Fit of univariate distributions to non-censored data

Fit of univariate distributions to non-censored data by maximum likelihood (mle), moment matching (mme), quantile matching (qme) or maximizing goodness-of-fit estimation (mge). The latter is also known as minimizing distance estimation. Generic methods are print, plot, summary, quantile, logLik, AIC, BIC, vcov and coef.

Usage

fitdist(data, distr, method = c("mle", "mme", "qme", "mge", "mse"), 
    start=NULL, fix.arg=NULL, discrete, keepdata = TRUE, keepdata.nb=100, 
    calcvcov=TRUE, ...)
    
# S3 method for class 'fitdist'
print(x, ...)

# S3 method for class 'fitdist'
plot(x, breaks="default", ...)

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

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

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

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

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

# S3 method for class 'fitdist'
coef(object, ...)

Arguments

data: A numeric vector.
distr: 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.
method: A character string coding for the fitting method: "mle" for 'maximum likelihood estimation', "mme" for 'moment matching estimation', "qme" for 'quantile matching estimation', "mge" for 'maximum goodness-of-fit estimation' and "mse" for 'maximum spacing estimation'.
start: A named list giving the initial values of parameters of the named distribution or a function of data computing initial values and returning a named list. This argument may be omitted (default) for some distributions for which reasonable starting values are computed (see the 'details' section of mledist). It may not be into account for closed-form formulas.
fix.arg: An optional named list giving the values of fixed parameters of the named distribution or a function of data computing (fixed) parameter values and returning a named list. Parameters with fixed value are thus NOT estimated by this maximum likelihood procedure. The use of this argument is not possible if method="mme" and a closed-form formula is used.
keepdata: a logical. If TRUE, dataset is returned, otherwise only a sample subset is returned.
keepdata.nb: When keepdata=FALSE, the length (>1) of the subset returned.
calcvcov: A logical indicating if (asymptotic) covariance matrix is required.
discrete: If TRUE, the distribution is considered as discrete. If discrete is missing, discrete is automaticaly set to TRUE when distr belongs to "binom", "nbinom", "geom", "hyper" or "pois" and to FALSE in the other cases. It is thus recommended to enter this argument when using another discrete distribution. This argument will not directly affect the results of the fit but will be passed to functions gofstat, plotdist and cdfcomp.
x: An object of class "fitdist".
object: An object of class "fitdist".
breaks: 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 with discrete distributions: "binom", "nbinom", "geom", "hyper" and "pois".
k: penalty per parameter to be passed to the AIC generic function (2 by default).
...: Further arguments to be passed to generic functions, or to one of the functions "mledist", "mmedist", "qmedist" or "mgedist" depending of the chosen method. See mledist, mmedist, qmedist, mgedist for details on parameter estimation.

Details

It is assumed that the distr argument specifies the distribution by the probability density function, the cumulative distribution function and the quantile function (d, p, q).

The four possible fitting methods are described below:

When method="mle"

Maximum likelihood estimation consists in maximizing the log-likelihood. A numerical optimization is carried out in mledist via optim to find the best values (see mledist for details).

When method="mme"

Moment matching estimation consists in equalizing theoretical and empirical moments. Estimated values of the distribution parameters are computed by a closed-form formula for the following distributions : "norm", "lnorm", "pois", "exp", "gamma", "nbinom", "geom", "beta", "unif" and "logis". Otherwise the theoretical and the empirical moments are matched numerically, by minimization of the sum of squared differences between observed and theoretical moments. In this last case, further arguments are needed in the call to fitdist: order and memp (see mmedist for details).

Since Version 1.2-0, mmedist automatically computes the asymptotic covariance matrix, hence the theoretical moments mdist should be defined up to an order which equals to twice the maximal order given order.

When method = "qme"

Quantile matching estimation consists in equalizing theoretical and empirical quantile. A numerical optimization is carried out in qmedist via optim to minimize of the sum of squared differences between observed and theoretical quantiles. The use of this method requires an additional argument probs, defined as the numeric vector of the probabilities for which the quantile(s) is(are) to be matched (see qmedist for details).

