cumhazPlot: Cumulative hazard plots to check the goodness of fit of...

In GofCens: Goodness-of-Fit Methods for Right-Censored Data

Description

cumhazPlot uses the cumulative hazard plot to check if a certain distribution is an appropiate choice for the data.

Usage

cumhazPlot(times, cens = rep(1, length(times)), distr = "all6", colour = 1, 
           betaLimits = c(0, 1), igumb = c(10, 10), ggplo = FALSE, m = NULL, 
           prnt = TRUE, decdig = 7, ...)

Arguments

times	Numeric vector of times until the event of interest.
cens	Status indicator (1, exact time; 0, right-censored time). If not provided, all times are assumed to be exact.
distr	A string specifying the names of the distributions to be studied. The possible distributions are the exponential ("exponential"), the Weibull ("weibull"), the Gumbel ("gumbel"), the normal ("normal"), the lognormal ("lognormal"), the logistic ("logistic"), the loglogistic ("loglogistic"), and the beta ("beta") distribution. By default, distr is set to "all6", which means that the cumulative hazard plots are drawn for the Weibull, loglogistic, lognormal, Gumbel, logistic, and normal distributions.
colour	Colour of the points. Default colour: black.
betaLimits	Two-components vector with the lower and upper bounds of the Beta distribution. This argument is only required, if the beta distribution is considered.
igumb	Two-components vector with the initial values for the estimation of the Gumbel distribution parameters.
ggplo	Logical to use or not the ggplot2 package to draw the plots. Default is FALSE.
m	Optional layout for the plots to be displayed.
prnt	Logical to indicate if the maximum likelihood estimates of the parameters of all distributions considered should be printed. Default is TRUE.
decdig	Number of significant (see signif) digits to print when printing the parameter estimates. It is a suggestion only.
...	Optional arguments for function par, if ggplo = FALSE.

Details

The cumulative hazard plot is based on transforming the cumulative hazard function Λ in such a way that it becomes linear in t or \log(t). This transformation is specific for each distribution. The function uses the data to compute the Nelson-Aalen estimator of the cumulative hazard function, \widehat{Λ}, and the maximum likelihood estimators of the parameters of the theoretical distribution under study. If the distribution fits the data, the plot is expected to be a straight line.

The parameter estimation is acomplished with the fitdistcens function of the fitdistrplus package.

Value

Parameter estimates

A list with the maximum likelihood estimates of the parameters of all distributions considered.

Author(s)

K. Langohr, M. Besalú, G. Gómez.

Examples

# Complete data and default distributions
set.seed(123)
x <- rlogis(1000, 50, 5)
cumhazPlot(x, lwd = 2)

# Censored data comparing three distributions
data(nba)
cumhazPlot(nba$survtime, nba$cens, distr = c("expo", "normal", "gumbel"))

GofCens documentation built on Jan. 6, 2022, 1:06 a.m.