| X.mat {g.ridge} | R Documentation |
X.mat (generating a design matrix)
Description
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)
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) |
Value
a matrix X (nrow(X)=n, ncol(X)=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
Examples
X.mat(n=10,p=5,q=2,r=2)
X.mat(n=100,p=50,q=10,r=10) # Case I in Section 5 of Yang and Emura (2017)