A convenience wrapper (and pretty-printer) for
maximum.likelihood()
and least.squares()
. Given a
dataset, it draws an empirical quantile function
(fit.davies.p()
) or PDF (fit.davies.q()
) and
superimposes the dataset.
fit.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, ...)
dataset to be fitted and plotted
Boolean with TRUE
meaning print details of the fit
Boolean with TRUE
meaning use least.squares()
(rather than maximum.likelihood()
)
three-element vector holding the three parameters of the
davies dataset. If NULL
, determine the parameters using the method
indicated by use.q
small positive number showing range of quantiles to plot
Additional parameters passed to plot()
If print.fit
is TRUE
, return the optimal parameters
# NOT RUN {
fit.davies.q(rchisq(100,1))
fit.davies.p(exp(rnorm(100)))
data(x00m700p4)
fit.davies.q(x00m700p4)
# }
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