cox.fit {glmnet}  R Documentation 
Fit a Cox regression model with elastic net regularization for a single value of lambda
Description
Fit a Cox regression model via penalized maximum likelihood for a single value of lambda. Can deal with (start, stop] data and strata, as well as sparse design matrices.
Usage
cox.fit(
x,
y,
weights,
lambda,
alpha = 1,
offset = rep(0, nobs),
thresh = 1e10,
maxit = 1e+05,
penalty.factor = rep(1, nvars),
exclude = c(),
lower.limits = Inf,
upper.limits = Inf,
warm = NULL,
from.cox.path = FALSE,
save.fit = FALSE,
trace.it = 0
)
Arguments
x 
Input matrix, of dimension 
y 
Survival response variable, must be a Surv or stratifySurv object. 
weights 
Observation weights. 
lambda 
A single value for the 
alpha 
See glmnet help file 
offset 
See glmnet help file 
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 
See glmnet help file 
exclude 
See glmnet help file 
lower.limits 
See glmnet help file 
upper.limits 
See glmnet help file 
warm 
Either a 
from.cox.path 
Was 
save.fit 
Return the warm start object? Default is FALSE. 
trace.it 
Controls how much information is printed to screen. If

Details
WARNING: Users should not call cox.fit
directly. Higherlevel
functions in this package call cox.fit
as a subroutine. If a
warm start object is provided, some of the other arguments in the function
may be overriden.
cox.fit
solves the elastic net problem for a single, userspecified
value of lambda. cox.fit
works for Cox regression models, including
(start, stop] data and strata. It solves the problem using iteratively
reweighted least squares (IRLS). For each IRLS iteration, cox.fit
makes a quadratic (Newton) approximation of the loglikelihood, then calls
elnet.fit
to minimize the resulting approximation.
In terms of standardization: cox.fit
does not standardize x
and weights
. penalty.factor
is standardized so that they sum
up to nvars
.
Value
An object with class "coxnet", "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 0 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, always "cox". 
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. 