mdr.hr: Function to estimate the accuracy of an MDR model given...
In MDR: Detect gene-gene interactions using multifactor dimensionality reduction

Description Usage Arguments Details Value Author(s) References See Also Examples

Determines the balanced accuracy (mean of sensitivity and specificity) of an MDR model (specified combination of loci and high-risk/low-risk genotype combinations) which minimize balanced accuracy. Is used to determine prediction error estimates in cross-validation (mdr.cv).

1	mdr.hr(split, model, hr, genotype = c(0, 1, 2))

`split`	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
`genotype`	a numeric vector of possible genotypes arising in `split`; 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

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).

List containing:

balanced accuracy

Balanced accuracy estimate (the mean of sensitivity and specificity) of the specified MDR model

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Stacey Winham

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

Velez et al (2007). A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 31, 306-315.

mdr,mdr.cv,mdr.3WS

#load test data
data(mdr1)

#split data into training and testing sets
train<-mdr1[1:125,]
test<-mdr1[-(1:125),]

#define matrix of all two-way combinations of 15 SNPs; this 105 by 2 matrix defines the 105 combinations of two-way interactions to consider 
loci<-t(combn(15,2)) 

#this runs mdr on the training data, considering the two-way combinations in 'loci', saving the top model, and defining the threshold as 1 since the data is balanced
fit<-mdr(train,loci,x=1,ratio=1) 

#estimate balanced accuracy given the MDR best model
acc<-mdr.hr(test,model=fit$models, hr=fit$high)

print(acc)