ctbi.outlier {ctbi} | R Documentation |
ctbi.outlier
Description
Please cite the following companion paper if you're using the ctbi
package: Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349–361, https://doi.org/10.5194/hess-27-349-2023, 2023.
Outliers in an univariate dataset y
are flagged using an enhanced box plot rule (called Logbox, input: coeff.outlier
) that is adapted to non-Gaussian data and keeps the type I error at \frac{0.1}{\sqrt{n}}
% (percentage of erroneously flagged outliers).
The box plot rule flags data points as outliers if they are below L
or above U
using the sample quantile q
:
L = q(0.25)-\alpha \times (q(0.75)- q(0.25))
U = q(0.75)+\alpha \times (q(0.75)- q(0.25))
Logbox replaces the original \alpha = 1.5
constant of the box plot rule with \alpha = A \times \log(n)+B+\frac{C}{n}
. The variable n \geq 9
is the sample size, C = 36
corrects biases emerging in small samples, and A
and B
are automatically calculated on a predictor of the maximum tail weight defined as m_{*} = \max(m_{-},m_{+})-0.6165
.
The two functions (m_{-}
,m_{+}
) are defined as:
m_{-} = \frac{q(0.875)- q(0.625)}{q(0.75)- q(0.25)}
m_{+} = \frac{q(0.375)- q(0.125)}{q(0.75)- q(0.25)}
And finally, A = f_{A}(
m_{*}
)
and B = f_{B}(
m_{*}
)
with m_{*}
restricted to [0,2]. The functions (f_{A},f_{B})
are defined as:
f_{A}(x) = 0.2294\exp(2.9416x-0.0512x^{2}-0.0684x^{3})
f_{B}(x) = 1.0585+15.6960x-17.3618x^{2}+28.3511x^{3}-11.4726x^{4}
Both functions have been calibrated on the Generalized Extreme Value and Pearson families.
Usage
ctbi.outlier(y, coeff.outlier = "auto")
Arguments
y |
univariate data (numeric vector) |
coeff.outlier |
one of |
Value
A list that contains:
xy, a two columns data frame that contains the clean data (first column) and the outliers (second column)
summary.outlier, a vector that contains A, B, C, m_{*}
, the size of the residuals (n), and the lower and upper outlier threshold
Examples
x <- runif(30)
x[c(5,10,20)] <- c(-10,15,30)
example1 <- ctbi.outlier(x)