performance_plot: Model performance plot

In promor: Proteomics Data Analysis and Modeling Tools

Model performance plot

Description

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.

Details

• performance_plot uses resampling results from models included in the model_list to generate plots showing model performance.

• 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.

Value

A ggplot2 object.

Author(s)

Chathurani Ranathunge

See Also

• train_models

• resamples

• train

• trainControl

Examples

## Create a model_df object
covid_model_df <- pre_process(covid_fit_df, covid_norm_df)

## 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)

## Generate box plots to visualize performance of different ML algorithms
performance_plot(covid_model_list)

## Generate dot plots
performance_plot(covid_model_list, type = "dot")

## Change color palette
performance_plot(covid_model_list, type = "dot", palette = "inferno")

promor documentation built on July 26, 2023, 5:39 p.m.