descdist {fitdistrplus} | R Documentation |
Computes descriptive parameters of an empirical distribution for non-censored data and provides a skewness-kurtosis plot.
descdist(data,discrete=FALSE,boot=NULL,graph=TRUE)
data |
A numeric vector. |
discrete |
If TRUE , the distribution is considered as discrete. |
boot |
If not NULL , boot values of skewness and kurtosis are plotted from bootstrap
samples of data . boot must be fixed in this case to an integer above 10. |
graph |
If FALSE , the graph is not plotted. |
Minimum, maximum, median, mean, sample sd, sample skewness and sample kurtosis values are printed.
A skewness-kurtosis plot such as the one proposed by Cullen and Frey (1999) is given for the
empirical distribution. On this plot, values for common distributions are also displayed as a tools
to help the choice of distributions to fit to data. In order to take into account the uncertainty
of the estimated values of kurtosis and skewness, the data set may be boostraped by
fixing the argument boot
to an integer above 10. boot
values of skewness and kurtosis
corresponding to the boot
bootstrap samples are then computed and reported in blue color on the
skewness-kurtosis plot.
If discrete
is TRUE
,
these common distributions are the Poisson, negative binomial and normal distributions.
If discrete
is FALSE
, these are uniform, normal, lognormal, beta and gamma distributions.
The Weibull distribution is not represented on the graph but it is indicated on the legend that
shapes close to lognormal and gamma distributions may be obtained with this distribution.
descdist
returns a list with 7 components,
min |
the minimum value |
max |
the maximum value |
median |
the median value |
mean |
the mean value |
sd |
the standard deviation sample value |
skewness |
the skewness sample value |
kurtosis |
the kurtosis sample value |
Marie-Laure Delignette-Muller ml.delignette@vet-lyon.fr
Cullen AC and Frey HC (1999) Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 81-159. Evans M, Hastings N and Peacock B (2000) Statistical distributions. John Wiley and Sons Inc.
x1<-c(6.4,13.3,4.1,1.3,14.1,10.6,9.9,9.6,15.3,22.1,13.4, 13.2,8.4,6.3,8.9,5.2,10.9,14.4) descdist(x1) descdist(x1,boot=1000) x2<-c(rep(4,1),rep(2,3),rep(1,7),rep(0,12)) descdist(x2,discrete=TRUE) x3<-rbeta(100,shape1=0.05,shape2=1) descdist(x3,boot=1000)