bootdistcens {fitdistrplus} | R Documentation |
Uses nonparametric bootstrap resampling in order to simulate uncertainty in the parameters of the distribution fitted to censored data.
bootdistcens(f, niter=999) ## S3 method for class 'bootdistcens': print(x,...) ## S3 method for class 'bootdistcens': plot(x,...) ## S3 method for class 'bootdistcens': summary(object,...)
f |
An object of class 'fitdistcens' result of the function fitdistcens . |
niter |
The number of samples drawn by bootstrap. |
x |
an object of class 'bootdistcens'. |
object |
an object of class 'bootdistcens'. |
... |
further arguments passed to other methods |
Samples are drawn by
non parametric bootstrap (resampling with replacement from the data set). On each bootstrap sample the function
mledistcens
is used to estimate bootstrapped values of parameters. When mledistcens
fails
to converge, NA
values are returned. Medians and 2.5 and 97.5 percentiles are computed by removing
NA
values. The number of iterations for which mledistcens
converges is printed in the summary with
the medians and the 95 percent confidence intervals of parameters (2.5 and 97.5 percentiles).
The plot of an object of class 'bootdistcens' consists in a scatterplot or a matrix of scatterplots
of the bootstrapped values of parameters.
It uses the function stripchart
when the fitted distribution
is characterized by only one parameter, and the function plot
in other cases.
In these last cases, it provides
a representation of the joint uncertainty distribution of the fitted parameters.
bootdistcens
returns an object of class 'bootdistcens', a list with 3 components,
estim |
a data frame containing the boostrapped values of parameters. |
converg |
a vector containing the codes for convergence obtained when using mledist on
each bootraped data set or NULL if momdist is used. |
CI |
bootstrap medians and 95 percent confidence intervals of parameters. |
Marie-Laure Delignette-Muller ml.delignette@vet-lyon.fr
Cullen AC and Frey HC (1999) Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 181-241.
fitdistcens
and mledistcens
.
d1<-data.frame( left=c(1.73,1.51,0.77,1.96,1.96,-1.4,-1.4,NA,-0.11,0.55, 0.41,2.56,NA,-0.53,0.63,-1.4,-1.4,-1.4,NA,0.13), right=c(1.73,1.51,0.77,1.96,1.96,0,-0.7,-1.4,-0.11,0.55, 0.41,2.56,-1.4,-0.53,0.63,0,-0.7,NA,-1.4,0.13)) f1<-fitdistcens(d1, "norm") b1<-bootdistcens(f1) b1 summary(b1) plot(b1)