glmnet.fit {glmnet}  R Documentation 
Fit a generalized linear model via penalized maximum likelihood for a single value of lambda. Can deal with any GLM family.
glmnet.fit(
x,
y,
weights,
lambda,
alpha = 1,
offset = rep(0, nobs),
family = gaussian(),
intercept = TRUE,
thresh = 1e10,
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
)
x 
Input matrix, of dimension 
y 
Quantitative response variable. 
weights 
Observation weights. 
lambda 
A single value for the 
alpha 
The elasticnet mixing parameter, with

offset 
A vector of length 
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 
intercept 
Should intercept be fitted (default=TRUE) or set to zero (FALSE)? 
thresh 
Convergence threshold for coordinate descent. Each inner
coordinatedescent loop continues until the maximum change in the objective
after any coefficient update is less than thresh times the null deviance.
Default value is 
maxit 
Maximum number of passes over the data; default is 
penalty.factor 
Separate penalty factors can be applied to each
coefficient. This is a number that multiplies 
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

upper.limits 
Vector of upper limits for each coefficient; default

warm 
Either a 
from.glmnet.path 
Was 
save.fit 
Return the warm start object? Default is FALSE. 
trace.it 
Controls how much information is printed to screen. If

WARNING: Users should not call glmnet.fit
directly. Higherlevel 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, userspecified
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
loglikelihood, 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
.
An object with class "glmnetfit" and "glmnet". The list returned contains more keys than that of a "glmnet" object.
a0 
Intercept value. 
beta 
A 
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 loglikelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1dev/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 
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. 