cran. See also theR-universe documentation.Package: GofCens 1.2.1
Klaus Langohr
GofCens: Goodness-of-Fit Methods for Complete and Right-Censored Data
Graphical tools and goodness-of-fit tests for complete and right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which utilize the empirical distribution function for complete data and are extended to handle right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. Graphical tools, such as probability and cumulative hazard plots, to help guide decisions about the most appropriate parametric model for the data.
Authors:
GofCens_1.2.1.tar.gz
GofCens_1.2.1.tar.gz(r-4.5-noble)GofCens_1.2.1.tar.gz(r-4.4-noble)
GofCens_1.2.1.tgz(r-4.4-emscripten)GofCens_1.2.1.tgz(r-4.3-emscripten)
GofCens.pdf |GofCens.html✨
GofCens/json (API)
| # Install 'GofCens' in R: |
| install.packages('GofCens', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/arnaugarciagrbio/gofcens/issues0 issues
Pkgdown site:https://arnaugarciagrbio.github.io
- nba - Survival times of former NBA players.
Last updated 5 months agofrom:3c1fb88fbc. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 11 2025 |
| R-4.5-linux | OK | Mar 11 2025 |
| R-4.4-linux | OK | Mar 11 2025 |
Exports:ADcenschisqcenscumhazPlotCvMcensgofcenskmPlotKScensprobPlot
Dependencies:actuarbootclicolorspaceexpintfansifarverfitdistrplusggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalessurvivaltibbleutf8vctrsviridisLitewithr
Citation
To cite package ‘GofCens’ in publications use:
Langohr K, Besalú M, Francisco M, Garcia A, Gómez G (2024). GofCens: Goodness-of-Fit Methods for Complete and Right-Censored Data. R package version 1.2.1, https://CRAN.R-project.org/package=GofCens.
Corresponding BibTeX entry:
@Manual{,
title = {GofCens: Goodness-of-Fit Methods for Complete and
Right-Censored Data},
author = {Klaus Langohr and Mireia Besalú and Matilde Francisco and
Arnau Garcia and Guadalupe Gómez},
year = {2024},
note = {R package version 1.2.1},
url = {https://CRAN.R-project.org/package=GofCens},
}
Readme and manuals
GofCens: Goodness-of-Fit Methods for Complete and Right-Censored Data
The GofCens package include the following graphical tools and goodness-of-fit tests for complete and right-censored data:
- Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data.
- Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data.
- A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data.
Installation
GofCens can be installed from CRAN:
install.packages("GofCens")
Brief Example
To conduct goodness-of-fit tests with right censored data we can use the KScens(), CvMcens(), ADcens() and chisqcens() functions. We illustrate this by means of the colon dataset:
# Kolmogorov-Smirnov
set.seed(123)
KScens(Surv(time, status) ~ 1, colon, distr = "norm")
# Cramér-von Mises
colonsamp <- colon[sample(nrow(colon), 300), ]
CvMcens(Surv(time, status) ~ 1, colonsamp, distr = "normal")
# Anderson-Darling
ADcens(Surv(time, status) ~ 1, colonsamp, distr = "normal")
# Generalized chi-squared-type test
chisqcens(Surv(time, status) ~ 1, colonsamp, M = 6, distr = "normal")
The graphical tools provide nice plots via the functions cumhazPlot(), kmPlot() and probPlot(). See several examples using the nba data set:
data(nba)
cumhazPlot(Surv(survtime, cens) ~ 1, nba, distr = c("expo", "normal", "gumbel"))
kmPlot(Surv(survtime, cens) ~ 1, nba, distr = c("normal", "weibull", "lognormal"),
prnt = FALSE)
probPlot(Surv(survtime, cens) ~ 1, nba, "lognorm", plots = c("PP", "QQ", "SP"),
ggp = TRUE, m = matrix(1:3, nr = 1))
