wtest (version 3.2)

w.diagnosis: W-test Probability Distribution Diagnostic Plot

Description

Diagnostic checking of W-test probability distribution estimation.

Usage

w.diagnosis(data, w.order = c(1, 2), n.rep = 10,
  n.sample = nrow(data), n.marker = ncol(data), hf1 = "default.hf1",
  hf2 = "default.hf2", ...)

Arguments

data

a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1).

w.order

an integer value of 0 or 1. w.order = 1 gives main effect calculation; w.order = 2 gives pairwise calculation.

n.rep

a numeric value, the number of bootstrapping times.

n.sample

a numeric value, the number of samples to use in bootstrapping. Default is the total number of samples in the data.

n.marker

a numeric value, the number of markers to use in bootstrapping. Default is the total number of markers.

hf1

h and f values to calculate main effect, organized as a matrix, with columns (k, h, f), k = 2 to 3. Needed when w.order = 1.

hf2

h and f values to calculate interaction associations, organized as a matrix, with columns (k, h, f), k = 2 to 9. Needed when w.order = 2.

...

graphical parameters.

Details

This function evaluates the input W values of main or interaction effects using a set of null Y by the W-test, and the evaluation is performed in several bootstrap samples to achieve fast and stable output. The W histogram and its theoretical Chi-squared distribution density with f degrees of freedom are plotted indexed by k. Close overlaying of the histogram and the probability density curve indicates that the estimated h and f give a good test statistic probability distribution.

References

Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.

See Also

wtest, hf, w.qqplot

Examples

# NOT RUN {
data(diabetes.geno)
# Please note that parameter B is recommended to be greater than 400.
hf1 <- hf(data = diabetes.geno, w.order = 1, B = 100)
hf2 <- hf(data = diabetes.geno, w.order = 2, B = 50)
w.diagnosis(diabetes.geno, w.order = 1, n.rep = 100, hf1 = hf1, main=NULL, xlab=NULL, ylab=NULL)
w.diagnosis(diabetes.geno, w.order = 2, n.rep = 100, hf2 = hf2, main=NULL, xlab=NULL, ylab=NULL)
# }