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Graphical comparison of multiple fitted distributions for censored data

graphcompcens.Rd

cdfcompcens plots the empirical cumulative distribution against fitted distribution functions, qqcompcens plots theoretical quantiles against empirical ones, ppcompcens plots theoretical probabilities against empirical ones.

Usage

cdfcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, 
    datacol, fillrect, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, 
    xlegend = "bottomright", ylegend = NULL, lines01 = FALSE, 
    Turnbull.confint = FALSE, 
    NPMLE.method = "Wang", 
    add = FALSE, plotstyle = "graphics", ...)
qqcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab,
      fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = "bottomright", 
      ylegend = NULL, line01 = TRUE, line01col = "black", line01lty = 1, 
      ynoise = TRUE, NPMLE.method = "Wang", plotstyle = "graphics", ...)
ppcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, 
      fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = "bottomright", 
      ylegend = NULL, line01 = TRUE, line01col = "black", line01lty = 1, 
      ynoise = TRUE, NPMLE.method = "Wang", plotstyle = "graphics", ...)

Arguments

ft

One "fitdistcens" object or a list of objects of class "fitdistcens".

xlim

The \(x\)-limits of the plot.

ylim

The \(y\)-limits of the plot.

xlogscale

If TRUE, uses a logarithmic scale for the \(x\)-axis.

ylogscale

If TRUE, uses a logarithmic scale for the \(y\)-axis.

main

A main title for the plot, see also title.

xlab

A label for the \(x\)-axis, defaults to a description of x.

ylab

A label for the \(y\)-axis, defaults to a description of y.

datacol

A specification of the color to be used in plotting data points.

fillrect

A specification of the color to be used for filling rectanges of non uniqueness of the empirical cumulative distribution (only used if NPMLE.method is equal to "Wang" in cdfcompcens). Fix it to NA if you do not want to fill the rectangles.

fitcol

A (vector of) color(s) to plot fitted distributions. If there are fewer colors than fits they are recycled in the standard fashion.

fitlty

A (vector of) line type(s) to plot fitted distributions. If there are fewer values than fits they are recycled in the standard fashion. See also par.

fitlwd

A (vector of) line size(s) to plot fitted distributions. If there are fewer values than fits they are recycled in the standard fashion. See also par.

addlegend

If TRUE, a legend is added to the plot.

legendtext

A character or expression vector of length \(\geq 1\) to appear in the legend, see also legend.

xlegend, ylegend

The \(x\) and \(y\) coordinates to be used to position the legend. They can be specified by keyword. If plotstyle = "graphics", see xy.coords and legend. If plotstyle = "ggplot", the xlegend keyword must be one of top, bottom, left, or right. See also guide_legend in ggplot2

lines01

A logical to plot two horizontal lines at h=0 and h=1 for cdfcompcens.

Turnbull.confint

if TRUE confidence intervals will be added to the Turnbull plot. In that case NPMLE.method is forced to "Turnbull"

NPMLE.method

Three NPMLE techniques are provided, "Wang", the default one, rewritten from the package npsurv using function constrOptim from the package stats for optimisation, "Turnbull.middlepoints", an older one which is implemented in the package survival and "Turnbull.intervals" that uses the same Turnbull algorithm from the package survival but associates an interval to each equivalence class instead of the middlepoint of this interval (see details). Only "Wang" and "Turnbull.intervals" enable the derivation of a Q-Q plot and a P-P plot.

add

If TRUE, adds to an already existing plot. If FALSE, starts a new plot. This parameter is not available when plotstyle = "ggplot".

line01

A logical to plot an horizontal line \(y=x\) for qqcompcens and ppcompcens.

line01col, line01lty

Color and line type for line01. See also par.

ynoise

A logical to add a small noise when plotting empirical quantiles/probabilities for qqcompcens and ppcompcens. ynoise is only used when various fits are plotted with the "graphics" plotstyle. Facets are used instead with the "ggplot" plotstyle.

plotstyle

"graphics" or "ggplot". If "graphics", the display is built with graphics functions. If "ggplot", a graphic object output is created with ggplot2 functions (the ggplot2 package must be installed). In "cdfcompcens", "ggplot" graphics are only available with "Wang" NPMLE technique.

...

Further graphical arguments passed to graphical functions used in cdfcompcens, ppcompcens and qqcompcens.

Details

See details of plotdistcens for a detailed description of provided goddness-of-fit plots.

See also

plotdistcens, survfit.formula, legend and par.

References

Turnbull BW (1974), Nonparametric estimation of a survivorship function with doubly censored data. Journal of American Statistical Association, 69, 169-173.

