dietary_survey_IBS: Synthetic data using a dietary survey of patients with...

In ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Synthetic data using a dietary survey of patients with irritable bowel syndrome (IBS)

Description

The data are based on the article "A dietary survey of patients with irritable bowel syndrome". The mean and standard deviation of the table 1 (Foods perceived as causing or worsening irritable bowel syndrome symptoms in the IBS group and digestive symptoms in the healthy comparative group) were used to generate the synthetic data.

Usage

data(dietary_survey_IBS)

Format

A data frame with 400 Instances and 43 attributes (including the class attribute, "class")

Details

The predictors are: bread, wheat, pasta, breakfast_cereal, yeast, spicy_food, curry, chinese_takeaway, chilli, cabbage, onion, garlic, potatoes, pepper, vegetables_unspecified, tomato, beans_and_pulses, mushroom, fatty_foods_unspecified, sauces, chocolate, fries, crisps, desserts, eggs, red_meat, processed_meat, pork, chicken, fish_shellfish, dairy_products_unspecified, cheese, cream, milk, fruit_unspecified, nuts_and_seeds, orange, apple, banana, grapes, alcohol, caffeine

The response variable ("class") consists of two groups: healthy-group (class == 0) vs. the IBS-patients (class == 1)

References

P. Hayes, C. Corish, E. O'Mahony, E. M. M. Quigley (May 2013). A dietary survey of patients with irritable bowel syndrome. Journal of Human Nutrition and Dietetics.

Examples

data(dietary_survey_IBS)

X = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

y = dietary_survey_IBS[, ncol(dietary_survey_IBS)]

ClusterR documentation built on June 22, 2024, 10:28 a.m.