sidatunerange: Tuning paramter grid values for sida

In lasandrall/SIDA: Sparse Integrative Discriminant Analysis for Multi-view Structured Data

Tuning paramter grid values for sida

Description

Sida function to provide tuning parameter grid values for each view, not including covariates, if available. It is recommended to use this to get lower and upper bounds of tuning parameters for each view that can be used in sida. This function is called by cvSIDA to select optimal tuning parameters.

Usage

sidatunerange(Xdata=Xdata,Y=Y,ngrid=8,standardize=TRUE,weight=0.5,withCov=FALSE)

Arguments

Xdata	A list with each entry containing each n\times p_d training view, where d =1,...,D views. Rows are samples and columns are variables. If covariates are available, they should be included as a separate view, and set as the last dataset. For binary or categorical covariates (assumes no ordering), we suggest the use of indicator variables.
Y	n \times 1 vector of class membership. Same size as the number of training samples.
ngrid	Number of grid points for tuning parameters.
standardize	TRUE or FALSE. If TRUE, data will be normalized to have mean zero and variance one for each variable. Default is TRUE.
weight	Balances separation and association. Default is 0.5.
withCov	TRUE or FALSE if covariates are available. If TRUE, set all covariates as one dataset and should be the last dataset. For binary and categorical variables, use indicator matrices/vectors. Default is FALSE.

Details

The function will return an R object with grid values for each data, not including covariates, if available. To see the results, use the “$" operator.

Value

Tauvec

grid values for each data, not including covariates, if available.

References

Sandra E. Safo, Eun Jeong Min, and Lillian Haine (2019) , Sparse Linear Discriminant Analysis for Multi-view Structured Data, submitted

See Also

sida

Examples

## see examples in sida

lasandrall/SIDA documentation built on Oct. 19, 2022, 9:23 a.m.