stability.plot {envoutliers}  R Documentation 
A stability plot for maximum likelihood or moment estimates of the GP parameters (Coles, 2001), including confidence intervals, at a range of thresholds or number of the largest observations.
stability.plot(x, umin = quantile(na.omit(x), probs = 0.8), umax = quantile(na.omit(x), probs = 0.95), kmin = round(length(na.omit(x)) * 0.05), kmax = round(length(na.omit(x)) * 0.2), nint = 100, conf = 0.95, est.method = "mle", u0 = NULL, k0 = NULL)
x 
data values. Supported data types

umin 
the minimum threshold at which the mean residual life function is calculated. Default is 
umax 
the maximum threshold at which the mean residual life function is calculated. Default is 
kmin 
the minimum number of largest order statistics for which the mean residual life function is calculated based on moment estimates. Default is 
kmax 
the maximum number of largest order statistics for which the mean residual life function is calculated based on moment estimates. Default is 
nint 
the number of points at which the mean residual life function is calculated. Default is 
conf 
the confidence coefficient for the confidence intervals depicted in the plot. Default is 
est.method 
a character string specifying the type of estimates for the scale and shape parameters of GP distribution. Possible options are

u0 
a numeric value giving the threshold meant for a GP approximation of the threshold exceedances. Default is 
k0 
a numeric value giving the number 
The function estimates the GP parameters at a range of thresholds (in case est.method = "mle"
) or a range of upper order statistics (in case of est.method = "moment"
), and shows the sample paths of the estimates.
The estimates of the shape or the scale parameter are expected to be constant or to change linearly, respectively, with threshold levels at which the GP model is appropriate.
If u0
(or k0
, respectively) is given, a thresholddependency lines for the particular parameters are plotted in addition. The lines provide the user an option to assess the suitability of u0
or k0
as a lower bound for the threshold exceedances (for u0
) or the number of upper order statistics (for k0
) to fit the GP distribution.
In case est.method = "mle"
and u0
takes a value, the theoretical dependency lines for the parameters are evaluated on the basis of MLE estimates. For the case est.method = "moment"
and k0
is given, the dependency lines are estimated using the moment estimators.
In case est.method = "moment"
the value x(nk)
on the xaxis of MRL plot denotes the (k + 1)
th largest observation of the total number of n
observations.
Theo Gasser, Alois Kneip & Walter Koehler (1991) A flexible and fast method for automatic smoothing. Journal of the American Statistical Association 86, 643652. https://doi.org/10.2307/2290393
E. Herrmann (1997) Local bandwidth choice in kernel regression estimation. Journal of Graphical and Computational Statistics 6, 3554.
Herrmann E, Maechler M (2013). lokern: Kernel Regression Smoothing with Local or Global Plugin Bandwidth. R package version 1.15, URL http://CRAN.Rproject.org/package=lokern.
Gasser, T, Muller, HG, Mammitzsch, V (1985). Kernels for nonparametric curve estimation. Journal of the Royal Statistical Society, B Met., 47(2), 238252.
Coles, S (2001). An Introduction to Statistical Modeling of Extreme Values. SpringerVerlag, London, U.K., 208pp.
de Haan, L, Ferreira, A (2006). Extreme Value Theory: An Introduction. Springer.
data("mydata", package = "openair") x = mydata$o3[format(mydata$date, "%m %Y") == "12 2002"] res = smoothing(y = x)$residuals stability.plot(res)