BIC_IS: Variable selection for regularized SCA using BIC and Index of...

In ZhengguoGu/RegularizedSCA: Regularized Simultaneous Component Based Data Integration

Description

BIC_IS is used to find the proper combination of Lasso and Group Lasso tuning parameters for regularized SCA based on BIC and Index of Sparseness.

Usage

BIC_IS(DATA, Jk, R, NRSTARTS, MaxIter, LassoSequence, GLassoSequence,
  method)

Arguments

DATA	The concatenated data block, with rows representing subjects.
Jk	A vector. Each element of this vector is the number of columns of a data block.
R	The number of components (R>=2).
NRSTARTS	The number of multistarts for this algorithm. The default value is 5.
MaxIter	Maximum number of iterations for this algorithm. The default value is 400.
LassoSequence	The range of Lasso tuning parameters. The default value is a sequence of 20 numbers from 0.00000001 to the smallest Lasso tuning parameter value that makes all the component loadings equal to zero. Note that by default the 20 numbers are equally spaced on the log scale.
GLassoSequence	The range of Group Lasso tuning parameters. The default value is a sequence of 20 numbers from 0.00000001 to the smallest Group Lasso tuning parameter value that makes all the component loadings equal to zero. Note that by default the 20 numbers are equally spaced (but not on the log scale).
method	"datablock" or "component". These are two options with respect to the grouping of the loadings as used in the Group Lasso penalty. If method="component", the block-grouping of the coefficients is applied per component separately. If method = "datablock", the grouping is applied on the concatenated data block, with loadings of all components together. If method is missing, then the "component" method is used by default.

Value

BIC	A matrix of BIC values.
IS	A matrix of IS values.
BIC_tuning	Recommended tuning parameters for Lasso and Group Lasso based on BIC
IS_tuning	Recommended tuning parameters for Lasso and Group Lasso based on IS

References

Gajjar, S., Kulahci, M., & Palazoglu, A. (2017). Selection of non-zero loadings in sparse principal component analysis. Chemometrics and Intelligent Laboratory Systems, 162, 160-171.

Trendafilov, N. T. (2014). From simple structure to sparse components: a review. Computational Statistics, 29(3-4), 431-454.

Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse principal component analysis. Journal of computational and graphical statistics, 15(2), 265-286.

Croux, C., Filzmoser, P., & Fritz, H. (2013). Robust sparse principal component analysis. Technometrics, 55(2), 202-214.

Guo, J., James, G., Levina, E., Michailidis, G., & Zhu, J. (2010). Principal component analysis with sparse fused loadings. Journal of Computational and Graphical Statistics, 19(4), 930-946.

Examples

## Not run: 
DATA1 <- matrix(rnorm(50), nrow=5)
DATA2 <- matrix(rnorm(100), nrow=5)  
DATA <- cbind(DATA1, DATA2)
Jk <- c(10, 20) 
BIC_IS(DATA, Jk, R=5, NRSTARTS = 40, MaxIter = 100)

## End(Not run)

ZhengguoGu/RegularizedSCA documentation built on July 4, 2019, 2:46 p.m.