Description Usage Arguments Value Examples
Compute an interpolation of the transformed cdf in the middle with parametric power law decay on the tails.
1 2 3 4 | ecdfHT.fit(p, transform.info, x.min = NA, x.max = NA, add.to.plot = TRUE,
weights = "var", ...)
ecdfHT.fit.tails(p, transform.info, weights, add.to.plot = TRUE, ...)
|
p |
Vector of 2 probabilities that identify the quantile where data is cut to fit power decay on lower/upper tail. Set tail.p[1]=0 to exclude lower tail fit; tail.p[2]=1 to exclude upper tail fit. |
transform.info |
List containing transformation information, returned from |
x.min |
Number describing cut-off of lower tail |
x.max |
Number describing cut-off of upper tail |
add.to.plot |
Boolean indicating whether or not the interpolation is plotted |
weights |
'none' to do unweighted regression or 'var' to use weighted regression on tail with weights proportional to variance of quantile |
... |
Optional parameters passed to plot routines, e.g. col='red' |
An object of class 'ecdfHT.fit' specifying the interpolation. The fields in the value are:
vector of length 3, copied from the input argument
vector of length 3, the quantiles from the data corresponding to scale.q
vector of the sorted, unique data values
nonstandard empirical cdf, see details
transformed x values: xx[i]=h(xsort[i])
transformed p values: yy[i]=g(ecdf[i])
monotonic spline function used to compute the cdf
monotonic spline function used to compute the inverse of the cdf
vector of length 2; probabilities saying where the lower and upper tails begin. Note these are generally not the exact values of input variable p, rather they are the closest values to those found in ecdf
vector of length 2; x values where the lower and upper tails begin
vector of length 2; tail constants for lower and upper powerlaw fit
vector of length 2; slope of tails on transformed plot
vector of length 2; exponents for lower and upper power law fit
integer vector of length 2; indices in xsort where tails begin
copy of input variable weights
1 2 3 4 |
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