Description Details Author(s) References Examples

The R package timedelay provides a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropoli-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to be implemented. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands.

Package: | timedelay |

Type: | Package |

Version: | 1.0.11 |

Date: | 2020-05-18 |

License: | GPL-2 |

Main functions: | `bayesian` , `bayesian.multiband` , `entirelogprofilelikelihood` |

Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, Xiao-Li Meng, and Aneta Siemiginowska

Maintainer: Hyungsuk Tak <hyungsuk.tak@gmail.com>

Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, Xiao-Li Meng, and Aneta Siemiginowska (2017). "Bayesian Estimates of Astronomical Time Delays between Gravitationally Lensed Stochastic Light Curves," The Annals of Applied Statistics, 11 (3), 1309-1348. Hyungsuk Tak, Xiao-Li Meng, and David A. van Dyk (2018), "A Repelling-Attracting Metropolis Algorithm for Multimodality", Journal of Computational and Graphical Statistics, 27 (3), 479-490. Zhirui Hu and Hyungsuk Tak (2020+), "Modeling Stochastic Variability in Multi-Band Time Series Data," arXiv:2005.08049.

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# Loading datasets
data(simple)
head(simple)
# Subset (data for image A) of the typical quasar data set
lcA <- simple[, 1 : 3]
# Another subset (data for image B) of the typical quasar data set
# The observation times for image B are not necessarily the same as those for image A
lcB <- simple[, c(1, 4, 5)]
# The two subsets do not need to have the same number of observations
# For example, here we add one more observation time for image B
lcB <- rbind(lcB, c(290, 1.86, 0.006))
dim(lcA)
dim(lcB)
###############################################
# Time delay estimation via Bayesian approach #
###############################################
# Cubic microlensing model (m = 3)
output <- bayesian(data.lcA = lcA, data.lcB = lcB,
data.flux = FALSE, theta.ini = c(0, 0.01, 200),
delta.ini = 50, delta.uniform.range = c(0, 100),
delta.proposal.scale = 1,
tau.proposal.scale = 3,
tau.prior.shape = 1, tau.prior.scale = 1,
sigma.prior.shape = 1, sigma.prior.scale = 2 / 10^7,
asis = TRUE, micro = 3,
sample.size = 100, warmingup.size = 50)
names(output)
# hist(output$delta)
# plot(output$delta, type = "l")
# acf(output$delta)
### Argument description
# data.lcA: An n1 by three matrix for image A (light curve A) with
# n1 observation times in the first column,
# n1 observed magnitudes (or in flux) in the second column,
# n1 measurement errors for image A in the third column.
# data.lcB: An n2 by three matrix for image B (light curve B) with
# n2 observation times in the first column,
# n2 observed magnitudes (or in flux) in the second column,
# n2 measurement errors for image A in the third column.
# data.flux: "True" if data are recorded on flux scale or "FALSE" if data are on magnitude scale.
# If your observed time series can take on negative values, then data.flux = FALSE.
# theta.ini: Initial values of theta = (mu, sigma, tau) for MCMC.
# delta.ini: Initial values of the time delay for MCMC.
# delta.uniform.range: The range of the Uniform prior distribution for the time delay.
# The feasible entire support is
# c(min(simple[, 1]) - max(simple[, 1]), max(simple[, 1]) - min(simple[, 1]))
# delta.proposal.scale: The proposal scale of the Metropolis step for the time delay.
# tau.proposal.scale: The proposal scale of the Metropolis-Hastings step for tau.
# tau.prior.shape: The shape parameter of the Inverse-Gamma hyper-prior distribution for tau.
# tau.prior.scale: The scale parameter of the Inverse-Gamma hyper-prior distribution for tau.
# sigma.prior.shape: The shape parameter of
# the Inverse-Gamma hyper-prior distribution for sigma^2.
# sigma.prior.scale: The scale parameter of
# the Inverse-Gamma hyper-prior distribution for sigma^2.
# micro: It determines the order of a polynomial regression model that accounts
# for the difference between microlensing trends. Default is 3.
# When zero is assigned, the Bayesian model fits a curve-shifted model.
# asis: "TRUE" if we use the ancillarity-sufficiency interweaving strategy (ASIS)
# for c (always recommended)
# multimodality: "TRUE" if we use the repelling-attracting Metropolis algorithm
# for sampling Delta in the presence of multimodality.
# Please make sure that "adaptive.delta = FALSE" so that
# adaptive MCMC for Delta is not used in the presence of multimodality.
# adaptive.freqeuncy: The adaptive MCMC is applied for every specified frequency.
# If it is specified as 100,
# the adaptive MCMC is applied to every 100th iterstion.
# adaptive.delta: "TRUE" if we use the adaptive MCMC for the time delay.
# adaptive.delta.factor: The factor, exp(adaptive.delta.factor) or exp(-adaptive.delta.factor),
# multiplied to the proposal scale of the time delay for adaptive MCMC.
# Default is 0.01.
# adaptive.tau: "TRUE" if we use the adaptive MCMC for tau.
# adaptive.tau.factor: The factor, exp(adaptive.tau.factor) or exp(-adaptive.tau.factor),
# multiplied to the proposal scale of tau for adaptive MCMC.
# sample.size: The number of posterior samples for each parameter
# warmingup.size: The number of burn-ins
# Type ?bayesian for further details on graphical model checking.
################################################
# Time delay estimation via profile likelihood #
################################################
###### The entire profile likelihood values on the grid of values of the time delay.
# Cubic microlensing model
ti1 <- lcB[, 1]
ti2 <- lcB[, 1]^2
ti3 <- lcB[, 1]^3
ss <- lm(lcB[, 2] - mean(lcA[, 2]) ~ ti1 + ti2 + ti3)
initial <- c(mean(lcA[, 2]), log(0.01), log(200), ss$coefficients)
delta.uniform.range <- c(0, 100)
grid <- seq(0, 100, by = 0.1)
# grid interval "by = 0.1" is recommended for accuracy,
# but users can set a finer grid of values of the time delay.
### Running the following codes takes more time than CRAN policy
### Please type the following lines without "#" to run the function and to see the results
# logprof <- entirelogprofilelikelihood(data.lcA = lcA, data.lcB = lcB, grid = grid,
# initial = initial, data.flux = FALSE,
# delta.uniform.range = delta.uniform.range, micro = 3)
# plot(grid, logprof, type = "l",
# xlab = expression(bold(Delta)),
# ylab = expression(bold(paste("log L"[prof], "(", Delta, ")"))))
# prof <- exp(logprof - max(logprof)) # normalization
# plot(grid, prof, type = "l",
# xlab = expression(bold(Delta)),
# ylab = expression(bold(paste("L"[prof], "(", Delta, ")"))))
### Argument description
# data.lcA: The data set (n by 3 matrix) for light curve A
# (1st column: observation times, 2nd column: values of fluxes or magnitudes,
# 3rd column: measurement errors)
# data.lcB: The data set (w by 3 matrix) for light curve B
# (1st column: observation times, 2nd column: values of fluxes or magnitudes,
# 3rd column: measurement errors)
# grid: the vector of grid values of the time delay
# on which the log profile likelihood values are calculated.
# initial: The initial values of the other model parameters (mu, log(sigma), log(tau), beta)
# data.flux: "True" if data are recorded on flux scale or "FALSE" if data are on magnitude scale.
# delta.uniform.range: The range of the Uniform prior distribution for the time delay.
# micro: It determines the order of a polynomial regression model that accounts
# for the difference between microlensing trends. Default is 3.
# When zero is assigned, the Bayesian model fits a curve-shifted model.
``` |

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