admissions

<p>This is a simulated data set that represents admissions information for a graduate program in the sciences.  The binary outcome is 1 for admitted and 0 for not admitted.  This data set is meant to be used with the <code>ci.cvAUC</code> function.</p>

datasets

Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

Erin LeDell

cvAUC

Cross-Validated Area Under the ROC Curve Confidence Intervals

admissions function

<p>A data frame. The five predictor variables are: <code>quant</code>, <code>verbal</code>, <code>gpa</code>, <code>toptier</code> and <code>research</code>.  We can treat <code>quant</code> and <code>verbal</code>, which represent quantitative and verbal GRE scores, as continuous variables.  The binary indicator variables, <code>toptier</code> and <code>research</code>, indicate whether the application is coming from a ``top tier'' institution and whether or not they have prior research experience.  The binary indicator, <code>Y</code>, represents admitted <code>(Y=1)</code> vs. not admitted <code>(Y=0)</code>.</p>

admissions: Data set: Simulated Admissions Data with Binary Outcome

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