generate.spc.matrix {abess} | R Documentation |
Generate simulated matrix that its principal component are sparse linear combination of its columns.
generate.spc.matrix(
n,
p,
support.size = 3,
snr = 20,
sigma = NULL,
sparse.loading = NULL,
seed = 1
)
n |
The number of observations. |
p |
The number of predictors of interest. |
support.size |
A integer specify the number of non-zero entries in the first column of loading matrix. |
snr |
A positive value controlling the signal-to-noise ratio (SNR).
A larger SNR implies the identification of sparse matrix is much easier.
Default |
sigma |
A numerical vector with length |
sparse.loading |
A |
seed |
random seed. Default: |
The methods for generating the matrix is detailedly described in the APPENDIX A: Data generation Section in Schipper et al (2021).
A list
object comprising:
x |
An |
coef |
The sparse loading matrix used to generate x. |
support.size |
A vector recording the number of non-zero entries in each . |
Model selection techniques for sparse weight-based principal component analysis. de Schipper, Niek C and Van Deun, Katrijn. Journal of Chemometrics. 2021. doi: 10.1002/cem.3289.