fitdistcens: Fitting of univariate distributions to censored data

Description

Fits a univariate distribution to censored data by maximum likelihood.

Usage

fitdistcens(censdata, distr, start=NULL, fix.arg=NULL,  keepdata = TRUE, keepdata.nb=100, ...)
"print"(x, ...)
"plot"(x, ...)
"summary"(object, ...)
"logLik"(object, ...)
"vcov"(object, ...)
"coef"(object, ...)

Arguments

censdata

A dataframe of two columns respectively named left and right, describing each observed value as an interval. The left column contains either NA for left censored observations, the left bound of the interval for interval censored observations, or the observed value for non-censored observations. The right column contains either NA for right censored observations, the right bound of the interval for interval censored observations, or the observed value for non-censored observations.

distr

A character string "name" naming a distribution, for which the corresponding density function dname and the corresponding distribution function pname must be defined, or directly the density function.

start

A named list giving the initial values of parameters of the named distribution. This argument may be omitted for some distributions for which reasonable starting values are computed (see the 'details' section of mledist).

fix.arg

An optional named list giving the values of parameters of the named distribution that must be kept fixed rather than estimated by maximum likelihood.

an object of class "fitdistcens".

object

an object of class "fitdistcens".

keepdata

a logical. If TRUE, dataset is returned, otherwise only a sample subset is returned.

keepdata.nb

When keepdata=FALSE, the length of the subset returned.

...

further arguments to be passed to generic functions, to the function plotdistcens in order to control the type of ecdf-plot used for censored data, or to the function mledist in order to control the optimization method.

Value

fitdistcens returns an object of class "fitdistcens", a list with the following components:Generic functions:

Details

Maximum likelihood estimations of the distribution parameters are computed using the function mledist. By default direct optimization of the log-likelihood is performed using optim, with the "Nelder-Mead" method for distributions characterized by more than one parameter and the "BFGS" method for distributions characterized by only one parameter. The algorithm used in optim can be chosen or another optimization function can be specified using ... argument (see mledist for details). start may be omitted (i.e. NULL) for some classic distributions (see the 'details' section of mledist). Note that when errors are raised by optim, it's a good idea to start by adding traces during the optimization process by adding control=list(trace=1, REPORT=1) in ... argument.

The function is not able to fit a uniform distribution. With the parameter estimates, the function returns the log-likelihood and the standard errors of the estimates calculated from the Hessian at the solution found by optim or by the user-supplied function passed to mledist. By default (keepdata = TRUE), the object returned by fitdist contains the data vector given in input. When dealing with large datasets, we can remove the original dataset from the output by setting keepdata = FALSE. In such a case, only keepdata.nb points (at most) are kept by random subsampling keepdata.nb-4 points from the dataset and adding the component-wise minimum and maximum. If combined with bootdistcens, be aware that bootstrap is performed on the subset randomly selected in fitdistcens. Currently, the graphical comparisons of multiple fits is not available in this framework. Weighted version of the estimation process is available for method = "mle" by using weights=.... See the corresponding man page for details. It is not yet possible to take into account weighths in functions plotdistcens, plot.fitdistcens and cdfcompcens (developments planned in the future).

References

Venables WN and Ripley BD (2002), Modern applied statistics with S. Springer, New York, pp. 435-446.

Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34.

Examples

Run this code

# (1) Fit of a lognormal distribution to bacterial contamination data
#
data(smokedfish)
fitsf  <-  fitdistcens(smokedfish,"lnorm")
summary(fitsf)
# default plot using the Turnbull algorithm
plot(fitsf)
# plot using the Turnbull algorithm with confidence intervals for the 
# empirical distribution
plot(fitsf, Turnbull.confint = TRUE)
# basic plot using intervals and points (see ?plotdiscens for details)
plot(fitsf, Turnbull = FALSE)
# plot of the same fit using the Turnbull algorithm in logscale
cdfcompcens(fitsf,main="bacterial contamination fits",
    xlab="bacterial concentration (CFU/g)",ylab="F",
    addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, xlim = c(1e-2,1e2))
# zoom on large values of F
cdfcompcens(fitsf,main="bacterial contamination fits",
    xlab="bacterial concentration (CFU/g)",ylab="F",
    addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, 
    xlim = c(1e-2,1e2),ylim=c(0.4,1))

# (2) Fit of a normal distribution on acute toxicity values 
# of fluazinam (in decimal logarithm) for
# macroinvertebrates and zooplancton, using maximum likelihood estimation
# to estimate what is called a species sensitivity distribution 
# (SSD) in ecotoxicology
#

data(fluazinam)
log10EC50 <-log10(fluazinam)
fln <- fitdistcens(log10EC50,"norm")
fln
summary(fln)
plot(fln)

# (3) defining your own distribution functions, here for the Gumbel distribution
# for other distributions, see the CRAN task view dedicated to 
# probability distributions
#

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))
fg <- fitdistcens(log10EC50,"gumbel",start=list(a=1,b=1))
summary(fg)
plot(fg)

# (4) comparison of fits of various distributions
# 

fll <- fitdistcens(log10EC50,"logis")
summary(fll)

cdfcompcens(list(fln,fll,fg),legendtext=c("normal","logistic","gumbel"),
xlab = "log10(EC50)")

# (5) how to change the optimisation method?
#

fitdistcens(log10EC50,"logis",optim.method="Nelder-Mead")
fitdistcens(log10EC50,"logis",optim.method="BFGS") 
fitdistcens(log10EC50,"logis",optim.method="SANN") 

# (6) custom optimisation function - example with the genetic algorithm
#
## Not run: 
# 
#     #wrap genoud function rgenoud package
#     mygenoud  <-  function(fn, par, ...) 
#     {
#         require(rgenoud)
#         res  <-  genoud(fn, starting.values=par, ...)        
#         standardres  <-  c(res, convergence=0)
#             
#         return(standardres)
#     }
# 
#     # call fitdistcens with a 'custom' optimization function
#     fit.with.genoud <- fitdistcens(log10EC50,"logis", custom.optim=mygenoud, nvars=2,    
#         Domains=cbind(c(0,0), c(5, 5)), boundary.enforcement=1, 
#         print.level=1, hessian=TRUE)
# 
#     summary(fit.with.genoud)
# ## End(Not run)

# (7) estimation of the mean of a normal distribution 
# by maximum likelihood with the standard deviation fixed at 1 using the argument fix.arg
#
flnb <- fitdistcens(log10EC50, "norm", start = list(mean = 1),fix.arg = list(sd = 1))

# (8) Fit of a lognormal distribution on acute toxicity values of fluazinam for
# macroinvertebrates and zooplancton, using maximum likelihood estimation
# to estimate what is called a species sensitivity distribution 
# (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of 
# the fitted distribution (which is called the 5 percent hazardous concentration, HC5,
# in ecotoxicology) and estimation of other quantiles.

data(fluazinam)
log10EC50 <-log10(fluazinam)
fln <- fitdistcens(log10EC50,"norm")

quantile(fln, probs = 0.05)
quantile(fln, probs = c(0.05, 0.1, 0.2))

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