Model performance plot

This function generates plots to visualize model performance

Usage

performance_plot(
  model_list,
  type = "box",
  text_size = 10,
  palette = "viridis",
  save = FALSE,
  file_path = NULL,
  file_name = "Performance_plot",
  file_type = "pdf",
  plot_width = 7,
  plot_height = 7,
  dpi = 80
)

Arguments

model_list: A model_list object from performing train_models.
type: Type of plot to generate. Choices are "box" or "dot." Default is "box." for boxplots.
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 "Performance_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

The default metrics used for classification based models are "Accuracy" and "Kappa."
These metric types can be changed by providing additional arguments to the train_models function. See train and trainControl for more information.

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 based on the default list of machine learning (ML) algorithms
covid_model_list <- train_models(covid_split_df)
#> 
#> Running svmRadial...
#> Loading required package: ggplot2
#> Loading required package: lattice
#> 
#> 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: 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: 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!

## Generate box plots to visualize performance of different ML algorithms
performance_plot(covid_model_list)
#> Using Resample as id variables


## Generate dot plots
performance_plot(covid_model_list, type = "dot")
#> Using Resample as id variables
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).


## Change color palette
performance_plot(covid_model_list, type = "dot", palette = "inferno")
#> Using Resample as id variables
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).

# }

Usage

Arguments

Value

Details

See also

Author

Examples