Description Usage Arguments Value
View source: R/constraintsReg.R
Fit a linearly constrained linear model with lasso regularization.
1 2 3 4 5 6 7 | 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-8,
inner_maxiter = 1e+6, inner_eps = 1e-8,
A = rep(1, times = p), b = 0, beta.ini)
|
y |
a response vector with length n. |
Z |
a design matrix, with dimension n*p. |
Zc |
design matrix for unpenalized variables. Default value is NULL. |
intercept |
Boolean, specifying whether to include an intercept. Default is TRUE. |
pf |
penalty factor, a vector of length p. Zero implies no shrinkage. Default value for each entry is 1. |
lam |
a user supplied lambda sequence.
If |
nlam |
the length of the |
lambda.factor |
the factor for getting the minimal lambda in the |
dfmax |
limit the maximum number of groups in the model. Useful for handling 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 re-enters the model through the path, it will be counted only once.
Default is |
u |
the inital value of the penalty parameter of the augmented Lagrange method adopted in the outer loop. Default value is 1. |
mu_ratio |
the increasing ratio, with value at least 1, for |
tol |
tolerance for the estimated coefficients to be considered as non-zero, i.e., if abs(β_j) < |
outer_maxiter, outer_eps |
|
inner_maxiter, inner_eps |
|
A, b |
linear equalities of the form Aβ_p = b, where b is a scaler,
and A is a row-vector of length |
beta.ini |
inital value of the coefficients. Can be unspecified. |
A list of
beta |
a matrix of coefficients. |
lam |
the sequence of lambda values. |
df |
a vector, the number of nonzero coefficients for |
npass |
total number of iteration. |
error |
a vector of error flag. |
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