gskat_seq_opt2: Perform GEE_SKAT Optimal Test with no covariates

In xfwang/gskat: GEE_KM

Usage

gskat_seq_opt(y, Z, ID, NB=10000, impute.method = "fixed", SNP.weights = NULL, w_a = 1, w_b = 25, resampling = TRUE, pw = "Rade", Uc = TRUE, sW = FALSE, np = 10000)

Arguments

y	binary phenotype coded as 0, 1
Z	SNP genotypes coded 0/1/2 (minor allele count).
ID	Pedigree ID matrix, including Family ID (FID) and Individual ID (IID)
NB	number of comparisons to get p-value, default=10000
impute.method	default is fixed method i.e. fill with means
SNP.weights	If NULL, the default beta (1,25) desensity will be used, or a custimoized weight vector may be used
w_a	The first parameter of the beta density in the weight function
w_b	The second parameter of the beta density in the weight function
resampling	If TRUE, resampling will be applied
pw	r.v. used in the perturbation, "Norm"=Normal , "Rade"=Rademacher
Uc	Score centered or not
sW	standardize weights
np	number of perturbed samples. default=10000

Value

pfinal

Returns p-value

Author(s)

Xuefeng Wang

Examples

       attach(gdata)
       gskat_seq_opt2(y,Z,ID=data.frame(FID=ID$FID,IID=ID$IID),NB=10000)

xfwang/gskat documentation built on May 4, 2019, 1:05 p.m.