Depending on method this calculates pointwise confidence intervalls for
a smoothed periodgram that belongs to a time series defined by model and
param. If (method = "quantiles") it computes the \(\alpha/2\)
and \(1-\alpha/2\) quantiles from the Values of the simulated smoothed periodograms and
returns those. If (method = "norm") it uses the asymptotic normality
of the smoothed periodograms by estimating mean and
standard deviation for each frequency and computing the \(\alpha/2\)
and \(1-\alpha/2\) quantiles from a normal distribution with the estimated
parameters.
computeCIs(object, alpha = 0.05, method = c("quantiles", "norm"),
levels = object@sPG@levels[[1]])the QPBoot object that will be plotted
the significiant level of the confidence intervalls, defaults to 0.05
either "quantile" or "norm", determines how the confidence intervalls are calculated. see description for details
numeric vector containing values between 0 and 1 for which the smoothedPG. Will be estimated. These are the quantiles levels that are used for the validation
Returns a list with four elements
q_upq_lowmeansd