fitdistrplus (version 1.0-8)

mgedist: Maximum goodness-of-fit fit of univariate continuous distributions

Description

Fit of univariate continuous distribution by maximizing goodness-of-fit (or minimizing distance) for non censored data.

Usage

mgedist(data, distr, gof = "CvM", start = NULL, fix.arg = NULL, optim.method = "default",  lower = -Inf, upper = Inf, custom.optim = NULL, silent = TRUE, gradient = NULL, ...)

Arguments

data

A numeric vector for non censored data.

distr

A character string "name" naming a distribution for which the corresponding quantile function qname and the corresponding density distribution dname must be classically defined.

gof

A character string coding for the name of the goodness-of-fit distance used : "CvM" for Cramer-von Mises distance,"KS" for Kolmogorov-Smirnov distance, "AD" for Anderson-Darling distance, "ADR", "ADL", "AD2R", "AD2L" and "AD2" for variants of Anderson-Darling distance described by Luceno (2006).

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).

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.

optim.method

"default" or optimization method to pass to optim.

lower

Left bounds on the parameters for the "L-BFGS-B" method (see optim).

upper

Right bounds on the parameters for the "L-BFGS-B" method (see optim).

custom.optim

a function carrying the optimization.

silent

A logical to remove or show warnings when bootstraping.

gradient

A function to return the gradient of the gof distance for the "BFGS", "CG" and "L-BFGS-B" methods. If it is NULL, a finite-difference approximation will be used.

...

further arguments passed to the optim, constrOptim or custom.optim function.

Value

mgedist returns a list with following components,

Details

The mgedist function numerically maximizes goodness-of-fit, or minimizes a goodness-of-fit distance coded by the argument gof. One may use one of the classical distances defined in Stephens (1986), the Cramer-von Mises distance ("CvM"), the Kolmogorov-Smirnov distance ("KS") or the 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). The right-tail AD ("ADR") gives more weight only to the right tail, the left-tail AD ("ADL") gives more weight only to the left tail. Either of the tails, or both of them, can receive even larger weights by using second order Anderson-Darling Statistics (using "AD2R", "AD2L" or "AD2"). The optimization process is the same as mledist, see the 'details' section of that function.

This function is not intended to be called directly but is internally called in fitdist and bootdist. This function is intended to be used only with continuous distributions and weighted maximum goodness-of-fit estimation is not allowed. NB: if your data values are particularly small or large, a scaling may be needed before the optimization process. See example (4).

References

Luceno A (2006), Fitting the generalized Pareto distribution to data using maximum goodness-of-fit estimators. Computational Statistics and Data Analysis, 51, 904-917.

Stephens MA (1986), Tests based on edf statistics. In Goodness-of-fit techniques (D'Agostino RB and Stephens MA, eds), Marcel Dekker, New York, pp. 97-194.

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

See Also

mmedist, mledist, qmedist, fitdist for other estimation methods.

Examples

# (1) Fit of a Weibull distribution to serving size data by maximum 
# goodness-of-fit estimation using all the distances available
# 

data(groundbeef)
serving <- groundbeef$serving
mgedist(serving, "weibull", gof="CvM")
mgedist(serving, "weibull", gof="KS")
mgedist(serving, "weibull", gof="AD")
mgedist(serving, "weibull", gof="ADR")
mgedist(serving, "weibull", gof="ADL")
mgedist(serving, "weibull", gof="AD2R")
mgedist(serving, "weibull", gof="AD2L")
mgedist(serving, "weibull", gof="AD2")


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

set.seed(1234)
u <- runif(100,min=5,max=10)
mgedist(u,"unif",gof="CvM")
mgedist(u,"unif",gof="KS")

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

## Not run: 
# require(mc2d)
# set.seed(1234)
# t <- rtriang(100,min=5,mode=6,max=10)
# mgedist(t,"triang",start = list(min=4, mode=6,max=9),gof="CvM")
# mgedist(t,"triang",start = list(min=4, mode=6,max=9),gof="KS")
# ## End(Not run)

# (4) 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 6:0)
    cat(i, try(mgedist(x*10^i,"cauchy")$estimate, silent=TRUE), "\n")