Description Usage Arguments Value Examples
Solve for local minimum with one dimensional golden section on a L1 lmmen penalty.
1 2 3 | 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
1 2 3 4 5 6 | ## 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)
|
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