ROC plot

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.

Value

A ggplot2 object.

Details

Next, relevant information is extracted from the ROC object to plot the ROC curves.

Author

Chathurani Ranathunge

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: 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: 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: 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: 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)

# }

Usage

Arguments

Value

Details

See also

Author

Examples