Wang Y (2008), Dimension-reduced nonparametric maximum likelihood computation for interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402.

Wang Y and Taylor SM (2013), Efficient computation of nonparametric survival functions via a hierarchical mixture formulation. Statistics and Computing, 23, 713-725.

Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34.

Author

Marie-Laure Delignette-Muller and Christophe Dutang.

Examples

# (1) Plot various distributions fitted to bacterial contamination data
#
data(smokedfish)
Clog10 <- log10(smokedfish)

fitsfn <- fitdistcens(Clog10,"norm")
summary(fitsfn)
#> Fitting of the distribution ' norm ' By maximum likelihood on censored data 
#> Parameters
#>       estimate Std. Error
#> mean -1.575392   2.043857
#> sd    1.539446   2.149561
#> Loglikelihood:  -87.10945   AIC:  178.2189   BIC:  183.4884 
#> Correlation matrix:
#>            mean         sd
#> mean  1.0000000 -0.4325228
#> sd   -0.4325228  1.0000000
#> 

fitsfl <- fitdistcens(Clog10,"logis")
summary(fitsfl)
#> Fitting of the distribution ' logis ' By maximum likelihood on censored data 
#> Parameters
#>            estimate Std. Error
#> location -1.5394230   1.706269
#> scale     0.8121862   1.352708
#> Loglikelihood:  -86.45499   AIC:  176.91   BIC:  182.1794 
#> Correlation matrix:
#>            location      scale
#> location  1.0000000 -0.3189915
#> scale    -0.3189915  1.0000000
#> 

dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b))
pgumbel <- function(q,a,b) exp(-exp((a-q)/b))
qgumbel <- function(p,a,b) a-b*log(-log(p))
fitsfg<-fitdistcens(Clog10,"gumbel",start=list(a=-3,b=3))
#> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg,     argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'.
summary(fitsfg)
#> Error: object 'fitsfg' not found

# CDF plot
cdfcompcens(list(fitsfn,fitsfl,fitsfg))
#> Error: object 'fitsfg' not found
cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol="orange",fillrect = NA, 
  legendtext=c("normal","logistic","Gumbel"),
  main="bacterial contamination fits",
  xlab="bacterial concentration (CFU/g)",ylab="F",
  xlegend = "bottom",lines01 = TRUE)
#> Error: object 'fitsfg' not found
# alternative Turnbull plot for the empirical cumulative distribution
# (default plot of the previous versions of the package)
cdfcompcens(list(fitsfn,fitsfl,fitsfg), NPMLE.method = "Turnbull.middlepoints")
#> Error: object 'fitsfg' not found

# customizing graphical output with ggplot2
if (requireNamespace ("ggplot2", quietly = TRUE)) {
  cdfcompcens <- cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol="orange",fillrect = NA, 
    legendtext=c("normal","logistic","Gumbel"),
    xlab="bacterial concentration (CFU/g)",ylab="F",
    xlegend = "bottom",lines01 = TRUE, plotstyle = "ggplot")
  cdfcompcens + ggplot2::theme_minimal() + ggplot2::ggtitle("Bacterial contamination fits")
}
#> Error: object 'fitsfg' not found

# PP plot
ppcompcens(list(fitsfn,fitsfl,fitsfg))
#> Error: object 'fitsfg' not found
ppcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE)
#> Error: object 'fitsfg' not found
par(mfrow = c(2,2))
ppcompcens(fitsfn)
ppcompcens(fitsfl)
ppcompcens(fitsfg)
#> Error: object 'fitsfg' not found
par(mfrow = c(1,1))

if (requireNamespace ("ggplot2", quietly = TRUE)) {
  ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = "ggplot")
  ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = "ggplot", 
    fillrect = c("lightpink", "lightblue", "lightgreen"), 
    fitcol = c("red", "blue", "green"))
}
#> Error: object 'fitsfg' not found

# QQ plot
qqcompcens(list(fitsfn,fitsfl,fitsfg))
#> Error: object 'fitsfg' not found
qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE)
#> Error: object 'fitsfg' not found
par(mfrow = c(2,2))
qqcompcens(fitsfn)
qqcompcens(fitsfl)
qqcompcens(fitsfg)
#> Error: object 'fitsfg' not found
par(mfrow = c(1,1))


if (requireNamespace ("ggplot2", quietly = TRUE)) {
  qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = "ggplot")
  qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = "ggplot", 
    fillrect = c("lightpink", "lightblue", "lightgreen"), 
    fitcol = c("red", "blue", "green"))
}
#> Error: object 'fitsfg' not found