rmlnomogram (version 0.1.2)

create_nomogram: Construct nomogram for a machine learning model

Description

This function constructs a nomogram for either binary or continuous outcomes based on provided sample features and outputs. It can also incorporate feature explainability values, such as SHAP values.

Usage

create_nomogram(
  sample_features,
  sample_output,
  feature_exp = NULL,
  threshold = 0.5,
  prob = FALSE,
  est = FALSE,
  verbose = FALSE
)

Value

A ggplot object representing the nomogram.

Arguments

sample_features

A data frame of feature values where each column represents a feature. The data frame must contain all possible combinations of feature values. There must be at least one categorical predictor and no more than one numerical predictor. Only factor and numeric data types are allowed. The column name 'output' is not allowed. Must not contain any NA values.

sample_output

A data frame with one column 'output' containing numeric values for either the predicted probabilities (for binary outcomes) or estimated values (for continuous outcomes). Must not contain any NA values.

feature_exp

Optional data frame containing feature explainability values (e.g., SHAP values) with one column for each feature. The structure must match sample_features in terms of column names. Each column must contain numeric values. Must not contain any NA values.

threshold

A numeric scalar between 0 and 1, used to define the threshold for classifying predicted probabilities into binary outcomes. A sample is predicted positive if the predicted probability is equal or greater than this threshold.

prob

A logical scalar indicating if the predicted probabilities should be shown in the nomogram.

est

A logical scalar indicating if the estimated values should be shown in the nomogram.

verbose

A logical scalar indicating whether to show a progress bar if it is required.

Examples

# Binary outcome (or class-wise multinomial outcome)

## 1 - Categorical predictors and binary outcome without probability
data(nomogram_features)
data(nomogram_outputs)
create_nomogram(nomogram_features, nomogram_outputs)

## 2 - Categorical predictors and binary outcome with probability
create_nomogram(nomogram_features, nomogram_outputs, prob = TRUE)

data(nomogram_shaps)
create_nomogram(
  nomogram_features, nomogram_outputs, nomogram_shaps
  , prob = TRUE
)

## 3 - Categorical and 1 numerical predictors and binary outcome with probability
data(nomogram_features2)
data(nomogram_outputs2)
create_nomogram(nomogram_features2, nomogram_outputs2, prob = TRUE)

data(nomogram_shaps2)
create_nomogram(
  nomogram_features2, nomogram_outputs2, nomogram_shaps2
  , prob = TRUE
)

# Continuous outcome

## 4 - Categorical predictors and continuous outcome
data(nomogram_features3)
data(nomogram_outputs3)
create_nomogram(nomogram_features3, nomogram_outputs3, est = TRUE)

data(nomogram_shaps3)
create_nomogram(
  nomogram_features3, nomogram_outputs3, nomogram_shaps3
  , est = TRUE
)

## 5 - Categorical and 1 numerical predictors and continuous outcome
data(nomogram_features4)
data(nomogram_outputs4)
create_nomogram(nomogram_features4, nomogram_outputs4, est = TRUE)

data(nomogram_shaps4)
create_nomogram(
  nomogram_features4, nomogram_outputs4, nomogram_shaps4
  , est = TRUE
)