golden_section: Golden section grid search on a lmmen penalty
In yonicd/lmmen: Linear Mixed Model Elastic Net

Description Usage Arguments Value Examples

Solve for local minimum with one dimensional golden section on a L1 lmmen penalty.

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golden_section(dat, init.beta, pen.effect = "FE.L1", opt.lb = 0,
  opt.ub = 1, opt.maxiter = 100, opt.tol = 0.1, opt.tau = (sqrt(5) -
  1)/2)

`dat`	matrix, matrix that includes y (response),X (population covariates), Z (random effects covariates (not incl random intercept))
`init.beta`	numeric, initial fixed effects estimates
`pen.effect`	character,which penalty to search on c('FE.L1','RE.L1',FE.L2”,'RE.L2'), Default: 'FE.L1'
`opt.lb`	numeric, start of search interval, Default: 0
`opt.ub`	numeric, end of search interval, Default: 1
`opt.maxiter`	numeric, maximum iterations to search, Default: 100
`opt.tol`	numeric, accuracy value, Default: 0.1
`opt.tau`	numeric, golden proportion coefficient (~0.618) Default: (sqrt(5) - 1)/2

lmmen list object inluding lmmen fit object of min BIC solution and summary statistics from the grid searc

## Not run:  
 dat <- initialize_example(n.i = 5,n = 30,q=4,seed=1)
 init <- init.beta(dat,method='glmnet')
 golden_section(dat,init,pen.effect = 'FE.L1')
 
## End(Not run)