normal.distr.quantiles.detect {envoutliers}

Normal distribution based identification of outliers on segments - Only intended for developer use

Description

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.

Usage

normal.distr.quantiles.detect(x, cp.segment, alpha.default)

Arguments

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 alpha.default = NULL, its value on individual segments is estimated using Modified Algorithm A1 (Campulova et al., 2018).

Details

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.

Value

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, TRUE means that corresponding data value from vector x is detected as an outlier

References

Campulova M, Michalek J, Mikuska P, Bokal D (2018). Nonparametric algorithm for identification of outliers in environmental data. Journal of Chemometrics, 32, 453-463.