kgroups_surv: k-groups of survival curves

Description Usage Arguments Value Author(s) Examples

View source: R/kgroups_surv.R

Description

Function for grouping survival curves, given a number k, based on the k-means or k-medians algorithm.

Usage

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kgroups_surv(time, status, fac, k = K, kbin = 50, algorithm = "kmeans",
  seed = NULL)

Arguments

time

Survival time.

status

Censoring indicator of the survival time of the process; 0 if the total time is censored and 1 otherwise.

fac

Categoriacl variable indicating the population to which the subject belongs

k

An integer specifying the number of groups of curves to be performed.

kbin

Size of the grid over which the survival functions are to be estimated.

algorithm

A character string specifying which clustering algorithm is used, i.e., k-means("kmeans") or k-medians ("kmedians").

seed

Seed to be used in the procedure.

Value

A list containing the following items:

measure

A measure of...

levels

Original levels of the variable fac.

cluster

A vector of integers (from 1:k) indicating the cluster to which each curve is allocated.

centers

An object of class survfit containing the centroids (mean of the curves pertaining to the same group).

curves

An object of class survfit containing the survival curves for each population.

Author(s)

Marta Sestelo, Nora M. Villanueva.

Examples

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library(clustcurv)
library(survival)
data(veteran)

# 2 groups k-means
cl2 <- kgroups_surv(time = veteran$time, status = veteran$status,
fac = veteran$celltype, k = 2, algorithm = "kmeans")

data.frame(level = cl2$level, cluster = cl2$cluster)


# 2 groups k-medians
cl2 <- kgroups_surv(time = veteran$time, status = veteran$status,
fac = veteran$celltype, k = 2, algorithm = "kmedians")

data.frame(level = cl2$level, cluster = cl2$cluster)



# 3 groups
cl3 <- kgroups_surv(time = veteran$time, status = veteran$status,
fac = veteran$celltype, k = 3, algorithm = "kmeans")

data.frame(level = $level, cluster = cl3$cluster)

sestelo/clustcurv documentation built on Oct. 20, 2017, 10:30 p.m.