R: Fit a GLM with elastic net regularization for a single value...

spglmnet.fit {glmnet}

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. This version should be called if x is a sparse matrix.

Usage

spglmnet.fit(x, xm, xs, 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.
`xm`	Vector of length `nvars`: `xm(j)` is the centering factor for variable j.
`xs`	Vector of length `nvars`: `xs(j)` is the scaling factor for variable j.
`y`	Quantitative response variable.
`weights`	Observation weights. `spglmnet.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.
`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`	A `glmnetfit` object which can be used as a warm start. Default is `NULL`, indicating no warm start. For internal use only.
`from.glmnet.path`	Was `spglmnet.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 spglmnet.fit directly. Higher-level functions in this package call spglmnet.fit as a subroutine. If a warm start object is provided, some of the other arguments in the function may be overriden.

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

In terms of standardization: spglmnet.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 an "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 FORTRAN 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.

[Package glmnet version 4.0 Index]