classo: Fits regularization paths for lasso penalized learning...
In Zhe-Research/compReg: Compositional Data Regression

Description Usage Arguments

Fits regularization paths for linear constraints lasso penalized learning problems at a sequence of regularization parameters lambda.

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 np* matrix after taking log transformation on compositional data.
`Zc`	a design matrix of other covariates considered. Default is `NULL`.
`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 `lam` sequence based on `nlam` and `lambda.factor` If `lam` is provided but a scaler, `lam` sequence is also created starting from `lam`. Supplying a value of lambda overrides this. It is better to supply a decreasing sequence of lambda values, if not, the program will sort user-defined `lambda` sequence in decreasing order automatically.
`nlam`	the length of `lam` sequence - default is 100.
`lambda.factor`	the factor for getting the minimal lambda in `lam` sequence, where `min(lam)` = `lambda.factor` * `max(lam)`. `max(lam)` is the smallest value of `lam` for which all penalized group are zero's. The default depends on the relationship between n and p1 If n >= p1 the default is `0.001`, close to zero. If n < p1, the default is `0.05`. A very small value of `lambda.factor` will lead to a saturated fit. It takes no effect if there is user-defined lambda sequence.
`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 `min(dfmax*1.5, p)`.
`u`	`u` is the inital value for penalty parameter of augmented Lanrange method adopted in outer loop - default value is 1.
`mu_ratio`	`mu_ratio` is the increasing ratio for `u` - default value is 1.01. Inital values for scaled Lagrange multipliers are set as 0's. If `mu_ratio` < 1, there is no linear constraints included. Group lasso coefficients are estimated.
`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)) < `tol`, set β_j as 0's. Default value is 0.
`outer_maxiter`	`outer_maxiter` is the maximun munber of loops allowed for Augmented Lanrange method; and `outer_eps` is the convergence termination tolerance.
`outer_eps`	`outer_maxiter` is the maximun munber of loops allowed for Augmented Lanrange method; and `outer_eps` is the convergence termination tolerance.
`inner_maxiter`	`inner_maxiter` is the maximun munber of loops allowed for blockwise-GMD; and `inner_eps` is the convergence termination tolerance.
`inner_eps`	`inner_maxiter` is the maximun munber of loops allowed for blockwise-GMD; and `inner_eps` is the convergence termination tolerance.
`A, b`	linear equality constraints Aβ_p = b, where b is a scaler, and A is a vector with length `p`. Default values, b is a vector of 0 and `A = rep(1, times = p)`.
`beta.ini`	inital value of beta