| wpbc {mboost} | R Documentation |
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.
data("wpbc")
A data frame with 198 observations on the following 34 variables.
statusN (nonrecur) and
R (recur)timestatus == "R") or
disease-free time (for status == "N"). mean_radiusmean_texturemean_perimetermean_areamean_smoothnessmean_compactnessmean_concavitymean_concavepointsmean_symmetrymean_fractaldimSE_radiusSE_textureSE_perimeterSE_areaSE_smoothnessSE_compactnessSE_concavitySE_concavepointsSE_symmetrySE_fractaldimworst_radiusworst_textureworst_perimeterworst_areaworst_smoothnessworst_compactnessworst_concavityworst_concavepointsworst_symmetryworst_fractaldimtsizepnodesThe 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 = non-recurrent - Dataset should first be filtered to reflect a particular endpoint; e.g., recurrences before 24 months = positive, non-recurrence 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/.
W. Nick Street, Olvi L. Mangasarian and William 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 (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
data("wpbc", package = "mboost")
### fit logistic regression model with 100 boosting iterations
coef(glmboost(status ~ ., data = wpbc[,colnames(wpbc) != "time"],
family = Binomial()))