Usage
impute.mdr(dataset, colresp, cs, combi, cv.fold = 10, na.method = 0, max_iter = 30, randomize = FALSE)
Arguments
dataset
A matrix of SNP data with class variable (response; phenotype; disease status). Genotypes must be coded as allele counts (0,1,2). Missing genotypes should be coded as 3
colresp
Column number of class variables in the dataset. No missing value is allowed for the class variable
cs
The value used to indicate "case (affected)" for class variable
combi
The number of SNPs considered simultaneously as predictor variables (An order of interactions to analyze)
cv.fold
The number of folds k for k-fold cross-validation
na.method
Options for missing handling approaches.
na.method = 0 for complete data, na.method = 1 for treating missing genotypes
as another genotype category, na.method=2 for using available data for given
number of SNPs under consideration as a model, na.method=3 for using method
of imputing missing information by using EM (expectation-maximization) algorithm
max_iter
The number of maximum iteration in EM impute approach (na.method=3). In order to apply one-step EM approach, set this argument as 1
randomize
Logical. If 'TRUE' the cross validation sets are randomized