R: Do a lasso regression with the number of non-zero variables...

lasso_regression_K_fixed.glmnet_multivariate {PhylogeneticEM} | R Documentation

## Do a lasso regression with the number of non-zero variables fixed.

### Description

`lasso_regression_K_fixed` does the following regression : ||Yp-Xp.delta|| + lambda |delta|_1 using the function `glmnet::glmnet` of package `glmnet`, where delta is a vector representing the shifts occurring on the branches. It does a gauss lasso regression using function `lm` on top of it. This function is used in functions `init.EM.lasso`, `segmentation.OU.specialCase.lasso`, ...

### Usage

    lasso_regression_K_fixed.glmnet_multivariate(Yp, Xp, K, root = NULL,
      penscale = rep(1, ncol(Xp)), K_lag = 0)

### Arguments

`Yp` | (transformed) data
`Xp` | (transformed) matrix of regression
`K` | number of non-zero components allowed

### Details

lambda is chosen so that delta has the right number of non zero components. If not possible, either temporarily raise the number of shifts and then select only the shifts with the highest modulus, or if not possible, throw an error.

### Value

E0.gauss the intercept (value at the root)

shifts.gauss the list of shifts found on the branches

[Package PhylogeneticEM version 1.1.0 Index]