A design matrix (X; nrow(X)=n, ncol(X)=p) is generated
by random numbers as previously used in our simulation studies
(Section 5 of Yang and Emura (2017); p.6093).
The design matrix has two blocks of correlated regressors
(Pearson correlation=0.5): the first q regressors and the second r regressors.
Other p-q-r regressors are independent.
If regressors are gene expressions, the correlated blocks may be regarded as
"gene pathways" (Emura et al. 2012).
Usage
X.mat(n, p, q, r)
Value
a matrix X (nrow(X)=n, ncol(X)=p)
Arguments
n
the number of rows (samples)
p
the number of columns (regressors)
q
the number of correlated regressors in the first block (1<=q<p, q+r<p)
r
the number of correlated regressors in the second block (1<=r<p, q+r<p)
References
Yang SP, Emura T (2017) A Bayesian approach with generalized ridge estimation
for high-dimensional regression and testing, Commun Stat-Simul 46(8): 6083-105
Emura T, Chen YH, Chen HY (2012) Survival prediction based on compound covariate method
under Cox proportional hazard models PLoS ONE 7(10) doi:10.1371/journal.pone.0047627