Type:	Package
Title:	Determining Groups in Multiples Curves
URL:	https://github.com/noramvillanueva/clustcurv
BugReports:	https://github.com/noramvillanueva/clustcurv/issues
Version:	2.0.2
Date:	2024-10-09
Maintainer:	Nora M. Villanueva <nmvillanueva@uvigo.es>
Description:	A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Depends:	R (≥ 3.5.0)
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Imports:	doParallel, doRNG, foreach, ggfortify, ggplot2, Gmedian, grDevices, npregfast, RColorBrewer, survival
Suggests:	covr, knitr, plotly, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-10-23 10:39:34 UTC; sestelo
Author:	Nora M. Villanueva [aut, cre], Marta Sestelo [aut]
Repository:	CRAN
Date/Publication:	2024-10-25 08:20:07 UTC

clustcurv: Determining Groups in Multiple Curves.

Description

This package provides a method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework.

Details

clustcurv is designed along lines similar to those of other R packages. This software helps the user determine groups in multiple curves (survival and regression curves). In addition, it enables both numerical and graphical outputs to be displayed (by means of ggplot2). The package provides the kclustcurv() function that groups the curves given a number k and the autoclustcurv() function that selects the optimal number of groups automatically through a boostrap-based test. The autoplot() function let the user draws the resulted estimated curves coloured by groups.

For a listing of all routines in the clustcurv package type: library(help="clustcurv").

Author(s)

Nora M. Villanueva and Marta Sestelo

References

Villanueva, N. M., Sestelo, M., and Meira-Machado, J. (2019). A method for determining groups in multiple survival curves. Statistics in Medicine, 8(5):866-877

See Also

Useful links:

• https://github.com/noramvillanueva/clustcurv

• Report bugs at https://github.com/noramvillanueva/clustcurv/issues

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

Arguments

Details

See help page of the function ggfortify::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(ggplot2)
library(ggfortify)

# Survival


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)

Barnacle data

Description

This barnacle data set gives the measurements of the variables dry weight (in g.) and rostro-carinal length (in mm) for 5000 barnacles collected along the intertidal zone from five sites of the Atlantic coast of Galicia (Spain).

Usage

barnacle5

Format

barnacle5 is a data frame with 5000 cases (rows) and 3 variables (columns).

Note that barnacle data set from the npregfast package gives the same three variables (columns) but for two sites, thus 2000 cases (rows).

DW

Dry weight (in g.)

RC

Rostro-carinal length (in mm).

F

Factor indicating the sites of harvest: laxe, lens, barca, laxe, and lens.

Author(s)

Marta Sestelo

References

Sestelo, M. and Roca-Pardinas, J. (2011). A new approach to estimation of length-weight relationship of Pollicipes pollicipes (Gmelin, 1789) on the Atlantic coast of Galicia (Northwest Spain): some aspects of its biology and management. Journal of Shellfish Research, 30(3), 939–948.

Sestelo, M., Villanueva, N.M., Meira-Machado, L., Roca-Pardinas, J. (2017). npregfast: An R Package for Nonparametric Estimation and Inference in Life Sciences. Journal of Statistical Software, 82(12), 1-27.

Examples

data(barnacle5)
head(barnacle5)

k-groups of multiple regression curves

Description

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

Usage

kregcurves(y, x, z, k, kbin = 50, h = -1, algorithm = "kmeans", seed = NULL)

Arguments

Value

A list containing the following items:

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(clustcurv)

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

data.frame(level = r2$level, cluster = r2$cluster)

k-groups of multiple survival curves

Description

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

Usage

ksurvcurves(
  time,
  status = NULL,
  x,
  k,
  kbin = 50,
  algorithm = "kmeans",
  seed = NULL
)

Arguments

Value

A list containing the following items:

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

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

# Survival: 2 groups k-means
s2 <- ksurvcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, k = 2, algorithm = "kmeans")

data.frame(level = s2$level, cluster = s2$cluster)


# Survival: 2 groups k-medians
s22 <- ksurvcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, k = 2, algorithm = "kmedians")

data.frame(level = s22$level, cluster = s22$cluster)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot

Clustering multiple regression curves

Description

Function for grouping regression curves based on the k-means or k-medians algorithm. It returns the number of groups and the assignment.

Usage

regclustcurves(
  y,
  x,
  z,
  kvector = NULL,
  kbin = 50,
  h = -1,
  nboot = 100,
  algorithm = "kmeans",
  alpha = 0.05,
  cluster = FALSE,
  ncores = NULL,
  seed = NULL,
  multiple = FALSE,
  multiple.method = "holm"
)

Arguments

Details

The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ('holm'), Hochberg (1988) ('hochberg'), Hommel (1988) ('hommel'), Benjamini & Hochberg (1995) ('BH' or its alias 'fdr'), and Benjamini & Yekutieli (2001) ('BY'), respectively. A pass-through option ('none') is also included.

Value

A list containing the following items:

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(clustcurv)

# Regression framework
res <- regclustcurves(y = barnacle5$DW, x = barnacle5$RC, z = barnacle5$F,
algorithm = 'kmeans', nboot = 2, cluster = TRUE, ncores = 2)

Summarizing fits of kclustcurves class produced by survclustcurves and regclustcurves

Description

Takes a clustcurves object and produces various useful summaries from it.

Usage

## S3 method for class 'clustcurves'
summary(object, ...)

Arguments

Details

print.clustcurves tries to be smart about summary.clustcurves.

Value

summary.clustcurves computes and returns a list of summary information for a clustcurves object.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

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

# Survival framework
ressurv <- survclustcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, algorithm = 'kmeans', nboot = 2)

summary(ressurv)


# Regression framework
resreg <- regclustcurves(y = barnacle5$DW, x = barnacle5$RC, z = barnacle5$F,
algorithm = 'kmeans', nboot = 2)

summary(resreg)

Summarizing fits of kcurves class produced by ksurvcurves and kregcurves

Description

Takes a kcurves object and produces various useful summaries from it.

Usage

## S3 method for class 'kcurves'
summary(object, ...)

Arguments

Details

print.kcurves tries to be smart about summary.kcurves.

Value

summary.kcurves computes and returns a list of summary information for a kcurves object.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

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

# Survival: 2 groups k-means
s2 <- ksurvcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, k = 2, algorithm = "kmeans")

summary(s2)


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

summary(r2)

Clustering multiple survival curves

Description

Function for grouping survival curves based on the k-means or k-medians algorithm. It returns the number of groups and the assignment.

Usage

survclustcurves(
  time,
  status = NULL,
  x,
  kvector = NULL,
  kbin = 50,
  nboot = 100,
  algorithm = "kmeans",
  alpha = 0.05,
  cluster = FALSE,
  ncores = NULL,
  seed = NULL,
  multiple = FALSE,
  multiple.method = "holm"
)

Arguments

Details

The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ('holm'), Hochberg (1988) ('hochberg'), Hommel (1988) ('hommel'), Benjamini & Hochberg (1995) ('BH' or its alias 'fdr'), and Benjamini & Yekutieli (2001) ('BY'), respectively. A pass-through option ('none') is also included.

Value

A list containing the following items:

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

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

# Survival framework
res <- survclustcurves(time = veteran$time, status = veteran$status,
x = veteran$celltype, algorithm = 'kmeans', nboot = 2)