pareto2.ftest(x, y, s, alternative=c("two.sided", "less", "greater"),
significance)
significance$<1$ or="" NULL. See Value for details.1$>$
significance
is not NULL
, then
the list of class power.htest
with the following components is passed as a result:
statistic
the value of the test statistic.
result
either FALSE (accept null hypothesis) or TRUE (reject).
alternative
a character string describing the alternative hypothesis.
method
a character string indicating what type of test was performed.
data.name
a character string giving the name(s) of the data.
}
Otherwise, the list of class htest
with the following components is passed as a result:
statistic
the value of the test statistic.
p.value
the p-value of the test.
alternative
a character string describing the alternative hypothesis.
method
a character string indicating what type of test was performed.
data.name
a character string giving the name(s) of the data.
}alternative
.
It bases on test statistic
T=n/m*sum(log(1+Y/m))/sum(log(1+X/n))
which, under $H_0$, has the Snedecor's F distribution with $(2m, 2n)$
degrees of freedom.Note that for $k_x < k_y$, then $X$ dominates $Y$ stochastically.
dpareto2
, pareto2.goftest
, pareto2.htest
, pareto2.htest.approx