Generates measurement accuracies, a white noise component depending on them and a second (possibly power law, i.e. red) noise component which does not depend on the measurement accuracies.
For more details see tsgen or Thieler, Fried and Rathjens (2016).
See RobPer-package for more information about light curves.
lc_noise(tt, sig, SNR, redpart, alpha = 1.5)numeric vector: Observation times given.
numeric vector of same length as tt: A given signal to which the noise will be added.
positive number: Defines the relation between signal and noise (see tsgen for Details).
numeric value in [0,1]: Proportion of the power law noise in noise components (see tsgen for Details).
numeric value: Power law index for the power law noise component (see tsgen for Details).
numeric vector: Observed values: signal + noise.
numeric vector: Measurement accuracies related to the white noise component.
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>
Applied in tsgen (see there for an example), applies TK95_uneq.