When method = "mge"

Maximum goodness-of-fit estimation consists in maximizing a goodness-of-fit statistics. A numerical optimization is carried out in mgedist via optim to minimize the goodness-of-fit distance. The use of this method requires an additional argument gof coding for the goodness-of-fit distance chosen. One can use the classical Cramer-von Mises distance ("CvM"), the classical Kolmogorov-Smirnov distance ("KS"), the classical Anderson-Darling distance ("AD") which gives more weight to the tails of the distribution, or one of the variants of this last distance proposed by Luceno (2006) (see mgedist for more details). This method is not suitable for discrete distributions.

When method = "mse"

Maximum goodness-of-fit estimation consists in maximizing the average log spacing. A numerical optimization is carried out in msedist via optim.

By default, direct optimization of the log-likelihood (or other criteria depending of the chosen method) 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 optimization 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.

Once the parameter(s) is(are) estimated, fitdist computes the log-likelihood for every estimation method and for maximum likelihood estimation 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-2 points from the dataset and adding the minimum and the maximum. If combined with bootdist, and use with non-parametric bootstrap be aware that bootstrap is performed on the subset randomly selected in fitdist. Currently, the graphical comparisons of multiple fits is not available in this framework.

Weighted version of the estimation process is available for method = "mle", "mme", "qme" by using weights=.... See the corresponding man page for details. Weighted maximum GOF estimation (when method = "mge") is not allowed. It is not yet possible to take into account weighths in functions plotdist, plot.fitdist, cdfcomp, denscomp, ppcomp, qqcomp, gofstat and descdist (developments planned in the future).

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

NB: if your data values are particularly small or large, a scaling may be needed before the optimization process. See example (14) in this man page and examples (14,15) in the test file of the package. Please also take a look at the Rmpfr package available on CRAN for numerical accuracy issues.

Value

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

estimate: the parameter estimates.
method: the character string coding for the fitting method : "mle" for 'maximum likelihood estimation', "mme" for 'matching moment estimation', "qme" for 'matching quantile estimation' "mge" for 'maximum goodness-of-fit estimation' and "mse" for 'maximum spacing estimation'.
sd: the estimated standard errors, NA if numerically not computable or NULL if not available.
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 for the estimation method considered.
loglik: the log-likelihood.
aic: the Akaike information criterion.
bic: the the so-called BIC or SBC (Schwarz Bayesian criterion).
n: the length of the data set.
data: the 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 bootdist in iterative calls to mledist, mmedist, qmedist, mgedist 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: the input argument or the automatic definition by the function to be passed to functions gofstat, plotdist and cdfcomp.
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 and when the maximum likelihood is used, the standard errors of the parameter estimates and the correlation matrix between parameter estimates.
plot: The plot of an object of class "fitdist" returned by fitdist uses the function plotdist. An object of class "fitdist" or a list of objects of class "fitdist" corresponding to various fits using the same data set may also be plotted using a cdf plot (function cdfcomp), a density plot(function denscomp), a density Q-Q plot (function qqcomp), or a P-P plot (function ppcomp).
logLik: Extracts the estimated log-likelihood from the "fitdist" object.
AIC: Extracts the AIC from the "fitdist" object.
BIC: Extracts the estimated BIC from the "fitdist" object.
vcov: Extracts the estimated var-covariance matrix from the "fitdist" object (only available When method = "mle").
coef: Extracts the fitted coefficients from the "fitdist" object.

References

I. Ibragimov and R. Has'minskii (1981), Statistical Estimation - Asymptotic Theory, Springer-Verlag, doi:10.1007/978-1-4899-0027-2

Cullen AC and Frey HC (1999), Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 81-155.

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 .

Vose D (2000), Risk analysis, a quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143.

