classo.path: fit a complex-valued lasso for a path of lambda values

Description

Fit a complex-valued lasso formulation for a path of lambda values. classo.path solves the Lasso problem for a path of lambda values.

Usage

classo.path(
  x,
  y,
  weights = NULL,
  standardize = FALSE,
  lambda = NULL,
  nlambda = 100,
  lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04),
  intercept = FALSE,
  thresh = 1e-10,
  maxit = 1e+05,
  trace.it = 0,
  ...
)

Value

An object with class "classofit" and "classo".

a0: Intercept sequence of length length(lambda).
beta: A nvars x length(lambda) matrix of coefficients, stored in sparse matrix format.
df: The number of nonzero coefficients for each value of lambda.
dim: Dimension of coefficient matrix.
lambda: The actual sequence of lambda values used. When alpha=0, the largest lambda reported does not quite give the zero coefficients reported (lambda=inf would in principle). Instead, the largest lambda for alpha=0.001 is used, and the sequence of lambda values is derived from this.
dev.ratio: The fraction of (null) deviance explained. The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat - loglike), where loglike_sat is the log-likelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1-dev/nulldev.
nulldev: Null deviance (per observation). This is defined to be 2*(loglike_sat -loglike(Null)). The null model refers to the intercept model.
npasses: Total passes over the data summed over all lambda values.
jerr: Error flag, for warnings and errors (largely for internal debugging).
call: The call that produced this object.
family: Family used for the model.
nobs: Number of observations.

Arguments

x: Complex-valued input matrix, of dimension nobs by nvar; each row is an observation vector.
y: Complex-valued response variable, nobs dimensional vector.
weights: Observation weights. Default is 1 for each observation.
standardize: Logical flag for x variable standardize beforehand; i.e. for n and p by nobs and nvar, $$\|X_j\|=\sqrt{n} \textrm{for all }j=1,\ldots,p$$ is satisfied for the input x. Default is FALSE.
lambda: A user supplied lambda sequence. Default is NULL.
nlambda: The number of lambda values. Default is 100.
lambda.min.ratio: If nobs < nvars, the default is 0.01.
intercept: Should intercept be set to zero (default=FALSE) or fitted (FALSE)? This default is reversed from glmnet package.
thresh: Convergence threshold for coordinate descent. Each inner coordinate-descent loop continues until the maximum change in the objective after any coefficient update is less than thresh times the null deviance. Default value is 1e-10.
maxit: Maximum number of iterations of outer loop. Default 10,000.
trace.it: Controls how much information is printed to screen. Default is trace.it=0 (no information printed). If trace.it=1, a progress bar is displayed. If trace.it=2, some information about the fitting procedure is printed to the console as the model is being fitted.
...: Other arguments that can be passed to classo