| ideal_pcc {HDDesign} | R Documentation |
Determine the Ideal PCC
Description
Determine the probability of correct classification (PCC) for a study employing the ideal classifier. The ideal classifier is constructed assuming we know exactly the important features and their effect size. The ideal PCC is the uppper bound of the PCC of any linear classifiers.
Usage
ideal_pcc(mu0, m, p1 = 0.5)
Arguments
mu0 |
The effect size of the important features. |
m |
The number of the important features. |
p1 |
The prevalence of the group 1 in the population, default to 0.5. |
Value
The PCC of the ideal classifier.
Author(s)
Meihua Wu <meihuawu@umich.edu> Brisa N. Sanchez <brisa@umich.edu> Peter X.K. Song <pxsong@umich.edu> Raymond Luu <raluu@umich.edu> Wen Wang <wangwen@umich.edu>
References
Dobbin, Kevin K., and Richard M. Simon. 2007. "Sample Size Planning for Developing Classifiers Using High-dimensional DNA Microarray Data." Biostatistics 8 (1) (January 1): 101-117.
Examples
ideal_pcc(mu0=0.4, m=10, p1 = 0.6)
#return: 0.8999055