prefit  R Documentation 
Search good starting values
prefit(data, distr, method = c("mle", "mme", "qme", "mge"),
feasible.par, memp=NULL, order=NULL,
probs=NULL, qtype=7, gof=NULL, fix.arg=NULL, lower,
upper, weights=NULL, silent=TRUE, ...)
data 
A numeric vector. 
distr 
A character string 
method 
A character string coding for the fitting method:

feasible.par 
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 
order 
A numeric vector for the moment order(s). The length of this vector must be equal to the number of parameters to estimate. 
memp 
A function implementing empirical moments, raw or centered but has to be consistent with

probs 
A numeric vector of the probabilities for which the quantile matching is done. The length of this vector must be equal to the number of parameters to estimate. 
qtype 
The quantile type used by the R 
gof 
A character string coding for the name of the goodnessoffit distance used : "CvM" for Cramervon Mises distance,"KS" for KolmogorovSmirnov distance, "AD" for AndersonDarling distance, "ADR", "ADL", "AD2R", "AD2L" and "AD2" for variants of AndersonDarling distance described by Luceno (2006). 
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 
weights 
an optional vector of weights to be used in the fitting process.
Should be 
silent 
A logical to remove or show warnings. 
lower 
Lower bounds on the parameters. 
upper 
Upper bounds on the parameters. 
... 
Further arguments to be passed to generic functions, or to one of the functions

Searching good starting values is achieved by transforming the parameters (from their constraint interval to the real line) of the probability distribution. Indeed,
positive parameters in (0,Inf)
are transformed using the logarithm
(typically the scale parameter sd
of a normal distribution, see Normal),
parameters in (1,Inf)
are transformed using the function log(x1)
,
probability parameters in (0,1)
are transformed using the logit function log(x/(1x))
(typically the parameter prob
of a geometric distribution, see Geometric),
negative probability parameters in (1,0)
are transformed using the function log(x/(1+x))
,
real parameters are of course not transformed at all,
typically the mean
of a normal distribution, see Normal.
Once parameters are transformed, an optimization is carried out by a quasiNewton algorithm (typically BFGS) and then we transform them back to original parameter value.
A named list.
Christophe Dutang and MarieLaure DelignetteMuller.
DelignetteMuller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 134.
See mledist
, mmedist
, qmedist
,
mgedist
for details on parameter estimation.
See fitdist
for the main procedure.
# (1) fit of a gamma distribution by maximum likelihood estimation
#
x < rgamma(1e3, 5/2, 7/2)
prefit(x, "gamma", "mle", list(shape=3, scale=3), lower=Inf, upper=Inf)
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