glmnet: elnet.fit – R documentation

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elnet.fit

Solve weighted least squares (WLS) problem for a single lambda value

Description

Solves the weighted least squares (WLS) problem for a single lambda value. Internal function that users should not call directly.

Usage

elnet.fit(
  x,
  y,
  weights,
  lambda,
  alpha = 1,
  intercept = TRUE,
  thresh = 1e-07,
  maxit = 1e+05,
  penalty.factor = rep(1, nvars),
  exclude = c(),
  lower.limits = -Inf,
  upper.limits = Inf,
  warm = NULL,
  from.glmnet.fit = FALSE,
  save.fit = FALSE
)

Arguments

`x`	Input matrix, of dimension `nobs x nvars`; each row is an observation vector. If it is a sparse matrix, it is assumed to be unstandardized. It should have attributes `xm` and `xs`, where `xm(j)` and `xs(j)` are the centering and scaling factors for variable j respsectively. If it is not a sparse matrix, it is assumed that any standardization needed has already been done.
`y`	Quantitative response variable.
`weights`	Observation weights. `elnet.fit` does NOT standardize these weights.
`lambda`	A single value for the `lambda` hyperparameter.
`alpha`	The elasticnet mixing parameter, with 0 ≤ α ≤ 1. The penalty is defined as (1-α)/2\|\|β\|\|_2^2+α\|\|β\|\|_1. `alpha=1` is the lasso penalty, and `alpha=0` the ridge penalty.
`intercept`	Should intercept be fitted (default=TRUE) or set to zero (FALSE)?
`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-7`.
`maxit`	Maximum number of passes over the data; default is `10^5`. (If a warm start object is provided, the number of passes the warm start object performed is included.)
`penalty.factor`	Separate penalty factors can be applied to each coefficient. This is a number that multiplies `lambda` to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in exclude). Note: the penalty factors are internally rescaled to sum to `nvars`.
`exclude`	Indices of variables to be excluded from the model. Default is none. Equivalent to an infinite penalty factor.
`lower.limits`	Vector of lower limits for each coefficient; default `-Inf`. Each of these must be non-positive. Can be presented as a single value (which will then be replicated), else a vector of length `nvars`.
`upper.limits`	Vector of upper limits for each coefficient; default `Inf`. See `lower.limits`.
`warm`	Either a `glmnetfit` object or a list (with names `beta` and `a0` containing coefficients and intercept respectively) which can be used as a warm start. Default is `NULL`, indicating no warm start. For internal use only.
`from.glmnet.fit`	Was `elnet.fit()` called from `glmnet.fit()`? Default is FALSE.This has implications for computation of the penalty factors.
`save.fit`	Return the warm start object? Default is FALSE.

Details

WARNING: Users should not call elnet.fit directly. Higher-level functions in this package call elnet.fit as a subroutine. If a warm start object is provided, some of the other arguments in the function may be overriden.

elnet.fit is essentially a wrapper around a FORTRAN subroutine which minimizes

1/2 ∑ w_i (y_i - X_i^T β)^2 + ∑ λ γ_j [(1-α)/2 β^2+α|β|],

over β, where γ_j is the relative penalty factor on the jth variable. If intercept = TRUE, then the term in the first sum is w_i (y_i - β_0 - X_i^T β)^2, and we are minimizing over both β_0 and β.

None of the inputs are standardized except for penalty.factor, which is standardized so that they sum up to nvars.

Value

An object with class "glmnetfit" and "glmnet". The list returned has the same keys as that of a glmnet object, except that it might have an additional warm_fit key.

`a0`	Intercept value.
`beta`	A `nvars x 1` matrix of coefficients, stored in sparse matrix format.
`df`	The number of nonzero coefficients.
`dim`	Dimension of coefficient matrix.
`lambda`	Lambda value used.
`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.
`jerr`	Error flag, for warnings and errors (largely for internal debugging).
`offset`	Always FALSE, since offsets do not appear in the WLS problem. Included for compability with glmnet output.
`call`	The call that produced this object.
`nobs`	Number of observations.
`warm_fit`	If `save.fit=TRUE`, output of FORTRAN routine, used for warm starts. For internal use only.

glmnet

Lasso and Elastic-Net Regularized Generalized Linear Models

v4.1-1

GPL-2

Authors

Jerome Friedman [aut], Trevor Hastie [aut, cre], Rob Tibshirani [aut], Balasubramanian Narasimhan [aut], Kenneth Tay [aut], Noah Simon [aut], Junyang Qian [ctb]

Initial release

2021-02-17