Description Usage Arguments Details Value Author(s) References Examples

View source: R/entirelogprofilelikelihood.R

`entirelogprofilelikelihood`

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

1 2 3 | ```
entirelogprofilelikelihood(data.lcA, data.lcB, grid,
initial, data.flux,
delta.uniform.range, micro)
``` |

`data.lcA` |
A ( |

`data.lcB` |
A ( |

`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. |

The function `entirelogprofilelikelihood`

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

The outcome of `entirelogprofilelikelihood`

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

Hyungsuk Tak

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
# 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, ")"))))
``` |

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