coxphSGD {coxphSGD} | R Documentation |
coxphSGD
estimates coefficients using stochastic
gradient descent algorithm in Cox proportional hazards model.
coxphSGD(formula, data, learn.rates = function(x) { 1/x },
beta.zero = 0, epsilon = 1e-05, max.iter = 500, verbose = FALSE)
formula |
a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function. |
data |
a list of batch data.frames in which to interpret the variables named in the |
learn.rates |
a function specifing how to define learning rates in
steps of the algorithm. By default the |
beta.zero |
a numeric vector (if of length 1 then will be replicated) of length
equal to the number of variables after using |
epsilon |
a numeric value with the stop condition of the estimation algorithm. |
max.iter |
numeric specifing maximal number of iterations. |
verbose |
whether to cat the number of the iteration |
A data
argument should be a list of data.frames, where in every batch data.frame
there is the same structure and naming convention for explanatory and survival (times, censoring)
variables. See Examples.
If one of the conditions is fullfiled (j denotes the step number)
||\beta_{j+1}-\beta_{j}|| <
epsilon
parameter for any j
j>max.iter
the estimation process is stopped.
Marcin Kosinski, m.p.kosinski@gmail.com
library(survival)
set.seed(456)
x <- matrix(sample(0:1, size = 20000, replace = TRUE), ncol = 2)
head(x)
dCox <- dataCox(10^4, lambda = 3, rho = 2, x,
beta = c(2,2), cens.rate = 5)
batch_id <- sample(1:90, size = 10^4, replace = TRUE)
dCox_split <- split(dCox, batch_id)
results <-
coxphSGD(formula = Surv(time, status) ~ x.1+x.2,
data = dCox_split,
epsilon = 1e-5,
learn.rates = function(x){1/(100*sqrt(x))},
beta.zero = c(0,0),
max.iter = 10*90)
coeff_by_iteration <-
as.data.frame(
do.call(
rbind,
results$coefficients
)
)
head(coeff_by_iteration)