Description Usage Arguments Details Value Author(s) References See Also Examples
Fit of univariate continuous distribution by maximizing goodnessoffit (or minimizing distance) for non censored data.
1 2 3 
data 
A numeric vector for non censored data. 
distr 
A character string 
gof 
A character string coding for the name of the goodnessoffit distance used :

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 
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 

lower 
Left bounds on the parameters for the 
upper 
Right bounds on the parameters for the 
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 
checkstartfix 
A logical to test starting and fixed values. Do not change it. 
... 
further arguments passed to the 
The mgedist
function numerically maximizes goodnessoffit,
or minimizes a goodnessoffit distance coded by the argument
gof
. One may use one of the classical distances defined in Stephens (1986),
the Cramervon Mises distance ("CvM"
), the
KolmogorovSmirnov distance ("KS"
) or the AndersonDarling 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 righttail AD ("ADR"
)
gives more weight only to the right tail, the lefttail 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 AndersonDarling 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 goodnessoffit 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).
mgedist
returns a list with following components,
estimate 
the parameter estimates. 
convergence 
an integer code for the convergence of 
value 
the minimal value reached for the criterion to minimize. 
hessian 
a symmetric matrix computed by 
optim.function 
the name of the optimization function used for maximum likelihood. 
optim.method 
when 
fix.arg 
the named list giving the values of parameters of the named distribution
that must kept fixed rather than estimated by maximum likelihood or 
fix.arg.fun 
the function used to set the value of 
weights 
the vector of weigths used in the estimation process or 
counts 
A twoelement integer vector giving the number of calls
to the loglikelihood function and its gradient respectively.
This excludes those calls needed to compute the Hessian, if requested,
and any calls to loglikelihood function to compute a finitedifference
approximation to the gradient. 
optim.message 
A character string giving any additional information
returned by the optimizer, or 
loglik 
the loglikelihood value. 
gof 
the code of the goodnessoffit distance maximized. 
MarieLaure DelignetteMuller and Christophe Dutang.
Luceno A (2006), Fitting the generalized Pareto distribution to data using maximum goodnessoffit estimators. Computational Statistics and Data Analysis, 51, 904917.
Stephens MA (1986), Tests based on edf statistics. In Goodnessoffit techniques (D'Agostino RB and Stephens MA, eds), Marcel Dekker, New York, pp. 97194.
DelignetteMuller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 134.
mmedist
, mledist
, qmedist
,
fitdist
for other estimation methods.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  # (1) Fit of a Weibull distribution to serving size data by maximum
# goodnessoffit estimation using all the distances available
#
data(groundbeef)
serving < groundbeef$serving
mgedist(serving, "weibull", gof="CvM")
mgedist(serving, "weibull", gof="KS")
mgedist(serving, "weibull", gof="AD")
mgedist(serving, "weibull", gof="ADR")
mgedist(serving, "weibull", gof="ADL")
mgedist(serving, "weibull", gof="AD2R")
mgedist(serving, "weibull", gof="AD2L")
mgedist(serving, "weibull", gof="AD2")
# (2) Fit of a uniform distribution using Cramervon Mises or
# KolmogorovSmirnov distance
#
set.seed(1234)
u < runif(100,min=5,max=10)
mgedist(u,"unif",gof="CvM")
mgedist(u,"unif",gof="KS")
# (3) Fit of a triangular distribution using Cramervon Mises or
# KolmogorovSmirnov distance
#
## Not run:
require(mc2d)
set.seed(1234)
t < rtriang(100,min=5,mode=6,max=10)
mgedist(t,"triang",start = list(min=4, mode=6,max=9),gof="CvM")
mgedist(t,"triang",start = list(min=4, mode=6,max=9),gof="KS")
## End(Not run)
# (4) scaling problem
# the simulated dataset (below) has particularly small values, hence without scaling (10^0),
# the optimization raises an error. The for loop shows how scaling by 10^i
# for i=1,...,6 makes the fitting procedure work correctly.
set.seed(1234)
x2 < rnorm(100, 1e4, 2e4)
for(i in 6:0)
cat(i, try(mgedist(x*10^i,"cauchy")$estimate, silent=TRUE), "\n")

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