coxnet.deviance {glmnet}  R Documentation 
Compute deviance for Cox model
Description
Compute the deviance (2 log partial likelihood) for Cox model.
Usage
coxnet.deviance(
pred = NULL,
y,
x = NULL,
offset = NULL,
weights = NULL,
std.weights = TRUE,
beta = NULL
)
Arguments
pred 
Fit vector or matrix (usually from glmnet at a particular lambda or a sequence of lambdas). 
y 
Survival response variable, must be a 
x 
Optional 
offset 
Optional offset vector. 
weights 
Observation weights (default is all equal to 1). 
std.weights 
If TRUE (default), observation weights are standardized to sum to 1. 
beta 
Optional coefficient vector/matrix, to be supplied if

Details
Computes the deviance for a single set of predictions, or for a matrix
of predictions. The user can either supply the predictions
directly through the pred
option, or by supplying the x
matrix
and beta
coefficients. Uses the Breslow approach to ties.
The function first checks if pred
is passed: if so, it is used as
the predictions. If pred
is not passed but x
and beta
are passed, then these values are used to compute the predictions. If
neither x
nor beta
are passed, then the predictions are all
taken to be 0.
coxnet.deviance()
is a wrapper: it calls the appropriate internal
routine based on whether the response is rightcensored data or
(start, stop] survival data.
Value
A vector of deviances, one for each column of predictions.
See Also
coxgrad
Examples
set.seed(1)
eta < rnorm(10)
time < runif(10, min = 1, max = 10)
d < ifelse(rnorm(10) > 0, 1, 0)
y < survival::Surv(time, d)
coxnet.deviance(pred = eta, y = y)
# if pred not provided, it is set to zero vector
coxnet.deviance(y = y)
# example with x and beta
x < matrix(rnorm(10 * 3), nrow = 10)
beta < matrix(1:3, ncol = 1)
coxnet.deviance(y = y, x = x, beta = beta)
# example with (start, stop] data
y2 < survival::Surv(time, time + runif(10), d)
coxnet.deviance(pred = eta, y = y2)
# example with strata
y2 < stratifySurv(y, rep(1:2, length.out = 10))
coxnet.deviance(pred = eta, y = y2)