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 gamma distribution by maximum likelihood estimation
#

data(groundbeef)
serving <- groundbeef$serving
fitg <- fitdist(serving, "gamma")
summary(fitg)
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters : 
#>         estimate Std. Error
#> shape 4.00955898 5.44184369
#> rate  0.05443907 0.07868664
#> Loglikelihood:  -1253.625   AIC:  2511.25   BIC:  2518.325 
#> Correlation matrix:
#>           shape      rate
#> shape 1.0000000 0.9384578
#> rate  0.9384578 1.0000000
#> 
plot(fitg)

plot(fitg, demp = TRUE)

plot(fitg, histo = FALSE, demp = TRUE)

cdfcomp(fitg, addlegend=FALSE)

denscomp(fitg, addlegend=FALSE)

ppcomp(fitg, addlegend=FALSE)

qqcomp(fitg, addlegend=FALSE)



# (2) use the moment matching estimation (using a closed formula)
#

fitgmme <- fitdist(serving, "gamma", method="mme")
summary(fitgmme)
#> Fitting of the distribution ' gamma ' by matching moments 
#> Parameters : 
#>         estimate Std. Error
#> shape 4.22848617 6.64959843
#> rate  0.05741663 0.09451052
#> Loglikelihood:  -1253.825   AIC:  2511.65   BIC:  2518.724 
#> Correlation matrix:
#>           shape      rate
#> shape 1.0000000 0.9553622
#> rate  0.9553622 1.0000000
#> 

# (3) Comparison of various fits 
#

fitW <- fitdist(serving, "weibull")
fitg <- fitdist(serving, "gamma")
fitln <- fitdist(serving, "lnorm")
summary(fitW)
#> Fitting of the distribution ' weibull ' by maximum likelihood 
#> Parameters : 
#>        estimate Std. Error
#> shape  2.185885    1.66666
#> scale 83.347679   40.27156
#> Loglikelihood:  -1255.225   AIC:  2514.449   BIC:  2521.524 
#> Correlation matrix:
#>          shape    scale
#> shape 1.000000 0.321821
#> scale 0.321821 1.000000
#> 
summary(fitg)
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters : 
#>         estimate Std. Error
#> shape 4.00955898 5.44184369
#> rate  0.05443907 0.07868664
#> Loglikelihood:  -1253.625   AIC:  2511.25   BIC:  2518.325 
#> Correlation matrix:
#>           shape      rate
#> shape 1.0000000 0.9384578
#> rate  0.9384578 1.0000000
#> 
summary(fitln)
#> Fitting of the distribution ' lnorm ' by maximum likelihood 
#> Parameters : 
#>          estimate Std. Error
#> meanlog 4.1693701  0.5366095
#> sdlog   0.5366095  0.3794343
#> Loglikelihood:  -1261.319   AIC:  2526.639   BIC:  2533.713 
#> Correlation matrix:
#>         meanlog sdlog
#> meanlog       1     0
#> sdlog         0     1
#> 
cdfcomp(list(fitW, fitg, fitln), legendtext=c("Weibull", "gamma", "lognormal"))

denscomp(list(fitW, fitg, fitln), legendtext=c("Weibull", "gamma", "lognormal"))

qqcomp(list(fitW, fitg, fitln), legendtext=c("Weibull", "gamma", "lognormal"))

ppcomp(list(fitW, fitg, fitln), legendtext=c("Weibull", "gamma", "lognormal"))

gofstat(list(fitW, fitg, fitln), fitnames=c("Weibull", "gamma", "lognormal"))
#> Goodness-of-fit statistics
#>                                Weibull     gamma lognormal
#> Kolmogorov-Smirnov statistic 0.1396646 0.1281486 0.1493090
#> Cramer-von Mises statistic   0.6840994 0.6936274 0.8277358
#> Anderson-Darling statistic   3.5736460 3.5672625 4.5436542
#> 
#> Goodness-of-fit criteria
#>                                 Weibull    gamma lognormal
#> Akaike's Information Criterion 2514.449 2511.250  2526.639
#> Bayesian Information Criterion 2521.524 2518.325  2533.713

# (4) 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))

fitgumbel <- fitdist(serving, "gumbel", start=list(a=10, b=10))
#> Error in fitdist(serving, "gumbel", start = list(a = 10, b = 10)): The  dgumbel  function must be defined
summary(fitgumbel)
#> Error: object 'fitgumbel' not found
plot(fitgumbel)
#> Error: object 'fitgumbel' not found

