sampler {RobPer}

Generator for irregularly sampled observation times

Description

Generates irregularly sampled observation times with a periodic sampling pattern

Usage

sampler(ttype, npoints, ncycles, ps = 1)

Arguments

ttype

character string: Specifying the sampling pattern. Possible options: "equi" and "unif" for unperiodic sampling, "sine" and "trian" for sampling with a periodic density (see Details).

npoints

integer: Sample size n (see Details).

ncycles

integer: Number of sampling cycles n_s (see Details).

ps

positive numeric value: Sampling period p_s (see Details).

Details

sampler generates observation times t_1,\ldots,t_n with a periodic sampling of period p_s. Four distributions are possible: In case of ttype="equi", the t_i are equidistantly sampled with t_i=i\frac{p_sn_s}{n}. For ttype="unif", the observation times are independently drawn form a uniform distribution on [0,n_sp_s]. Both these sampling schemes are aperiodic, the sampling period p_s only influences the length t_n-t_1 of the series of observation times.

For ttype="sine" and ttype="trian", observation cycles z^\star_i are drawn from a uniform distribution on \{1,\ldots,n_s\} and observation phases \varphi^\star_i are drawn from a density

d_{sine}(x)= \sin(2\pi x)+1

(for ttype="sine") or

d_{trian}(x)= 3x, \quad 0\leq x\leq\frac{2}{3},

d_{trian}(x)= 6-6x, \quad \frac{2}{3}<x\leq 1

(for ttype="trian"). The unsorted observation times t^\star_i are then generated using

t^\star_i= \varphi^\star_i+(z^\star_i-1)p_s.

Separately sampling observation cycle and phase was proposed by Hall and Yin (2003). For more details see Thieler, Fried and Rathjens (2016) or Thieler et al. (2013).

Value

numeric vector: Ordered observation times.

Note

To sample from d_{sine}, the function BBsolve, package BB, is used.

A former version of this function is used in Thieler et al. (2013).

Author(s)

Anita M. Thieler and Jonathan Rathjens

References

Hall, P. and Yin, J. (2003): Nonparametric Methods for Deconvolving Multiperiodic Functions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65 (4), 869-886

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 an example).