example_object, what = 'posterior_CNA')
#> ✖ No fits available, returning empty plot.
```