mboost (version 0.4-6)

wpbc: Wisconsin Prognostic Breast Cancer Data

Description

Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis.

Usage

data("wpbc")

source

W. N. Street, O. L. Mangasarian, and W. H. Wolberg (1995). An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522--530, San Francisco, Morgan Kaufmann.

Peter Buhlmann and Torsten Hothorn (2006), Boosting: A statistical perspective. Submitted manuscript.

Details

The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

There are two possible learning problems: predicting status or predicting the time to recur.

1) Predicting field 2, outcome: R = recurrent, N = nonrecurrent - Dataset should first be filtered to reflect a particular endpoint; e.g., recurrences before 24 months = positive, nonrecurrence beyond 24 months = negative. - 86.3 previous version of this data.

2) Predicting Time To Recur (field 3 in recurrent records) - Estimated mean error 13.9 months using Recurrence Surface Approximation.

The data are originally available from the UCI machine learning repository, see http://www.ics.uci.edu/~mlearn/databases/breast-cancer-wisconsin/.

Examples

data("wpbc", package = "mboost")

    ### fit logistic regression model with 100 boosting iterations
    coef(glmboost(status ~ ., data = wpbc[,colnames(wpbc) != "time"], 
                  family = Binomial()))