Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function) are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package.

No abstract

The ORA provides reproducible corneal biomechanical and IOP measurements in nonoperated eyes. Considering the effect of ORA, corneal biomechanical metrics produces an outcome-significant IOP adjustment in at least one quarter of glaucomatous and normal eyes undergoing noncontact tonometry. Corneal viscoelasticity (CH) and resistance (CRF) appear to decrease minimally with increasing age in healthy adults.

Cirrus OCT has better scan quality than Stratus OCT, especially in glaucomatous eyes. In cases with good-quality scans, the sensitivity and specificity, and AUCs were similar. The best agreement was in the global average RNFL classification. The widths of limits of agreements exceed the limits of resolution of the OCTs.

No abstract