# (5) fit discrete distributions (Poisson and negative binomial)
#

data(toxocara)
number <- toxocara$number
fitp <- fitdist(number,"pois")
summary(fitp)
#> Fitting of the distribution ' pois ' by maximum likelihood 
#> Parameters : 
#>        estimate Std. Error
#> lambda 8.679245   2.946056
#> Loglikelihood:  -507.5334   AIC:  1017.067   BIC:  1019.037 
plot(fitp)

fitnb <- fitdist(number,"nbinom")
summary(fitnb)
#> Fitting of the distribution ' nbinom ' by maximum likelihood 
#> Parameters : 
#>       estimate Std. Error
#> size 0.3971457  0.6034502
#> mu   8.6802520 14.0870858
#> Loglikelihood:  -159.3441   AIC:  322.6882   BIC:  326.6288 
#> Correlation matrix:
#>              size           mu
#> size  1.000000000 -0.000103855
#> mu   -0.000103855  1.000000000
#> 
plot(fitnb)


cdfcomp(list(fitp,fitnb))

gofstat(list(fitp,fitnb))
#> Chi-squared statistic:  31256.96 7.48606 
#> Degree of freedom of the Chi-squared distribution:  5 4 
#> Chi-squared p-value:  0 0.1123255 
#>    the p-value may be wrong with some theoretical counts < 5  
#> Chi-squared table:
#>       obscounts theo 1-mle-pois theo 2-mle-nbinom
#> <= 0         14     0.009014207         15.295027
#> <= 1          8     0.078236515          5.808596
#> <= 3          6     1.321767253          6.845015
#> <= 4          6     2.131297825          2.407815
#> <= 9          6    29.827829425          7.835196
#> <= 21         6    19.626223437          8.271110
#> > 21          7     0.005631338          6.537242
#> 
#> Goodness-of-fit criteria
#>                                1-mle-pois 2-mle-nbinom
#> Akaike's Information Criterion   1017.067     322.6882
#> Bayesian Information Criterion   1019.037     326.6288

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

data(groundbeef)
serving <- groundbeef$serving
fitdist(serving, "gamma", optim.method="Nelder-Mead")
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters:
#>         estimate Std. Error
#> shape 4.00955898 5.44184369
#> rate  0.05443907 0.07868664
fitdist(serving, "gamma", optim.method="BFGS") 
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters:
#>         estimate Std. Error
#> shape 4.21183650 5.72702767
#> rate  0.05719298 0.08257022
fitdist(serving, "gamma", optim.method="SANN")
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters:
#>         estimate Std. Error
#> shape 3.85618448   5.225475
#> rate  0.05179296   0.074932

# (7) custom optimization function
#
# \donttest{
#create the sample
set.seed(1234)
mysample <- rexp(100, 5)
mystart <- list(rate=8)

res1 <- fitdist(mysample, dexp, start= mystart, optim.method="Nelder-Mead")

#show the result
summary(res1)
#> Fitting of the distribution ' exp ' by maximum likelihood 
#> Parameters : 
#>      estimate Std. Error
#> rate 5.120312   5.120312
#> Loglikelihood:  63.32596   AIC:  -124.6519   BIC:  -122.0467 

#the warning tell us to use optimise, because the Nelder-Mead is not adequate.

#to meet the standard 'fn' argument and specific name arguments, we wrap optimize, 
myoptimize <- function(fn, par, ...) 
{
    res <- optimize(f=fn, ..., maximum=FALSE)  
    #assume the optimization function minimize
    
    standardres <- c(res, convergence=0, value=res$objective, 
        par=res$minimum, hessian=NA)
    
    return(standardres)
}

#call fitdist with a 'custom' optimization function
res2 <- fitdist(mysample, "exp", start=mystart, custom.optim=myoptimize, 
    interval=c(0, 100))

#show the result
summary(res2)
#> Fitting of the distribution ' exp ' by maximum likelihood 
#> Parameters : 
#>      estimate
#> rate 5.120531
#> Loglikelihood:  63.32596   AIC:  -124.6519   BIC:  -122.0467 
# }


