Description Usage Arguments Details Value Author(s) References See Also Examples
Conducts a possibly very large number of restricted likelihood ratio tests (Crainiceanu and Ruppert, 2004), with common design matrix, for a polynomial null against a smooth alternative.
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Y |
ordinarily, an n \times V outcome matrix, where V is
the number of hypotheses (in brain imaging applications, the number of
voxels). Can also be given by an object of class " |
x |
a vector or matrix of covariates. |
loginvsp |
a grid of candidate values of the log inverse smoothing parameter. |
nbasis |
number of B-spline basis functions. |
norder |
order of B-splines. |
nulldim |
dimension of the null space of the penalty. |
evalarg |
if |
get.df |
logical: Should the effective df of the smooth at each point be obtained? |
B |
evaluation matrix of the B-spline basis functions. |
P |
penalty matrix. |
The RLRsim package of Scheipl et al. (2008) is used to simulate the common null distribution of the RLRT statistics.
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 |
logsp |
log smoothing parameter at which the supremum of the restricted likelihood ratio is attained for each test. |
df |
if |
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. |
Lei Huang huangracer@gmail.com and Philip Reiss phil.reiss@nyumc.org
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.
Reiss, P. T., Huang, L., Chen, Y.-H., Huo, L., Tarpey, T., and Mennes, M. (2014). Massively parallel nonparametric regression, with an application to developmental brain mapping. Journal of Computational and Graphical Statistics, Journal of Computational and Graphical Statistics, 23(1), 232–248.
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.
rlrt4d
, and Fdr.rlrt
for a more
sophisticated false discovery rate procedure.
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