fitdistrplus: graphcompcens – R documentation

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graphcompcens

Graphical comparison of multiple fitted distributions for censored data

Description

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 ≥q 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.

Author(s)

Marie-Laure Delignette-Muller and Christophe Dutang.

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.

Examples

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

fitsfn <- fitdistcens(Clog10,"norm")
summary(fitsfn)

fitsfl <- fitdistcens(Clog10,"logis")
summary(fitsfl)

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))
summary(fitsfg)

# CDF plot
cdfcompcens(list(fitsfn,fitsfl,fitsfg))
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)
# 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")

# 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")
}

# PP plot
ppcompcens(list(fitsfn,fitsfl,fitsfg))
ppcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE)
par(mfrow = c(2,2))
ppcompcens(fitsfn)
ppcompcens(fitsfl)
ppcompcens(fitsfg)
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"))
}

# QQ plot
qqcompcens(list(fitsfn,fitsfl,fitsfg))
qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE)
par(mfrow = c(2,2))
qqcompcens(fitsfn)
qqcompcens(fitsfl)
qqcompcens(fitsfg)
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"))
}

fitdistrplus

Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

v1.1-3

GPL (>= 2)

Authors

Marie-Laure Delignette-Muller [aut], Christophe Dutang [aut], Regis Pouillot [ctb], Jean-Baptiste Denis [ctb], Aurelie Siberchicot [aut, cre]

Initial release