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 = 1e10,
outer_maxiter = 3e+08, outer_eps = 1e8,
inner_maxiter = 1e+6, inner_eps = 1e8,
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 reenters 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 nonzero, 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 rowvector 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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