elnet.fit {glmnet}  R Documentation 
Solves the weighted least squares (WLS) problem for a single lambda value. Internal function that users should not call directly.
elnet.fit(
x,
y,
weights,
lambda,
alpha = 1,
intercept = TRUE,
thresh = 1e07,
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
)
x 
Input matrix, of dimension 
y 
Quantitative response variable. 
weights 
Observation weights. 
lambda 
A single value for the 
alpha 
The elasticnet mixing parameter, with

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.fit 
Was 
save.fit 
Return the warm start object? Default is FALSE. 
WARNING: Users should not call elnet.fit
directly. Higherlevel 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 C++ subroutine which
minimizes
1/2 \sum w_i (y_i  X_i^T \beta)^2 + \sum \lambda \gamma_j
[(1\alpha)/2 \beta^2+\alpha\beta],
over \beta
, where \gamma_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  \beta_0  X_i^T \beta)^2
, and we are minimizing over both
\beta_0
and \beta
.
None of the inputs are standardized except for penalty.factor
, which
is standardized so that they sum up to nvars
.
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 
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 
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 