This function generates Receiver Operating Characteristic (ROC) curves to evaluate models

## Usage

```
roc_plot(
probability_list,
split_df,
...,
multiple_plots = TRUE,
text_size = 10,
palette = "viridis",
save = FALSE,
file_path = NULL,
file_name = "ROC_plot",
file_type = "pdf",
plot_width = 7,
plot_height = 7,
dpi = 80
)
```

## Arguments

- probability_list
A

`probability_list`

object from performing`test_models`

with`type = "prob"`

.- split_df
A

`split_df`

object from performing`split_data`

- ...
Additional arguments to be passed on to

`roc`

.- multiple_plots
Logical. If

`FALSE`

plots all ROC curves representing algorithms included in the`probability_list`

in a single plot.- text_size
Text size for plot labels, axis labels etc. Default is

`10`

.- palette
Viridis color palette option for plots. Default is

`"viridis"`

. See`viridis`

for available options.- save
Logical. If

`TRUE`

saves a copy of the plot in the directory provided in`file_path`

.- file_path
A string containing the directory path to save the file.

- file_name
File name to save the plot. Default is

`"ROC_plot."`

- file_type
File type to save the plot. Default is

`"pdf"`

.- plot_width
Width of the plot. Default is

`7`

.- plot_height
Height of the plot. Default is

`7`

.- dpi
Plot resolution. Default is

`80`

.

## Examples

```
# \donttest{
## Create a model_df object
covid_model_df <- pre_process(covid_fit_df, covid_norm_df)
#> Total number of differentially expressed proteins (8) is less than n_top.
#> None of the proteins show high pair-wise correlation.
#>
#> No highly correlated proteins to be removed.
## Split the data frame into training and test data sets
covid_split_df <- split_data(covid_model_df)
## Fit models using the default list of machine learning (ML) algorithms
covid_model_list <- train_models(covid_split_df)
#>
#> Running svmRadial...
#>
#> Running rf...
#>
#> Running glm...
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#>
#> Running xgbLinear...
#>
#> Running naive_bayes...
#> Done!
# Test a list of models on a test data set and output class probabilities,
covid_prob_list <- test_models(covid_model_list, covid_split_df, type = "prob")
#>
#> Testing svmRadial...
#>
#> Testing rf...
#>
#> Testing glm...
#>
#> Testing xgbLinear...
#>
#> Testing naive_bayes...
#>
#> Done!
## Plot ROC curves separately for each ML algorithm
roc_plot(covid_prob_list, covid_split_df)
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
## Plot all ROC curves in one plot
roc_plot(covid_prob_list, covid_split_df, multiple_plots = FALSE)
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
## Change color palette
roc_plot(covid_prob_list, covid_split_df, palette = "plasma")
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Setting levels: control = Non.Severe, case = Severe
#> Setting direction: controls > cases
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
#> Warning: number of columns of result is not a multiple of vector length (arg 3)
# }
```