Goeman_perm: Resample based Goeman test.

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

Description

Goeman_perm returns resample based p-value for a test proposed by Goeman (2011).

Usage

Goeman_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

Goeman_perm calculates the resample based p-value. You can calculate the asymptotic based p-value via using R package globaltest. Based on our experience, resample based p-value is often different from the asymptotic based one, except when the dimension of X is larger than the sample size n.

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

Goeman, J. J., Van Houwelingen, H. C. and Finos, L. (2011). Testing against a high-dimensional alternative in the generalized linear model asymptotic type 1 error control. Biometrika, 98(2), 381-390.

Examples

p = 200
n = 100
beta = c(1,3,3)
s = 0.15
signal.r = 0.08
non.zero = floor(p * s)
seed = 2
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
Goeman_perm(Y, X, cov = cov,model="gaussian", n.perm=1000)

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