# 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

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

### Arguments

`data` |
The data set |

`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. The feasible entire support is c(min(simple[, 1]) - max(simple[, 1]), max(simple[, 1]) - min(simple[, 1])). |

`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 (in progress). Bayesian and Profile Likelihood Approaches to Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars

### Examples

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 | ```
# Loading datasets
data(simple)
head(simple)
################################################
# Time delay estimation via profile likelihood #
################################################
###### The entire profile likelihood values on the grid of values of the time delay.
# Cubic microlensing model
ti1 <- simple[, 1]
ti2 <- simple[, 1]^2
ti3 <- simple[, 1]^3
ss <- lm(simple[, 4] - simple[, 2]~ ti1 + ti2 + ti3)
initial <- c(mean(simple[, 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.
### 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 = simple, 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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