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: {r CRAN-instalation, eval = FALSE} 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: ```{r, eval = FALSE} # 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:
```{r, eval = FALSE}
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))