MDR (version 1.2)

mdr.cv: A function to perform MDR on a dataset using k-fold cross-validation for internal validation.

Description

Determines the best MDR model up to a specified size of interaction K by minimizing balanced accuracy (mean of sensitivity and specificity), while using a k-fold cross-validation internal validation method. The function mdr.cv is essentially a wrapper for the function mdr.

Usage

mdr.cv(data, K, cv, ratio = NULL, equal = "HR", 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)

K

the highest level of interaction to consider

cv

the number of cross-validation intervals; for k-fold cross-validation, cv=k

ratio

the case/control ratio threshold to ascribe high-risk/low-risk status of a genotype combination

equal

how to treat genotype combinations with case/control ratio equal to the threshold; default is "HR" for high-risk, but can also consider "LR" for low-risk

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

An object of class 'mdr', which is a list containing:

final model

a numeric vector of the predictors included in the final model

final model accuracy

the balanced accuracy of the final model from the validation set

top models

a list containing the best model (with minimum BA) for each level of interaction, from 1 to K

top model accuracies

a matrix containing the training, testing, and validation accuracies for each level of interaction, from 1 to K

high-risk/low-risk

a vector of the high-risk/low-risk parameterizations of the genotype combinations for the final model

genotypes

the numeric vector of possible genotypes specified

validation method

"CV", since cross-validation was utilized for internal validation

...

Warning

MDR is a combinatorial search approach, so considering high-order interactions (i.e. large values for K) can be computationally expensive.

Details

MDR is a non-parametric data-mining approach to variable selection designed to detect gene-gene or gene-environment interactions in case-control studies. This function uses balanced accuracy as the evaluation measure to rank potential models. An overall best model is chosen to minimize balanced accuracy, while also preventing model over-fitting with internal validation. This function uses cv-fold cross-validation to separate the data into training and testing sets. The data is randomly separated into cv equal pieces and cv-1/cv of the data is used for training/model-building and 1/cv for testing/prediction; this procedure is repeated cv times.

References

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.

Hahn LW, Ritchie MD, Moore JH (2003). Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19(3):376-82.

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

Motsinger AA, Ritchie MD (2006). The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction. Genet Epidemiol 30(6):546-55.

See Also

mdr.3WS, mdr, boot.error, mdr.ca.adj, permute.mdr, plot.mdr, predict.mdr, summary.mdr

Examples

#load test data
data(mdr1)

fit<-mdr.cv(data=mdr1[,1:11], K=2, cv=5, ratio = NULL, equal = "HR", genotype = c(0, 1, 2)) #fit MDR with 5-fold cross-validation to a subset of the sample data, allowing for 1 to 2-way interactions

print(fit) #view the fitted mdr object

summary(fit) #create summary table of best MDR model

plot(fit, data=mdr1) #create contingency plot of best MDR model; may need to expand the plot window for large values of K