Plots an empirical distribution (non-censored data) with a theoretical one if specified.
plotdist(data, distr, para, histo = TRUE, breaks = "default",
demp = FALSE, discrete, …)A numeric vector.
A character string "name" naming a distribution for which the corresponding
density function dname, the corresponding distribution function pname and the corresponding
quantile function qname must be defined, or directly the density function.
This argument may be omitted only if para is omitted.
A named list giving the parameters of the named distribution. This argument may be
omitted only if distr is omitted.
A logical to plot the histogram using the
hist function.
If "default" the histogram is plotted with the function hist
with its default breaks definition. Else breaks is passed to the function hist.
This argument is not taken into account if discrete is TRUE.
A logical to plot the empirical density on the first plot
(alone or superimposed on the histogram depending of the value of the argument histo)
using the density function.
If TRUE, the distribution is considered as discrete.
If both distr and discrete are missing, discrete is set
to FALSE. If discrete is missing but not distr,
discrete is set to TRUE when distr belongs to
"binom", "nbinom","geom",
"hyper" or "pois".
further graphical arguments passed to graphical functions used in plotdist.
Empirical and, if specified, theoretical distributions are plotted
in density and in cdf. For the plot in density, the user can use the arguments
histo and demp to specify if he wants the histogram using the function
hist, the density plot using the function density, or both
(at least one of the two arguments must be put to "TRUE").
For continuous distributions, the function hist is used with its default
breaks definition if breaks is "default" or passing breaks as an argument if it differs
from "default". For continuous distribution and when a theoretical distribution is specified
by both arguments distname and para, Q-Q plot
(plot of the quantiles of the theoretical fitted distribution (x-axis) against the empirical quantiles of the data)
and P-P plot (i.e. for each value of the data set, plot of the cumulative density function of the fitted distribution
(x-axis) against the empirical cumulative density function (y-axis)) are also given (Cullen and Frey, 1999).
The function ppoints (with default parameter for argument a)
is used for the Q-Q plot, to generate the set of probabilities at
which to evaluate the inverse distribution.
NOTE THAT FROM VERSION 0.4-3, ppoints is also used for P-P plot and cdf plot for continuous data.
To personalize the four plots proposed for continuous data, for example to change the plotting position, we recommend
the use of functions cdfcomp, denscomp, qqcomp and ppcomp.
Cullen AC and Frey HC (1999), Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 81-155.
Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34.
# NOT RUN {
# (1) Plot of an empirical distribution with changing
# of default line types for CDF and colors
# and optionally adding a density line
#
set.seed(1234)
x1 <- rnorm(n=30)
plotdist(x1)
plotdist(x1,demp = TRUE)
plotdist(x1,histo = FALSE, demp = TRUE)
plotdist(x1, col="blue", type="b", pch=16)
plotdist(x1, type="s")
# (2) Plot of a discrete distribution against data
#
set.seed(1234)
x2 <- rpois(n=30, lambda = 2)
plotdist(x2, discrete=TRUE)
plotdist(x2, "pois", para=list(lambda = mean(x2)))
plotdist(x2, "pois", para=list(lambda = mean(x2)), lwd="2")
# (3) Plot of a continuous distribution against data
#
xn <- rnorm(n=100, mean=10, sd=5)
plotdist(xn, "norm", para=list(mean=mean(xn), sd=sd(xn)))
plotdist(xn, "norm", para=list(mean=mean(xn), sd=sd(xn)), pch=16)
plotdist(xn, "norm", para=list(mean=mean(xn), sd=sd(xn)), demp = TRUE)
plotdist(xn, "norm", para=list(mean=mean(xn), sd=sd(xn)),
histo = FALSE, demp = TRUE)
# (4) Plot of serving size data
#
data(groundbeef)
plotdist(groundbeef$serving, type="s")
# (5) Plot of numbers of parasites with a Poisson distribution
data(toxocara)
number <- toxocara$number
plotdist(number, discrete = TRUE)
plotdist(number,"pois",para=list(lambda=mean(number)))
# }
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