pDtrend | R Documentation |
This function computes \Pr(D ≤ k) (\Pr(D ≥ k)), i.e.
lower (upper) cumulative probabilities for
D=∑_{i=1}^{n} (R_i - i)^2, the nonparametric trend statistic
proposed by \insertCiteLehmann75;textualskedastic, under the assumption
that the ranks R_i are computed on a series of n independent and
identically distributed random variables with no ties. The function may be
used to compute one-sided p-values for the nonparametric test for
heteroskedasticity of \insertCiteHorn81;textualskedastic. Computation
time is extremely slow for n > 10 if usedata
is set to
FALSE
; thus a normal approximation is implemented, including a
continuity correction.
pDtrend( k, n, lower.tail = TRUE, exact = (n <= 10), tiefreq = NA, override = FALSE )
k |
An integer of |
n |
A positive integer representing the number of observations in the series. |
lower.tail |
A logical. Should lower tailed cumulative probability be
calculated? Defaults to |
exact |
A logical. Should exact distribution of D be used by
calling |
tiefreq |
A double vector corresponding to the value of d_i
in \insertCiteLehmann75;textualskedastic. These are the frequencies
of the various tied ranks. If ties are absent, |
override |
A logical. By default, the |
A double between 0 and 1 representing the probability/ies of D
taking on at least (at most) the value(s) in the names
attribute.
dDtrend
, horn
# For an independent sample of size 6, the probability that D is <= 50 is # 0.8222 pDtrend(k = 50, n = 6) # Normal approximation of the above with continuity correction is # 0.8145 pDtrend(k = 50, n = 6, exact = FALSE)
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