coxnet.deviance {glmnet}

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 Surv or stratifySurv object.

x

Optional x matrix, to be supplied if pred = NULL.

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 pred = NULL.

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 right-censored 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)