maximum.likelihood()
and
least.squares()
. Draws an empirical quantile function (fit.davies.p()
) or PDF (fit.davies.q()
) and the datasetfit.davies.p(x , print.fit=FALSE, use.q=TRUE , params=NULL, small=1e-5 , ...)
fit.davies.q(x , print.fit=FALSE, use.q=TRUE , params=NULL, ...)
TRUE
meaning print details of the fitTRUE
meaning use least.squares()
(rather than maximum.likelihood()
)NULL
, determine the parameters using the method
indicated by use.q
plot()
print.fit
is TRUE
, return the optimal parametersleast.squares
, maximum.likelihood
fit.davies.q(rnorm(100)^2)
fit.davies.p(exp(rnorm(100)))
data(x00m700p4)
fit.davies.q(x00m700p4)
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