Description Usage Arguments Details Value Note Author(s) References See Also Examples
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.
1 | mdr.ca.adj(data, model, hr, prev, genotype = c(0, 1, 2))
|
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 |
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.
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 |
...
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 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.
1 2 3 4 5 6 7 8 | #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)
|
Loading required package: lattice
$`adjusted classification accuracy`
[1] 51.76
$`adjusted classification error`
[1] 48.24
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