autoplot.clustcurves: Visualization of 'clustcurves' objects with ggplot2 graphics

In clustcurv: Determining Groups in Multiples Curves

Description

Useful for drawing the estimated functions grouped by color and the centroids (mean curve of the curves pertaining to the same group).

Usage

## S3 method for class 'clustcurves'
autoplot(
  object = object,
  groups_by_colour = TRUE,
  centers = FALSE,
  conf.int = FALSE,
  censor = FALSE,
  xlab = "Time",
  ylab = "Survival",
  interactive = FALSE,
  ...
)

Arguments

object	Object of clustcurves class.
groups_by_colour	A specification for the plotting groups by color.
centers	Draw the centroids (mean of the curves pertaining to the same group) into the plot. By default it is FALSE.
conf.int	Only for survival curves. Logical flag indicating whether to plot confidence intervals.
censor	Only for survival curves. Logical flag indicating whether to plot censors.
xlab	A title for the x axis.
ylab	A title for the y axis.
interactive	Logical flag indicating if an interactive plot with plotly is produced.
...	Other options.

Details

See help page of the function autoplot.survfit.

Value

A ggplot object, so you can use common features from ggplot2 package to manipulate the plot.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(survival)
library(clustcurv)
library(condSURV)
library(ggplot2)
library(ggfortify)

# Survival

data(veteran)
data(colonCS)

cl2 <- ksurvcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, k = 2, algorithm = "kmeans")

autoplot(cl2)
autoplot(cl2, groups_by_colour = FALSE)
autoplot(cl2, centers = TRUE)



# Regression

r2 <- kregcurves(y = barnacle5$DW, x = barnacle5$RC,
z = barnacle5$F, k = 2, algorithm = "kmeans")

autoplot(r2)
autoplot(r2, groups_by_colour = FALSE)
autoplot(r2, groups_by_colour = FALSE, interactive = TRUE)
autoplot(r2, centers = TRUE)


colonCSm <- data.frame(time = colonCS$Stime, status = colonCS$event,
                      nodes = colonCS$nodes)

table(colonCSm$nodes)
colonCSm$nodes[colonCSm$nodes == 0] <- NA
colonCSm <- na.omit(colonCSm)
colonCSm$nodes[colonCSm$nodes >= 10] <- 10
table(colonCSm$nodes) # ten levels

res <- survclustcurves(time = colonCSm$time, status = colonCSm$status,
       x = colonCSm$nodes, algorithm = "kmeans", nboot = 20)

autoplot(res)
autoplot(res, groups_by_colour = FALSE)
autoplot(res, centers = TRUE)

clustcurv documentation built on Jan. 14, 2021, 5:32 a.m.