generate.spc.matrix {abess} R Documentation

## Generate matrix with sparse principal component

### Description

Generate simulated matrix that its principal component are sparse linear combination of its columns.

### Usage

generate.spc.matrix(
n,
p,
support.size = 3,
snr = 20,
sigma = NULL,
seed = 1
)


### Arguments

 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 snr = Inf enforces no noise exists. sigma A numerical vector with length p specify the standard deviation of each columns. Default sigma = NULL implies it is determined by snr. If it is supplied, support.size would be omit. sparse.loading A p-by-p sparse orthogonal matrix. If it is supplied, support.size would be omit. seed random seed. Default: seed = 1.

### Details

The methods for generating the matrix is detailedly described in the APPENDIX A: Data generation Section in Schipper et al (2021).

### Value

A list object comprising:

 x An n-by-p matrix. coef The sparse loading matrix used to generate x. support.size A vector recording the number of non-zero entries in each .

### References

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.

[Package abess version 0.4.6 Index]