disturber {RobPer}

Disturbing light curve data

Description

Disturbes a light curve replacing measurement accuracies by outliers and/or observed values by atypical values. See RobPer-package for more information about light curves.

Usage

disturber(tt, y, s, ps, s.outlier.fraction = 0, interval)

Arguments

tt

numeric vector: Observation times t_1,\ldots,t_n (see Details).

y

numeric vector: Observed values y_1,\ldots,y_n (see Details).

s

numeric vector: Measurement accuracies s_1,\ldots,s_n (see Details).

ps

positive value: Sampling period p_s indirectly defines the length of the time interval, in which observed values y_i are replaced by atypical values (see Details).

s.outlier.fraction

numeric value in [0,1]: Defines the proportion of measurement accuracies that is replaced by outliers (see Details). A value of 0 means that no measurement accuracy is replaced by an outlier.

interval

logical: If TRUE, the observed values belonging to a random time interval of length 3p_s are replaced by atypical values (see Details). If TRUE and the light curve is shorter than 3p_s, the function will stop with an error message.

Details

This function disturbes the light curve (t_i,y_i,s_i)_{i=1,\ldots,n} given. It randomly chooses a proportion of s.outlier.fraction measurement accuracies s_i and replaces them by 0.5\min(s_1,\ldots,s_n). In case of interval=TRUE a time interval [t_{start},t_{start}+3p_s] within the intervall [t_1,t_n] is randomly chosen and all observed values belonging to this time interval are replaced by a peak function:

y_i^{changed} = 6 \ \tilde y_{0.9}\ \frac{d_{\mathcal N(t_{start}+1.5p_s, p_s^2)}(t_i) }{ d_{\mathcal N(0,p_s^2)}(0)} \quad \forall \ i \ : \ t_i\in[t_{start}, t_{start}+3p_s],

where d_{\mathcal N(a,b^2)}(x) denotes the density of a normal distribution with mean a and variance b^2 at x.

In case of s.outlier.fraction=0 and interval=FALSE, y and s are returned unchanged.

Value

y

numeric vector: New y_i-values, partly different from the old ones if interval=TRUE (see Details).

s

numeric vector: New s_i-values, partly different from the old ones if s.outlier.fraction>0 (see Details).

Note

A former version of this function is used in Thieler et al. (2013). See also Thieler, Fried and Rathjens (2016).

Author(s)

Anita M. Thieler

References

Thieler, A. M., Backes, M., Fried, R. and Rhode, W. (2013): Periodicity Detection in Irregularly Sampled Light Curves by Robust Regression and Outlier Detection. Statistical Analysis and Data Mining, 6 (1), 73-89

Thieler, A. M., Fried, R. and Rathjens, J. (2016): RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression. Journal of Statistical Software, 69 (9), 1-36, <doi:10.18637/jss.v069.i09>

See Also

Applied in tsgen (see there for example).