# (8) custom optimization function - another example with the genetic algorithm
#
# \donttest{
    #set a sample
    fit1 <- fitdist(serving, "gamma")
    summary(fit1)
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters : 
#>         estimate Std. Error
#> shape 4.00955898 5.44184369
#> rate  0.05443907 0.07868664
#> Loglikelihood:  -1253.625   AIC:  2511.25   BIC:  2518.325 
#> Correlation matrix:
#>           shape      rate
#> shape 1.0000000 0.9384578
#> rate  0.9384578 1.0000000
#> 

    #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 fitdist with a 'custom' optimization function
    fit2 <- fitdist(serving, "gamma", custom.optim=mygenoud, nvars=2,    
        Domains=cbind(c(0, 0), c(10, 10)), boundary.enforcement=1, 
        print.level=1, hessian=TRUE)
#> Loading required package: rgenoud
#> ##  rgenoud (Version 5.9-0.11, Build Date: 2024-10-03)
#> ##  See http://sekhon.berkeley.edu/rgenoud for additional documentation.
#> ##  Please cite software as:
#> ##   Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011.
#> ##   ``Genetic Optimization Using Derivatives: The rgenoud package for R.''
#> ##   Journal of Statistical Software, 42(11): 1-26. 
#> ##
#> 
#> 
#> Sat Oct 26 05:39:31 2024
#> Domains:
#>  0.000000e+00   <=  X1   <=    1.000000e+01 
#>  0.000000e+00   <=  X2   <=    1.000000e+01 
#> 
#> 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 	4.936206e+00
#> 
#> 'wait.generations' limit reached.
#> No significant improvement in 10 generations.
#> 
#> Solution Fitness Value: 4.935532e+00
#> 
#> Parameters at the Solution (parameter, gradient):
#> 
#>  X[ 1] :	4.008339e+00	G[ 1] :	2.759167e-10
#>  X[ 2] :	5.442735e-02	G[ 2] :	-3.841725e-07
#> 
#> Solution Found Generation 1
#> Number of Generations Run 11
#> 
#> Sat Oct 26 05:39:32 2024
#> Total run time : 0 hours 0 minutes and 1 seconds

    summary(fit2)
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters : 
#>         estimate Std. Error
#> shape 4.00833889 5.44012540
#> rate  0.05442735 0.07867032
#> Loglikelihood:  -1253.625   AIC:  2511.25   BIC:  2518.325 
#> Correlation matrix:
#>           shape      rate
#> shape 1.0000000 0.9384394
#> rate  0.9384394 1.0000000
#> 
# }

# (9) estimation of the standard deviation of a gamma distribution 
# by maximum likelihood with the shape fixed at 4 using the argument fix.arg
#

data(groundbeef)
serving <- groundbeef$serving
f1c  <- fitdist(serving,"gamma",start=list(rate=0.1),fix.arg=list(shape=4))
summary(f1c)
#> Fitting of the distribution ' gamma ' by maximum likelihood 
#> Parameters : 
#>        estimate Std. Error
#> rate 0.05431619 0.02714888
#> Fixed parameters:
#>       value
#> shape     4
#> Loglikelihood:  -1253.625   AIC:  2509.251   BIC:  2512.788 
plot(f1c)


# (10) fit of a Weibull distribution to serving size data 
# by maximum likelihood estimation
# or by quantile matching estimation (in this example 
# matching first and third quartiles)
#

data(groundbeef)
serving <- groundbeef$serving
fWmle <- fitdist(serving, "weibull")
summary(fWmle)
#> Fitting of the distribution ' weibull ' by maximum likelihood 
#> Parameters : 
#>        estimate Std. Error
#> shape  2.185885    1.66666
#> scale 83.347679   40.27156
#> Loglikelihood:  -1255.225   AIC:  2514.449   BIC:  2521.524 
#> Correlation matrix:
#>          shape    scale
#> shape 1.000000 0.321821
#> scale 0.321821 1.000000
#> 
plot(fWmle)

