Fitting of univariate distributions to censored data

Fits a univariate distribution to censored data by maximum likelihood.

Usage

fitdistcens(censdata, distr, start=NULL, fix.arg=NULL, 
  keepdata = TRUE, keepdata.nb=100, calcvcov=TRUE, ...)

# S3 method for class 'fitdistcens'
print(x, ...)

# S3 method for class 'fitdistcens'
plot(x, ...)

# S3 method for class 'fitdistcens'
summary(object, ...)

# S3 method for class 'fitdistcens'
logLik(object, ...)

# S3 method for class 'fitdistcens'
AIC(object, ..., k = 2)

# S3 method for class 'fitdistcens'
BIC(object, ...)

# S3 method for class 'fitdistcens'
vcov(object, ...)

# S3 method for class 'fitdistcens'
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.
x: 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.
calcvcov: A logical indicating if (asymptotic) covariance matrix is required.
k: penalty per parameter to be passed to the AIC generic function (2 by default).
...: 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.

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).

Once the parameter(s) is(are) estimated, gofstat allows to compute goodness-of-fit statistics.

Value

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

estimate: the parameter estimates.
method: the character string coding for the fitting method : only "mle" for 'maximum likelihood estimation'.
sd: the estimated standard errors.
cor: the estimated correlation matrix, NA if numerically not computable or NULL if not available.
vcov: the estimated variance-covariance matrix, NULL if not available.
loglik: the log-likelihood.
aic: the Akaike information criterion.
bic: the the so-called BIC or SBC (Schwarz Bayesian criterion).
censdata: the censored data set.
distname: the name of the distribution.
fix.arg: the named list giving the values of parameters of the named distribution that must be kept fixed rather than estimated by maximum likelihood or NULL if there are no such parameters.
fix.arg.fun: the function used to set the value of fix.arg or NULL.
dots: the list of further arguments passed in ... to be used in bootdistcens to control the optimization method used in iterative calls to mledist or NULL if no such arguments.
convergence: an integer code for the convergence of optim/constrOptim defined as below or defined by the user in the user-supplied optimization function. 0 indicates successful convergence. 1 indicates that the iteration limit of optim has been reached. 10 indicates degeneracy of the Nealder-Mead simplex. 100 indicates that optim encountered an internal error.
discrete: always FALSE.
weights: the vector of weigths used in the estimation process or NULL.

Generic functions:

print: The print of a "fitdist" object shows few traces about the fitting method and the fitted distribution.
summary: The summary provides the parameter estimates of the fitted distribution, the log-likelihood, AIC and BIC statistics, the standard errors of the parameter estimates and the correlation matrix between parameter estimates.
plot: The plot of an object of class "fitdistcens" returned by fitdistcens uses the function plotdistcens.
logLik: Extracts the estimated log-likelihood from the "fitdistcens" object.
AIC: Extracts the AIC from the "fitdistcens" object.
BIC: Extracts the BIC from the "fitdistcens" object.
vcov: Extracts the estimated var-covariance matrix from the "fitdistcens" object (only available When method = "mle").
coef: Extracts the fitted coefficients from the "fitdistcens" object.

References

Venables WN and Ripley BD (2002), Modern applied statistics with S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 .

Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .

Author

Marie-Laure Delignette-Muller and Christophe Dutang.

Examples

# (1) Fit of a lognormal distribution to bacterial contamination data
#
data(smokedfish)
fitsf  <-  fitdistcens(smokedfish,"lnorm")
summary(fitsf)
#> Fitting of the distribution ' lnorm ' By maximum likelihood on censored data 
#> Parameters
#>          estimate Std. Error
#> meanlog -3.627606  0.4637122
#> sdlog    3.544570  0.4876610
#> Loglikelihood:  -90.65154   AIC:  185.3031   BIC:  190.5725 
#> Correlation matrix:
#>            meanlog      sdlog
#> meanlog  1.0000000 -0.4325873
#> sdlog   -0.4325873  1.0000000
#> 
# default plot using the Wang technique (see ?plotdiscens for details)
plot(fitsf)

# plot using the Turnbull algorithm (see ?plotdiscens for details)
# with confidence intervals for the empirical distribution
plot(fitsf, NPMLE = TRUE, NPMLE.method = "Turnbull", Turnbull.confint = TRUE)
#> Warning: Turnbull is now a deprecated option for NPMLE.method. You should use Turnbull.middlepoints
#>             of Turnbull.intervals. It was here fixed as Turnbull.middlepoints, equivalent to former Turnbull.
#> Warning: Q-Q plot and P-P plot are available only  
#>             with the arguments NPMLE.method at Wang (default value) or Turnbull.intervals.

# basic plot using intervals and points (see ?plotdiscens for details)
plot(fitsf, NPMLE = FALSE)
#> Warning: When NPMLE is FALSE the nonparametric maximum likelihood estimation 
#>             of the cumulative distribution function is not computed.
#>             Q-Q plot and P-P plot are available only with the arguments NPMLE.method at Wang 
#>             (default value) or Turnbull.intervals.

