clustcurv (version 2.0.1)

autoplot.clustcurves: Visualization of clustcurves objects with ggplot2 graphics

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 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.

Value

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

Details

See help page of the function autoplot.survfit.

Examples

# NOT RUN {
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)


# }
# NOT RUN {
# 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)
# }