gofstat(fWmle)
#> Goodness-of-fit statistics
#>                              1-mle-weibull
#> Kolmogorov-Smirnov statistic     0.1396646
#> Cramer-von Mises statistic       0.6840994
#> Anderson-Darling statistic       3.5736460
#> 
#> Goodness-of-fit criteria
#>                                1-mle-weibull
#> Akaike's Information Criterion      2514.449
#> Bayesian Information Criterion      2521.524

fWqme <- fitdist(serving, "weibull", method="qme", probs=c(0.25, 0.75))
summary(fWqme)
#> Fitting of the distribution ' weibull ' by matching quantiles 
#> Parameters : 
#>        estimate
#> shape  2.268699
#> scale 86.590853
#> Loglikelihood:  -1256.129   AIC:  2516.258   BIC:  2523.332 
plot(fWqme)

gofstat(fWqme)
#> Goodness-of-fit statistics
#>                              1-qme-weibull
#> Kolmogorov-Smirnov statistic     0.1692858
#> Cramer-von Mises statistic       0.9664709
#> Anderson-Darling statistic       4.8479858
#> 
#> Goodness-of-fit criteria
#>                                1-qme-weibull
#> Akaike's Information Criterion      2516.258
#> Bayesian Information Criterion      2523.332


# (11) Fit of a Pareto distribution by numerical moment matching estimation
#

# \donttest{
    require("actuar")
#> Loading required package: actuar
#> 
#> Attaching package: ‘actuar’
#> The following objects are masked from ‘package:stats’:
#> 
#>     sd, var
#> The following object is masked from ‘package:grDevices’:
#> 
#>     cm
    #simulate a sample
    x4 <- rpareto(1000, 6, 2)

    #empirical raw moment
    memp <- function(x, order) mean(x^order)

    #fit
    fP <- fitdist(x4, "pareto", method="mme", order=c(1, 2), memp="memp", 
      start=list(shape=10, scale=10), lower=1, upper=Inf)
#> Error in mmedist(data, distname, start = arg_startfix$start.arg, fix.arg = arg_startfix$fix.arg,     checkstartfix = TRUE, calcvcov = calcvcov, ...): the empirical moment must be defined as a function
    summary(fP)
#> Error: object 'fP' not found
    plot(fP)
#> Error: object 'fP' not found

# }

# (12) Fit of a Weibull distribution to serving size data by maximum 
# goodness-of-fit estimation using all the distances available
# 
# \donttest{
data(groundbeef)
serving <- groundbeef$serving
(f1 <- fitdist(serving, "weibull", method="mge", gof="CvM"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.093204
#> scale 82.660014
(f2 <- fitdist(serving, "weibull", method="mge", gof="KS"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.065634
#> scale 81.450487
(f3 <- fitdist(serving, "weibull", method="mge", gof="AD"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.125425
#> scale 82.890502
(f4 <- fitdist(serving, "weibull", method="mge", gof="ADR"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.072035
#> scale 82.762593
(f5 <- fitdist(serving, "weibull", method="mge", gof="ADL"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.197498
#> scale 82.016005
(f6 <- fitdist(serving, "weibull", method="mge", gof="AD2R"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>       estimate
#> shape  1.90328
#> scale 81.33464
(f7 <- fitdist(serving, "weibull", method="mge", gof="AD2L"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.483836
#> scale 78.252113
(f8 <- fitdist(serving, "weibull", method="mge", gof="AD2"))
#> Fitting of the distribution ' weibull ' by maximum goodness-of-fit 
#> Parameters:
#>        estimate
#> shape  2.081168
#> scale 85.281194
cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8))

cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8), 
  xlogscale=TRUE, xlim=c(8, 250), verticals=TRUE)

denscomp(list(f1, f2, f3, f4, f5, f6, f7, f8))