# 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
#> Fitting of the distribution ' norm ' on censored data by maximum likelihood 
#> Parameters:
#>      estimate
#> mean 2.161449
#> sd   1.167290
summary(fln)
#> Fitting of the distribution ' norm ' By maximum likelihood on censored data 
#> Parameters
#>      estimate Std. Error
#> mean 2.161449  0.3223366
#> sd   1.167290  0.2630390
#> Loglikelihood:  -20.41212   AIC:  44.82424   BIC:  46.10235 
#> Correlation matrix:
#>           mean        sd
#> mean 1.0000000 0.1350237
#> sd   0.1350237 1.0000000
#> 
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))
#> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg,     argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'.
summary(fg)
#> Error: object 'fg' not found
plot(fg)
#> Error: object 'fg' not found

# (4) comparison of fits of various distributions
# 

fll <- fitdistcens(log10EC50,"logis")
summary(fll)
#> Fitting of the distribution ' logis ' By maximum likelihood on censored data 
#> Parameters
#>           estimate Std. Error
#> location 2.1518291  0.3222830
#> scale    0.6910423  0.1745231
#> Loglikelihood:  -20.55391   AIC:  45.10781   BIC:  46.38593 
#> Correlation matrix:
#>            location      scale
#> location 1.00000000 0.05097494
#> scale    0.05097494 1.00000000
#> 

cdfcompcens(list(fln,fll,fg),legendtext=c("normal","logistic","gumbel"),
xlab = "log10(EC50)")
#> Error: object 'fg' not found

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

fitdistcens(log10EC50,"logis",optim.method="Nelder-Mead")
#> Fitting of the distribution ' logis ' on censored data by maximum likelihood 
#> Parameters:
#>           estimate
#> location 2.1518291
#> scale    0.6910423
fitdistcens(log10EC50,"logis",optim.method="BFGS") 
#> Fitting of the distribution ' logis ' on censored data by maximum likelihood 
#> Parameters:
#>           estimate
#> location 2.1519961
#> scale    0.6910665
fitdistcens(log10EC50,"logis",optim.method="SANN") 
#> Fitting of the distribution ' logis ' on censored data by maximum likelihood 
#> Parameters:
#>           estimate
#> location 2.1459483
#> scale    0.7809069

# (6) custom optimisation function - example with the genetic algorithm
#
# \donttest{

    #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)
#> 
#> 
#> Mon Sep 15 07:41:21 2025
#> Domains:
#>  0.000000e+00   <=  X1   <=    5.000000e+00 
#>  0.000000e+00   <=  X2   <=    5.000000e+00 
#> 
#> Data Type: Floating Point
#> Operators (code number, name, population) 
#> 	(1) Cloning........................... 	122
#> 	(2) Uniform Mutation.................. 	125
#> 	(3) Boundary Mutation................. 	125
#> 	(4) Non-Uniform Mutation.............. 	125
#> 	(5) Polytope Crossover................ 	125
#> 	(6) Simple Crossover.................. 	126
#> 	(7) Whole Non-Uniform Mutation........ 	125
#> 	(8) Heuristic Crossover............... 	126
#> 	(9) Local-Minimum Crossover........... 	0
#> 
#> HARD Maximum Number of Generations: 100
#> Maximum Nonchanging Generations: 10
#> Population size       : 1000
#> Convergence Tolerance: 1.000000e-03
#> 
#> Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
#> Checking Gradients before Stopping.
#> Not Using Out of Bounds Individuals But Allowing Trespassing.
#> 
#> Minimization Problem.
#> 
#> 
#> Generation#	    Solution Value
#> 
#>       0 	1.478955e+00
#>       1 	1.468136e+00
#> 
#> 'wait.generations' limit reached.
#> No significant improvement in 10 generations.
#> 
#> Solution Fitness Value: 1.468136e+00
#> 
#> Parameters at the Solution (parameter, gradient):
#> 
#>  X[ 1] :	2.151910e+00	G[ 1] :	-1.061675e-07
#>  X[ 2] :	6.909663e-01	G[ 2] :	2.013893e-07
#> 
#> Solution Found Generation 1
#> Number of Generations Run 12
#> 
#> Mon Sep 15 07:41:22 2025
#> Total run time : 0 hours 0 minutes and 1 seconds

    summary(fit.with.genoud)
#> Fitting of the distribution ' logis ' By maximum likelihood on censored data 
#> Parameters
#>           estimate Std. Error
#> location 2.1519103  0.3222566
#> scale    0.6909663  0.1744835
#> Loglikelihood:  -20.55391   AIC:  45.10781   BIC:  46.38593 
#> Correlation matrix:
#>            location      scale
#> location 1.00000000 0.05106507
#> scale    0.05106507 1.00000000
#> 
# }

# (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)
#> Estimated quantiles for each specified probability (censored data)
#>             p=0.05
#> estimate 0.2414275
quantile(fln, probs = c(0.05, 0.1, 0.2))
#> Estimated quantiles for each specified probability (censored data)
#>             p=0.05     p=0.1    p=0.2
#> estimate 0.2414275 0.6655064 1.179033

# (9) Fit of a lognormal distribution on 72-hour acute salinity tolerance (LC50 values)
# of riverine macro-invertebrates using maximum likelihood estimation

data(salinity)
log10LC50 <-log10(salinity)
fln <- fitdistcens(log10LC50,"norm")
plot(fln)