MDR (version 1.2)

mdr.ca.adj: Function to calculate a post-hoc adjusted prediction estimate of classification accuracy, corrected for prospective data with previously estimated population prevalence

Description

After fitting an object of class 'mdr' and obtaining a best model, calculate an adjusted estimate of classification accuracy to be used for prediction that accounts for retrospective sampling and incorporates disease prevalence, as implemented in Winham and Motsinger-Reif 2010.

Usage

mdr.ca.adj(data, model, hr, prev, genotype = c(0, 1, 2))

Arguments

data

the dataset; an n by (p+1) matrix where the first column is the binary response vector (coded 0 or 1) and the remaining columns are the p SNP genotypes (coded numerically)

model

a numeric vector of the final MDR model loci

hr

vector of binary indicators for high-risk/low-risk of the genotype combinations of the final model loci

prev

an estimate of population prevalence

genotype

a numeric vector of possible genotypes arising in data; default is c(0,1,2), but this vector can be longer or shorter depending on if more or fewer than three genotypes are possible

Value

List containing:

adjusted classification accuracy

post-hoc prediction estimate of classification accuracy adjusted for prevalence, measured as a percentage

adjusted classification error

post-hoc prediction estimate of classification error (100-classification accuracy) adjusted for prevalence

...

Details

MDR provides a prediction error estimate of the final model calculated from retrospective data. To provide a prospective prediction estimate, an accurate estimate of the population prevalence rate must be incorporated.

References

Ritchie MD et al (2001). Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hm Genet 69(1): 138-147.

Winham SJ and Motsinger AA (2010). The effect of retrospective sampling on estimates of prediction error for multifactor dimensionality reduction. Annals of Human Genetics.

See Also

mdr.cv, mdr.3WS, boot.error

Examples

#load test data
data(mdr1)

#this runs mdr with 5-fold cross-validation on a subset of the sample data, considering all pairwise combinations (K=2)
fit<-mdr.cv(mdr1[,1:11],K=2,cv=5) 

#calculates adjusted CA estimate from the sample data for the previously fit MDR object 'fit', assuming the population prevalence is 10%
mdr.ca.adj(mdr1, model=fit$'final model', hr=fit$'high-risk/low-risk', prev=0.10)