PvarQuantile {pvar} | R Documentation |
Quantiles and probabilities of p-variation
Description
The distribution of p-variation of BridgeT(x)
depends on n=length(x)
.
This fact is important for getting appropriate quantiles (or p-value).
These functions helps to deal with it.
Usage
PvarQuantile(n, prob = c(0.9, 0.95, 0.99), DF = PvarQuantileDF)
PvarPvalue(n, stat, DF = PvarQuantileDF)
getMean(n, bMean = MeanCoef)
getSd(n, bSd = SdCoef)
NormalisePvar(x, n, bMean = MeanCoef, bSd = SdCoef)
Arguments
n |
a positive integer indicating the length of data vector. |
prob |
cumulative probabilities of p-variation distribution. |
DF |
a |
stat |
a vector of p-variation statistics. |
bMean |
a coefficient vector that defines a function of the mean of p-variation. |
bSd |
a coefficient vector that defines a function of the standard deviation of p-variation. |
x |
a numeric vector of data values. |
Details
The distribution of p-variance is form Monte-Carlo simulation based on 140 millions iterations.
The data frame PvarQuantileDF
saves the results of Monte-Carlo simulation.
Meanwhile, MeanCoef
and SdCoef
defines the coefficients of functional
form (conditional on n
) of mean
and sd
statistics.
A functional form of mean
and sd
statistics are the same, namely
f(n) = b_1 + b_2 n^b_2 .
The coefficients (b_1, b_2, b_3)
are saved in vectors MeanCoef
and SdCoef
.
Those vectors are estimated with nls
function form Monte-Carlo simulation.
Value
Functions PvarQuantile
and PvarPvalue
returns a corresponding value quantile or the probability.
Functions getMean
and getSd
returns a corresponding value of mean
and sd
statistics.
Function NormalisePvar
returns normalize values.
Note
Arguments n
, stat
and prob
might be vectors,
but they can't be vectors simultaneously (at least one of then must be a number).
See Also
PvarBreakTest
, PvarQuantileDF
,
NormalisePvar
, getMean
, getSd