vows (version 0.5)

rlrt.mp.fit: Massively parallel restricted likelihood ratio tests (internal)

Description

Conducts a possibly very large number of restricted likelihood ratio tests (Crainiceanu and Ruppert, 2004), with specified random-effects design matrix and fixed-effects design matrix, for a polynomial null against a smooth alternative.

Usage

rlrt.mp.fit(Y, X, Z, loginvsp, evalarg = NULL, get.df = FALSE)

Arguments

Y

ordinarily, an $n \times V$ outcome matrix, where $V$ is the number of hypotheses (in brain imaging applications, the number of voxels

X

the fixed-effects design matrix.

Z

the random-effects design matrix.

loginvsp

a grid of candidate values of the log inverse smoothing parameter.

evalarg

if Y is of class "fd", the argument values at which the functions are evaluated.

get.df

logical: Should the effective df of the smooth at each point be obtained?

Value

A list with components A list with components

Details

The RLRsim package of Scheipl et al. (2008) is used to simulate the common null distribution of the RLRT statistics.

References

Crainiceanu, C. M., and Ruppert, D. (2004). Likelihood ratio tests in linear mixed models with one variance component. Journal of the Royal Statistical Society, Series B, 66(1), 165--185.

Scheipl, F., Greven, S. and Kuechenhoff, H. (2008). Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Computational Statistics & Data Analysis, 52(7), 3283--3299.

Examples

Y = matrix(rnorm(6000), nrow=20)
x = rnorm(20)
z = rep(1:5, each = 4)
t4. = rlrt.mp.fit(Y, x, z, loginvsp = -22:0)