entirelogprofilelikelihood {timedelay}

Calculating the entire profilel likelihood curve over the given grid values of the time delay

Description

entirelogprofilelikelihood calculates the entire profilel likelihood curve over the given grid values of the time delay.

Usage

entirelogprofilelikelihood(data.lcA, data.lcB, grid, 
                                  initial, data.flux, 
                                  delta.uniform.range, micro)

Arguments

data.lcA

A (n_1 by 3) matrix for image A (light curve A); the first column has n_1 observation times, the second column contains n_1 flux (or magnitude) values, the third column includes n_1 measurement errors.

data.lcB

A (n_2 by 3) matrix for image B (light curve B); the first column has n_2 observation times, the second column contains n_2 flux (or magnitude) values, the third column includes n_2 measurement errors.

grid

A vector containing values of the time delay on which the profile likelihood values are calculated. We recommend using the grid interval equal to 0.1.

initial

The initial values of the other model parameters (mu, log(sigma), log(tau), beta). We take log on sigma and tau for numerical stability.

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.

Details

The function entirelogprofilelikelihood is used to obtain the entire profile likelihood curve over the given grid values of the time delay.

Value

The outcome of entirelogprofilelikelihood is the values of the log profile likelihood function over the given grid values of the time delay.

Author(s)

Hyungsuk Tak

References

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.

Examples

  # Loading datasets
  data(simple)
  head(simple)

  ################################################
  # Time delay estimation via profile likelihood #
  ################################################


  # 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)

  ###### 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,
  # but users can set a finer grid of values of the time delay.

  
  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, ")"))))