mdr.ca.adj: Function to calculate a post-hoc adjusted prediction estimate...

In MDR: Detect gene-gene interactions using multifactor dimensionality reduction

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

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.

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

...

Note

When determining the high-risk/low-risk status of a genotype combination, the order of combinations uses the convention that the genotypes of the first locus vary the most, based on the function expand.grid. For instance, with 3 genotypes (0,1,2), a two-way interaction results in the following 9 combinations: (0,0), (1,0), (2,0), (0,1), (1,1), (2,1), (0,2), (1,2), (2,2).

Author(s)

Stacey Winham

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)

Example output

Loading required package: lattice
$`adjusted classification accuracy`
[1] 51.76

$`adjusted classification error`
[1] 48.24

MDR documentation built on May 29, 2017, 7:05 p.m.