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

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

Cumulative hazard plots to check the goodness of fit of parametric models

Description

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

Usage

## Default S3 method:
cumhazPlot(times, cens = rep(1, length(times)), distr = "all6", colour = 1,
           betaLimits = c(0, 1), igumb = c(10, 10), ggp = FALSE, m = NULL,
           prnt = TRUE, degs = 3, print.AIC = TRUE, print.BIC = TRUE,...)
## S3 method for class 'formula'
cumhazPlot(formula, data, ...)

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.
ggp	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.
degs	Integer indicating the number of decimal places of the numeric results of the output.
formula	A formula with a numeric vector as response (which assumes no censoring) or Surv object.
data	Data frame for variables in formula.
print.AIC	Logical to indicate if the AIC of the model should be printed. Default is TRUE
print.BIC	Logical to indicate if the BIC of the model should be printed. Default is TRUE
...	Optional arguments for function par, if ggplo = FALSE.

Details

The cumulative hazard plot is based on transforming the cumulative hazard function \Lambda 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{\Lambda}, 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

If prnt = TRUE, the following output is returned:

Distribution	Distribution under study.
Estimates	A list with the maximum likelihood estimates of the parameters of all distributions considered.
StdErrors	Vector containing the estimated standard errors.
aic	The Akaike information criterion.
bic	The so-called BIC or SBC (Schwarz Bayesian criterion).

In addition, a list with the same contents is returned invisibly.

Author(s)

K. Langohr, M. Besalú, M. Francisco, A. Garcia, 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(Surv(survtime, cens) ~ 1, nba, distr = c("expo", "normal", "gumbel"))

GofCens documentation built on Oct. 30, 2024, 9:13 a.m.