plot.elife_par: Goodness-of-fit plots for parametric models

In longevity: Statistical Methods for the Analysis of Excess Lifetimes

Goodness-of-fit plots for parametric models

Description

Because of censoring and truncation, the plotting positions must be adjusted accordingly. For right-censored data, the methodology is described in Waller & Turnbull (1992). Only non-censored observations are displayed, which can create distortion.

Usage

## S3 method for class 'elife_par'
plot(
  x,
  plot.type = c("base", "ggplot"),
  which.plot = c("pp", "qq"),
  confint = c("none", "pointwise", "simultaneous"),
  plot = TRUE,
  ...
)

Arguments

x	a parametric model of class elife_par
plot.type	string, one of base for base R or ggplot
which.plot	vector of string indicating the plots, among pp for probability-probability plot, qq for quantile-quantile plot, erp for empirically rescaled plot (only for censored data), exp for standard exponential quantile-quantile plot or tmd for Tukey's mean difference plot, which is a variant of the Q-Q plot in which we map the pair (x,y) is mapped to ((x+y)/2,y-x) are detrended
confint	logical; if TRUE, creates uncertainty diagnostic via a parametric bootstrap
plot	logical; if TRUE, creates a plot when plot.type="ggplot". Useful for returning ggplot objects without printing the graphs
...	additional arguments, currently ignored by the function.

Details

For truncated data, we first estimate the distribution function nonparametrically, F_n. The uniform plotting positions of the data

v_i = [F_n(y_i) - F_n(a_i)]/[F_n(b_i) - F_n(a_i)].

For probability-probability plots, the empirical quantiles are transformed using the same transformation, with F_n replaced by the postulated or estimated distribution function F_0. For quantile-quantile plots, the plotting positions v_i are mapped back to the data scale viz.

F_0^{-1}\{F_0(a_i) + v_i[F_0(b_i) - F_0(a_i)]\}

When data are truncated and observations are mapped back to the untruncated scale (with, e.g., exp), the plotting positions need not be in the same order as the order statistics of the data.

Value

The function produces graphical goodness-of-fit plots using base R or ggplot objects (returned as an invisible list).

Examples

set.seed(1234)
samp <- samp_elife(
 n = 200,
 scale = 2,
 shape = 0.3,
 family = "gomp",
 lower = 0, upper = runif(200, 0, 10),
 type2 = "ltrc")
fitted <- fit_elife(
 time = samp$dat,
 thresh = 0,
 event = ifelse(samp$rcens, 0L, 1L),
 type = "right",
 family = "exp",
 export = TRUE)
plot(fitted, plot.type = "ggplot")
# Left- and right-truncated data
n <- 40L
samp <- samp_elife(
 n = n,
 scale = 2,
 shape = 0.3,
 family = "gp",
 lower = ltrunc <- runif(n),
 upper = rtrunc <- ltrunc + runif(n, 0, 15),
 type2 = "ltrt")
fitted <- fit_elife(
 time = samp,
 thresh = 0,
 ltrunc = ltrunc,
 rtrunc = rtrunc,
 family = "gp",
 export = TRUE)
plot(fitted,  which.plot = "tmd")

longevity documentation built on Nov. 12, 2023, 5:07 p.m.