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]