Fits regularization paths for linear constraints lasso penalized learning problems at a sequence of regularization parameters lambda.
1 2 3 4 5 6 | classo(y, Z, Zc = NULL, intercept = TRUE, pf = rep(1, times = p),
lam = NULL, nlam = 100, lambda.factor = ifelse(n < p, 0.05, 0.001),
dfmax = p, pfmax = min(dfmax * 1.5, p), u = 1, mu_ratio = 1.01,
tol = 1e-10, outer_maxiter = 3e+08, outer_eps = 1e-08,
inner_maxiter = 1e+06, inner_eps = 1e-08, A = rep(1, times = p),
b = 0, beta.ini)
|
y |
a vector of response variable with length n. |
Z |
a n*p matrix after taking log transformation on compositional data. |
Zc |
a design matrix of other covariates considered. Default is |
intercept |
Whether to include intercept in the model. Default is TRUE. |
pf |
penalty factor, a vector in length of p. Separate penalty weights can be applied to each coefficience β for composition variates to allow differential shrinkage. Can be 0 for some β's, which implies no shrinkage, and results in that composition always being included in the model. Default value for each entry is the 1. |
lam |
a user supplied lambda sequence. Typically, by leaving this option unspecified users can have the
program compute its own |
nlam |
the length of |
lambda.factor |
the factor for getting the minimal lambda in |
dfmax |
limit the maximum number of groups in the model. Useful for very large p, if a partial path is desired - default is p. |
pfmax |
limit the maximum number of groups ever to be nonzero. For example once a group enters the
model along the path, no matter how many times it exits or re-enters model through the path,
it will be counted only once. Default is |
u |
|
mu_ratio |
|
tol |
tolerance for vectors beta'ss to be considered as none zero's. For example, coefficient
β_j for group j, if max(abs(β_j)) < |
outer_maxiter |
|
outer_eps |
|
inner_maxiter |
|
inner_eps |
|
A, b |
linear equality constraints Aβ_p = b, where b is a scaler,
and A is a vector with length |
beta.ini |
inital value of beta |
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