impute.mdr {imputeMDR}R Documentation

The Multifactor Dimensionality Reduction (MDR) Analysis for Imcomplete Data

Description

This provides various approaches to handling missing values for the MDR analysis of incomplete data

Usage

impute.mdr(dataset, colresp, cs, combi, cv.fold = 10, na.method = 0, randomize = FALSE)

Arguments

dataset a matrix of SNP data with class variable. genotypes must be coded as allele counts (0,1,2).
colresp location of class variable in the dataset. no missing values are allowed for response variable
cs the value used to indicate "case" for class variable
combi number of SNPs considered simultaneously as predictor variables. an order of interactions to analyze.
cv.fold number of fold in 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
randomize logical. If 'TRUE' the cross validation sets are randomized

Value

min.comb combination with minimum error rate in each cross validation
train.erate training error
test.erate test error
best.combi the best combination

Author(s)

Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and Wonil Chung Maintainer: Junghyun Namkung <jh.namkung@gmail.com>

Examples

## sample data with missing values
data(incomplete)
## analysis example of 2nd order gene-gene interaction test
impute.mdr(incomplete, colresp=1, cs=1, combi=2, cv.fold = 10,na.method=2)

[Package imputeMDR version 1.0 Index]