Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
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
.
1 |
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 |
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.
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 |
top model accuracies |
a matrix containing the training, testing, and validation accuracies for each level of interaction, from 1 to |
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 |
...
MDR is a combinatorial search approach, so considering high-order interactions (i.e. large values for K
) can be computationally expensive.
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).
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.
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.
mdr.3WS
, mdr
, boot.error
, mdr.ca.adj
, permute.mdr
, plot.mdr
, predict.mdr
, summary.mdr
1 2 3 4 5 6 7 8 9 10 | #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
|
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