# }

# (13) Fit of a uniform distribution using maximum likelihood 
# (a closed formula is used in this special case where the loglikelihood is not defined),
# or maximum goodness-of-fit with Cramer-von Mises or Kolmogorov-Smirnov distance
# 

set.seed(1234)
u <- runif(50, min=5, max=10)

fumle <- fitdist(u, "unif", method="mle")
summary(fumle)
#> Fitting of the distribution ' unif ' by maximum likelihood 
#> Parameters : 
#>     estimate
#> min 5.047479
#> max 9.960752
#> Loglikelihood:  -79.59702   AIC:  163.194   BIC:  167.0181 
plot(fumle)

gofstat(fumle)
#> Goodness-of-fit statistics
#>                              1-mle-unif
#> Kolmogorov-Smirnov statistic  0.1340723
#> Cramer-von Mises statistic    0.1566892
#> Anderson-Darling statistic          Inf
#> 
#> Goodness-of-fit criteria
#>                                1-mle-unif
#> Akaike's Information Criterion   163.1940
#> Bayesian Information Criterion   167.0181

fuCvM <- fitdist(u, "unif", method="mge", gof="CvM")
summary(fuCvM)
#> Fitting of the distribution ' unif ' by maximum goodness-of-fit 
#> Parameters : 
#>     estimate
#> min 5.110497
#> max 9.552878
#> Loglikelihood:  -Inf   AIC:  Inf   BIC:  Inf 
plot(fuCvM)

gofstat(fuCvM)
#> Goodness-of-fit statistics
#>                              1-mge-unif
#> Kolmogorov-Smirnov statistic 0.11370966
#> Cramer-von Mises statistic   0.07791651
#> Anderson-Darling statistic          Inf
#> 
#> Goodness-of-fit criteria
#>                                1-mge-unif
#> Akaike's Information Criterion        Inf
#> Bayesian Information Criterion        Inf

fuKS <- fitdist(u, "unif", method="mge", gof="KS")
summary(fuKS)
#> Fitting of the distribution ' unif ' by maximum goodness-of-fit 
#> Parameters : 
#>     estimate
#> min 5.092357
#> max 9.323818
#> Loglikelihood:  -Inf   AIC:  Inf   BIC:  Inf 
plot(fuKS)

gofstat(fuKS)
#> Goodness-of-fit statistics
#>                              1-mge-unif
#> Kolmogorov-Smirnov statistic 0.09216159
#> Cramer-von Mises statistic   0.12241830
#> Anderson-Darling statistic          Inf
#> 
#> Goodness-of-fit criteria
#>                                1-mge-unif
#> Akaike's Information Criterion        Inf
#> Bayesian Information Criterion        Inf

# (14) scaling problem
# the simulated dataset (below) has particularly small values, hence without scaling (10^0),
# the optimization raises an error. The for loop shows how scaling by 10^i
# for i=1,...,6 makes the fitting procedure work correctly.

set.seed(1234)
x2 <- rnorm(100, 1e-4, 2e-4)

for(i in 0:6)
        cat(i, try(fitdist(x2*10^i, "cauchy", method="mle")$estimate, silent=TRUE), "\n")
#> 0 1.876032e-05 0.000110131 
#> 1 0.0001876032 0.00110131 
#> 2 0.001870693 0.01100646 
#> 3 0.01871473 0.1100713 
#> 4 0.1870693 1.100646 
#> 5 1.876032 11.0131 
#> 6 18.76032 110.131 

# (15) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for
# nonarthropod invertebrates, 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(endosulfan)
ATV <- subset(endosulfan, group == "NonArthroInvert")$ATV
log10ATV <- log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
fln <- fitdist(log10ATV, "norm")

quantile(fln, probs = 0.05)
#> Estimated quantiles for each specified probability (non-censored data)
#>            p=0.05
#> estimate 1.744227
quantile(fln, probs = c(0.05, 0.1, 0.2))
#> Estimated quantiles for each specified probability (non-censored data)
#>            p=0.05    p=0.1  p=0.2
#> estimate 1.744227 2.080093 2.4868


