HDGLM_perm: Resample based HDGLM test.

In GLMaSPU: An Adaptive Test on High Dimensional Parameters in Generalized Linear Models

Description

HDGLM_perm returns resample based p-value for HDGLM test (Guo 2016).

Usage

HDGLM_perm(Y, X, cov = NULL, model = c("gaussian", "binomial"),
  n.perm = 1000)

Arguments

Y	Response. It can be binary or continuous trait. A vector with length n (number of observations).
X	Genotype or other data; each row for a subject, and each column for a variable of interest. An n by p matrix (n: number of observations, p: number of predictors).
cov	Covariates. An n by q matrix (n: number of observations, q: number of covariates).
model	corresponding to the Response. "gaussian" for a quantitative response; "binomial" for a binary response.
n.perm	number of permutations or bootstraps.

Details

HDGLM_perm calculates the resample based p-value. You can calculate the asymptotic based p-value by using HDGLM_test function in R package HDGLM. Based on our experience, resample based p-value is often similar to the asymptotic based one, except when the signals are highly sparse.

Value

A list object, Ts : test statistics for the SPU tests and the aSPU test. pvs : p-values for the SPU and aSPU tests.

Author(s)

Chong Wu and Wei Pan

References

Guo, B. and S. X. Chen (2016). Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Examples

p = 200
n = 100
beta = c(1,3,3)
s = 0.15
signal.r = 0.08
seed = 2
non.zero = floor(p * s)
alpha = c(rep(signal.r,non.zero),rep(0,p-non.zero))
dat = generate_data(seed, n = n, p = p, beta = beta,alpha = alpha)
cov = dat$Z
X = dat$X
Y = dat$Y
HDGLM_perm(Y, X, cov = cov, model = "gaussian",  n.perm = 1000)

GLMaSPU documentation built on May 2, 2019, 11:24 a.m.