vows (version 0.4)

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

table

matrix of log restricted likelihood ratio values at each grid point, for each test.

stat

RLRT statistics, i.e., the supremum of the values in table for each test.

logsp

log smoothing parameter at which the supremum of the restricted likelihood ratio is attained for each test.

df

if get.df = TRUE, the effective degrees of freedom corresponding to the log smoothing parameter values in logsp.

sim

values simulated from the null distribution of the restricted likelihood ratio statistic.

pvalue

p-values for the RLRT statistics.

fdr

Benjamini-Hochberg false discovery rates corresponding to the above p-values.

call

the call to the function.

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)