# Cal_grpWTs: An auxilary function calculating the group weighting matrix... In MSGLasso: Multivariate Sparse Group Lasso for the Multivariate Multiple Linear Regression with an Arbitrary Group Structure

## Description

An auxilary function calculating the group weighting matrix `grpWTs` required when calling the MSGlasso function.

## Usage

 `1` ```Cal_grpWTs(P, Q, G, R, gmax, PQ.grps) ```

## Arguments

 `P` a positive interger indicating number of predictor variables `Q` a positive interger indicating number of response variables `G` a positive interger indicating number of predictor groups `R` a positive interger indicating number of response groups `gmax` a positive interger indicating the max number of different groups a single variable (either a predictor or response variable) belongs to. `PQ.grps` a matrix of (p+q) by (gmax+1), with each row starting with group indicators that row variable belongs to, and followed by 999's till the row is filled.

## Details

Generates the required input group weighting matrix `grpWTs` when calling the main MSGlasso function. The `grpWTs` is a g by r matrix containing the adaptive weighting scores for each group. `MSGLasso.grpWTs` use the square root of the group size (number of entries the group contains) as the weight for each group.

## Value

A list with one components:

 `grpWTs ` the `grpWTs` matrix generated

## Author(s)

Yanming Li, Bin Nan, Ji Zhu

## References

Y. Li, B. Nan and J. Zhu (2015) Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics. DOI: 10.1111/biom.12292

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```########################################################################### ## generating the grp.WTs matrix for an overlapping group structure ########################################################################### P <- 200 Q <- 200 G <- 10 R <- 10 gmax <- 1 GarrStarts <-c(0,20,40,60,80,100,120,140,160,180) GarrEnds <-c(19,39,59,79,99,119,139,159,179,199) RarrStarts <-c(0,20,40,60,80,100,120,140,160,180) RarrEnds <-c(19,39,59,79,99,119,139,159,179,199) tmp <- FindingPQGrps(P, Q, G, R, gmax, GarrStarts, GarrEnds, RarrStarts, RarrEnds) PQ.grps <- tmp\$PQgrps tmp1 <- Cal_grpWTs(P, Q, G, R, gmax, PQ.grps) grp.WTs <- tmp1\$grpWTs ```

MSGLasso documentation built on May 29, 2017, 9:34 a.m.