normal.distr.quantiles.detect {envoutliers} | R Documentation |
Identification of outlier data values on individual homogeneous segments using quantiles of normal distribution.
The function is called by KRDetect.outliers.changepoint
and is not intended for use by regular users of the package.
normal.distr.quantiles.detect(x, cp.segment, alpha.default)
x |
a numeric vector of data. |
cp.segment |
an integer membership vector for individual segments. |
alpha.default |
a numeric value from interval (0,1) of alpha parameter determining the criterion for outlier detection:
the limits for outlier observations on individual segments are set as +/- (alpha/2-quantile of normal distribution with parameters corresponding to data on studied segment) * (sample standard deviation of data on corresponding segment)
If |
This function detects outlier observations on individual segments using quantiles of normal distribution. The function is exported for developer use only. It does not perform any checks on inputs since it is only convenience function for identification of outlier residuals.
A list is returned with elements:
alpha |
a numeric vector of alpha parameters used for outlier identification on individual segments |
outlier |
a logical vector specyfing the identified outliers, |
Campulova M, Michalek J, Mikuska P, Bokal D (2018). Nonparametric algorithm for identification of outliers in environmental data. Journal of Chemometrics, 32, 453-463.