Description Usage Arguments Details Author(s) References See Also Examples
cdfcomp
plots the empirical cumulative distribution against fitted distribution functions,
denscomp
plots the histogram against fitted density functions,
qqcomp
plots theoretical quantiles against empirical ones,
ppcomp
plots theoretical probabilities against empirical ones.
Only cdfcomp
is able to plot fits of a discrete distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  cdfcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab,
datapch, datacol, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext,
xlegend = "bottomright", ylegend = NULL, horizontals = TRUE,
verticals = FALSE, do.points = TRUE, use.ppoints = TRUE, a.ppoints = 0.5,
lines01 = FALSE, discrete, add = FALSE, plotstyle = "graphics",
fitnbpts = 101, ...)
denscomp(ft, xlim, ylim, probability = TRUE, main, xlab, ylab, datacol, fitlty,
fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = "topright", ylegend = NULL,
demp = FALSE, dempcol = "black", plotstyle = "graphics",
discrete, fitnbpts = 101, fittype="l", ...)
qqcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab,
fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = "bottomright",
ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE,
line01col = "black", line01lty = 1, ynoise = TRUE, plotstyle = "graphics", ...)
ppcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab,
fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = "bottomright",
ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE,
line01col = "black", line01lty = 1, ynoise = TRUE, plotstyle = "graphics", ...)

ft 
One 
xlim 
The xlimits of the plot. 
ylim 
The ylimits of the plot. 
xlogscale 
If 
ylogscale 
If 
main 
A main title for the plot. See also 
xlab 
A label for the xaxis, defaults to a description of 
ylab 
A label for the yaxis, defaults to a description of 
datapch 
An integer specifying a symbol to be used in plotting data points.
See also 
datacol 
A specification of the color to be used in plotting data points.
See also 
fitcol 
A (vector of) color(s) to plot fitted distributions.
If there are fewer colors than fits they are recycled in the standard fashion.
See also 
fitlty 
A (vector of) line type(s) to plot fitted distributions/densities.
If there are fewer values than fits they are recycled in the standard fashion.
See also 
fitlwd 
A (vector of) line size(s) to plot fitted distributions/densities.
If there are fewer values than fits they are recycled in the standard fashion.
See also 
fitpch 
A (vector of) line type(s) to plot fitted quantiles/probabilities.
If there are fewer values than fits they are recycled in the standard fashion.
See also 
fittype 
The type of plot for fitted probabilities in the case of
discrete distributions: possible types are 
fitnbpts 
A numeric for the number of points to compute fitted probabilities
or cumulative probabilities. Default to 
addlegend 
If 
legendtext 
A character or expression vector of length ≥ 1 to appear
in the legend. See also 
xlegend, ylegend 
The x and y coordinates to be used to position the legend.
They can be specified by keyword.
If 
horizontals 
If 
do.points 
If 
verticals 
If 
use.ppoints 
If 
a.ppoints 
If 
lines01 
A logical to plot two horizontal lines at 
line01 
A logical to plot an horizontal line y=x for 
line01col, line01lty 
Color and line type for 
demp 
A logical to add the empirical density on the plot, using the

dempcol 
A color for the empirical density in case it is added on the plot ( 
ynoise 
A logical to add a small noise when plotting empirical
quantiles/probabilities for 
probability 
A logical to use the probability scale for 
discrete 
If 
add 
If 
plotstyle 