# (16) Fit of a triangular distribution using Cramer-von Mises or
# Kolmogorov-Smirnov distance
# 

# \donttest{
set.seed(1234)
require("mc2d")
#> Loading required package: mc2d
#> Loading required package: mvtnorm
#> 
#> Attaching package: ‘mc2d’
#> The following objects are masked from ‘package:base’:
#> 
#>     pmax, pmin
t <- rtriang(100, min=5, mode=6, max=10)
fCvM <- fitdist(t, "triang", method="mge", start = list(min=4, mode=6,max=9), gof="CvM")
#> Warning: Some parameter names have no starting/fixed value but have a default value: mean.
fKS <- fitdist(t, "triang", method="mge", start = list(min=4, mode=6,max=9), gof="KS")
#> Warning: Some parameter names have no starting/fixed value but have a default value: mean.
cdfcomp(list(fCvM,fKS))

# }

# (17) fit a non classical discrete distribution (the zero inflated Poisson distribution)
#
# \donttest{
require("gamlss.dist")
#> Loading required package: gamlss.dist
set.seed(1234)
x <- rZIP(n = 30, mu = 5, sigma = 0.2)
plotdist(x, discrete = TRUE)

fitzip <- fitdist(x, "ZIP", start =  list(mu = 4, sigma = 0.15), discrete = TRUE, 
  optim.method = "L-BFGS-B", lower = c(0, 0), upper = c(Inf, 1))
#> Warning: The dZIP function should return a zero-length vector when input has length zero
#> Warning: The pZIP function should return a zero-length vector when input has length zero
summary(fitzip)
#> Fitting of the distribution ' ZIP ' by maximum likelihood 
#> Parameters : 
#>        estimate Std. Error
#> mu    4.3166098  2.3777816
#> sigma 0.1891794  0.4062398
#> Loglikelihood:  -67.13886   AIC:  138.2777   BIC:  141.0801 
#> Correlation matrix:
#>               mu      sigma
#> mu    1.00000000 0.06418931
#> sigma 0.06418931 1.00000000
#> 
plot(fitzip)

fitp <- fitdist(x, "pois")
cdfcomp(list(fitzip, fitp))

gofstat(list(fitzip, fitp))
#> Chi-squared statistic:  3.579708 35.91516 
#> Degree of freedom of the Chi-squared distribution:  3 4 
#> Chi-squared p-value:  0.3105704 3.012341e-07 
#>    the p-value may be wrong with some theoretical counts < 5  
#> Chi-squared table:
#>      obscounts theo 1-mle-ZIP theo 2-mle-pois
#> <= 0         6       5.999996       0.9059215
#> <= 2         7       4.425507       8.7194943
#> <= 4         5       9.047522      12.1379326
#> <= 5         5       4.054142       3.9650580
#> <= 7         5       4.715294       3.4694258
#> > 7          2       1.757539       0.8021677
#> 
#> Goodness-of-fit criteria
#>                                1-mle-ZIP 2-mle-pois
#> Akaike's Information Criterion  138.2777   153.7397
#> Bayesian Information Criterion  141.0801   155.1409
# }



# (18) examples with distributions in actuar (predefined starting values)
#
# \donttest{
require("actuar")
x <- c(2.3,0.1,2.7,2.2,0.4,2.6,0.2,1.,7.3,3.2,0.8,1.2,33.7,14.,
       21.4,7.7,1.,1.9,0.7,12.6,3.2,7.3,4.9,4000.,2.5,6.7,3.,63.,
       6.,1.6,10.1,1.2,1.5,1.2,30.,3.2,3.5,1.2,0.2,1.9,0.7,17.,
       2.8,4.8,1.3,3.7,0.2,1.8,2.6,5.9,2.6,6.3,1.4,0.8)
#log logistic
ft_llogis <- fitdist(x,'llogis')

x <- c(0.3837053, 0.8576858, 0.3552237, 0.6226119, 0.4783756, 0.3139799, 0.4051403, 
       0.4537631, 0.4711057, 0.5647414, 0.6479617, 0.7134207, 0.5259464, 0.5949068, 
       0.3509200, 0.3783077, 0.5226465, 1.0241043, 0.4384580, 1.3341520)
#inverse weibull
ft_iw <- fitdist(x,'invweibull')
# }