glmnet.fit: Fit a GLM with elastic net regularization for a single value...
In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

glmnet.fit

R Documentation

Fit a GLM with elastic net regularization for a single value of lambda

Description

Fit a generalized linear model via penalized maximum likelihood for a single value of lambda. Can deal with any GLM family.

Usage

glmnet.fit(
  x,
  y,
  weights,
  lambda,
  alpha = 1,
  offset = rep(0, nobs),
  family = gaussian(),
  intercept = TRUE,
  thresh = 1e-10,
  maxit = 1e+05,
  penalty.factor = rep(1, nvars),
  exclude = c(),
  lower.limits = -Inf,
  upper.limits = Inf,
  warm = NULL,
  from.glmnet.path = FALSE,
  save.fit = FALSE,
  trace.it = 0
)

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. `glmnet.fit` does NOT standardize these weights.
`lambda`	A single value for the `lambda` hyperparameter.
`alpha`	The elasticnet mixing parameter, with `0 \le \alpha \le 1`. The penalty is defined as `(1-\alpha)/2\|\|\beta\|\|_2^2+\alpha\|\|\beta\|\|_1.` `alpha=1` is the lasso penalty, and `alpha=0` the ridge penalty.
`offset`	A vector of length `nobs` that is included in the linear predictor. Useful for the "poisson" family (e.g. log of exposure time), or for refining a model by starting at a current fit. Default is NULL. If supplied, then values must also be supplied to the `predict` function.
`family`	A description of the error distribution and link function to be used in the model. This is the result of a call to a family function. Default is `gaussian()`. (See `family` for details on family functions.)
`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-10`.
`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.path`	Was `glmnet.fit()` called from `glmnet.path()`? Default is FALSE.This has implications for computation of the penalty factors.
`save.fit`	Return the warm start object? Default is FALSE.
`trace.it`	Controls how much information is printed to screen. If `trace.it=2`, some information about the fitting procedure is printed to the console as the model is being fitted. Default is `trace.it=0` (no information printed). (`trace.it=1` not used for compatibility with `glmnet.path`.)

Details

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

glmnet.fit solves the elastic net problem for a single, user-specified value of lambda. glmnet.fit works for any GLM family. It solves the problem using iteratively reweighted least squares (IRLS). For each IRLS iteration, glmnet.fit makes a quadratic (Newton) approximation of the log-likelihood, then calls elnet.fit to minimize the resulting approximation.

In terms of standardization: glmnet.fit does not standardize x and weights. penalty.factor is standardized so that they sum up to nvars.

Value

An object with class "glmnetfit" and "glmnet". The list returned contains more keys than that of a "glmnet" object.

`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`	A logical variable indicating whether an offset was included in the model.
`call`	The call that produced this object.
`nobs`	Number of observations.
`warm_fit`	If `save.fit=TRUE`, output of C++ routine, used for warm starts. For internal use only.
`family`	Family used for the model.
`converged`	A logical variable: was the algorithm judged to have converged?
`boundary`	A logical variable: is the fitted value on the boundary of the attainable values?
`obj_function`	Objective function value at the solution.