... 
Further graphical arguments passed to graphical functions used in 
cdfcomp
provides a plot of the empirical distribution and each fitted
distribution in cdf, by default using the Hazen's rule
for the empirical distribution, with probability points defined as
(1:n  0.5)/n
. If discrete
is TRUE
, probability points
are always defined as (1:n)/n
. For large dataset (n > 1e4
), no
point is drawn but the line for ecdf
is drawn instead.
Note that when horizontals, verticals and do.points
are FALSE
,
no empirical point is drawn, only the fitted cdf is shown.
denscomp
provides a density plot of each fitted distribution
with the histogram of the data for conyinuous data.
When discrete=TRUE
, distributions are considered as discrete,
no histogram is plotted but demp
is forced to TRUE
and fitted and empirical probabilities are plotted either with vertical lines
fittype="l"
, with single points fittype="p"
or
both lines and points fittype="o"
.
ppcomp
provides a plot of the probabilities of each fitted distribution
(xaxis) against the empirical probabilities (yaxis) by default defined as
(1:n  0.5)/n
(data are assumed continuous).
For large dataset (n > 1e4
), lines are drawn instead of pointss and customized with the fitpch
parameter.
qqcomp
provides a plot of the quantiles of each theoretical distribution (xaxis)
against the empirical quantiles of the data (yaxis), by default defining
probability points as (1:n  0.5)/n
for theoretical quantile calculation
(data are assumed continuous).
For large dataset (n > 1e4
), lines are drawn instead of points and customized with the fitpch
parameter.
By default a legend is added to these plots. Many graphical arguments are optional, dedicated to personalize the plots, and fixed to default values if omitted.
Christophe Dutang, MarieLaure DelignetteMuller and Aurelie Siberchicot.
DelignetteMuller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 134.
See plot
, legend
, ppoints
,
plot.stepfun
for classic plotting functions.
See CIcdfplot
and plotdist
for other plot functions
of fitdistrplus.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111  # (1) Plot various distributions fitted to serving size data
#
data(groundbeef)
serving < groundbeef$serving
fitW < fitdist(serving, "weibull")
fitln < fitdist(serving, "lnorm")
fitg < fitdist(serving, "gamma")
cdfcomp(list(fitW, fitln, fitg), horizontals = FALSE)
cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE)
cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, verticals = TRUE, datacol = "purple")
cdfcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
main = "ground beef fits", xlab = "serving sizes (g)",
ylab = "F", xlim = c(0, 250), xlegend = "center", lines01 = TRUE)
denscomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
main = "ground beef fits", xlab = "serving sizes (g)", xlim = c(0, 250), xlegend = "topright")
ppcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
main = "ground beef fits", xlegend = "bottomright", line01 = TRUE)
qqcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
main = "ground beef fits", xlegend = "bottomright", line01 = TRUE,
xlim = c(0, 300), ylim = c(0, 300), fitpch = 16)
# (2) Plot lognormal distributions fitted by
# maximum goodnessoffit estimation
# using various distances (data plotted in log scale)
#
data(endosulfan)
ATV < subset(endosulfan, group == "NonArthroInvert")$ATV
flnMGEKS < fitdist(ATV, "lnorm", method = "mge", gof = "KS")
flnMGEAD < fitdist(ATV, "lnorm", method = "mge", gof = "AD")
flnMGEADL < fitdist(ATV, "lnorm", method = "mge", gof = "ADL")
flnMGEAD2L < fitdist(ATV, "lnorm", method = "mge", gof = "AD2L")
cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
xlogscale = TRUE, main = "fits of a lognormal dist. using various GOF dist.",
legendtext = c("MGE KS", "MGE AD", "MGE ADL", "MGE AD2L"),
verticals = TRUE, xlim = c(10, 100000))
qqcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
main = "fits of a lognormal dist. using various GOF dist.",
legendtext = c("MGE KS", "MGE AD", "MGE ADL", "MGE AD2L"),
xlogscale = TRUE, ylogscale = TRUE)
ppcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
main = "fits of a lognormal dist. using various GOF dist.",
legendtext = c("MGE KS", "MGE AD", "MGE ADL", "MGE AD2L"))
# (3) Plot normal and logistic distributions fitted by
# maximum likelihood estimation
# using various plotting positions in cdf plots
#
data(endosulfan)
log10ATV <log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
fln < fitdist(log10ATV, "norm")
fll < fitdist(log10ATV, "logis")
# default plot using Hazen plotting position: (1:n  0.5)/n
cdfcomp(list(fln, fll), legendtext = c("normal", "logistic"), xlab = "log10ATV")
# plot using mean plotting position (named also Gumbel plotting position)
# (1:n)/(n + 1)
cdfcomp(list(fln, fll),legendtext = c("normal", "logistic"), xlab = "log10ATV",
use.ppoints = TRUE, a.ppoints = 0)
# plot using basic plotting position: (1:n)/n
cdfcomp(list(fln, fll),legendtext = c("normal", "logistic"), xlab = "log10ATV",
use.ppoints = FALSE)
# (4) Comparison of fits of two distributions fitted to discrete data
#
data(toxocara)
number < toxocara$number
fitp < fitdist(number, "pois")
fitnb < fitdist(number, "nbinom")
cdfcomp(list(fitp, fitnb), legendtext = c("Poisson", "negative binomial"))
denscomp(list(fitp, fitnb),demp = TRUE, legendtext = c("Poisson", "negative binomial"))
denscomp(list(fitp, fitnb),demp = TRUE, fittype = "l", dempcol = "black",
legendtext = c("Poisson", "negative binomial"))
denscomp(list(fitp, fitnb),demp = TRUE, fittype = "p", dempcol = "black",
legendtext = c("Poisson", "negative binomial"))
denscomp(list(fitp, fitnb),demp = TRUE, fittype = "o", dempcol = "black",
legendtext = c("Poisson", "negative binomial"))
# (5) Customizing of graphical output and use of ggplot2
#
data(groundbeef)
serving < groundbeef$serving
fitW < fitdist(serving, "weibull")
fitln < fitdist(serving, "lnorm")
fitg < fitdist(serving, "gamma")
if (requireNamespace ("ggplot2", quietly = TRUE)) {
denscomp(list(fitW, fitln, fitg), plotstyle = "ggplot")
cdfcomp(list(fitW, fitln, fitg), plotstyle = "ggplot")
qqcomp(list(fitW, fitln, fitg), plotstyle = "ggplot")
ppcomp(list(fitW, fitln, fitg), plotstyle = "ggplot")
}
# customizing graphical output with graphics
denscomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
main = "ground beef fits", xlab = "serving sizes (g)", xlim = c(0, 250),
xlegend = "topright", addlegend = FALSE)
# customizing graphical output with ggplot2
if (requireNamespace ("ggplot2", quietly = TRUE)) {
dcomp < denscomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
xlab = "serving sizes (g)", xlim = c(0, 250),
xlegend = "topright", plotstyle = "ggplot", breaks = 20, addlegend = FALSE)
dcomp + ggplot2::theme_minimal() + ggplot2::ggtitle("Ground beef fits")
}

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.