varimp_plot {promor}

Variable importance plot

Description

This function visualizes variable importance in models

Usage

varimp_plot(
  model_list,
  ...,
  type = "lollipop",
  text_size = 10,
  palette = "viridis",
  n_row,
  n_col,
  save = FALSE,
  file_path = NULL,
  file_name = "VarImp_plot",
  file_type = "pdf",
  dpi = 80,
  plot_width = 7,
  plot_height = 7
)

Arguments

model_list

A model_list object from performing train_models.

...

Additional arguments to be passed on to varImp.

type

Type of plot to generate. Choices are "bar" or "lollipop." Default is "lollipop."

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.

n_row

Number of rows to print the plots.

n_col

Number of columns to print the plots.

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 "VarImp_plot."

file_type

File type to save the plot. Default is "pdf".

dpi

Plot resolution. Default is 80.

plot_width

Width of the plot. Default is 7.

plot_height

Height of the plot. Default is 7.

Details

• varimp_plot produces a list of plots showing variable importance measures calculated from models generated with different machine-learning algorithms.

• Note: Variables are ordered by variable importance in descending order, and by default, importance values are scaled to 0 and 100. This can be changed by specifying scale = FALSE. See varImp for more information.

Value

A list of ggplot2 objects.

Author(s)

Chathurani Ranathunge

See Also

• train_models, rem_feature

• varImp

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)

## Variable importance - lollipop plots
varimp_plot(covid_model_list)

## Bar plots
varimp_plot(covid_model_list, type = "bar")

## Do not scale variable importance values
varimp_plot(covid_model_list, scale = FALSE)

## Change color palette
varimp_plot(covid_model_list, palette = "magma")