lb_convex {lb} | R Documentation |
Fitting Log-Binomial Models by Convex Optimization
lb_convex(x, y, method = c("dense", "sparse"), tol = 1e-08, ceps = 1e-07, control = list(), dry_run = FALSE, solver = "ecos")
x |
design matrix of dimension |
y |
vector of observations of length |
method |
a character string giving the method used to construct the optimization problem,
possible options are |
tol |
tolerance for the optimizer (default is |
ceps |
epsilon subtracted from the right hand side (X β ≤q 0 - ceps). Since the inequality X β ≤q 0 is only fullfilled to a certain tolerance we have to substact epsilon to ensure X β ≤q 0. |
control |
a list containing additional arguments passed to |
dry_run |
a logical controling if the problem should be solved
(default |
solver |
a character string selecting the solver to be used (default is |
a vector giving the estimated coefficients.
d <- simdata_blizhosm2006(500, 1) lb_convex(d$x, d$y)