Description Usage Arguments Value Examples

This function is mostly just a wrapper function which calls the functions necessary to impute missing data, run the changepoint Gibbs algorithms, and select MAP estimates of the changepoint locations. Much less output is given for this function than for the functions called by this function. If all goes well, one should only need to explicitly use this function to estimate groove locations. Note that because this function calls the functions which do the Gibbs sampling, all of the input required for those functions is required by this function.

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`data` |
Data frame with columns "x" and "y." "x" is a column of the locations of the observed residual values, y. |

`iter` |
Number of iterations after warmup. |

`start.vals` |
Starting values for the changepoint algorithm. Either NA valued or a named list of lists. If list, the names of the lists should be "cp2","cp1", and "cp0". Each list posessing one of those aforementioned names is a list of starting values identical to what would be given if the changepoint algorithm were to be run with the corresponding number of specified changepoints. List with elements "sigma", "l", "cp", "beta", and "intercept." "sigma" and "l" are 3 element vectors where the first element is for the data on the left groove. The second element is for the land engraved area, and the third element is for the right groove. "cp" is the vector of changepoint starting values. "beta" and "intercept" are two element vectors of the slope and intercept for the left and right groove engraved area respectively. If NA, default starting values will be used. Note that the changepoint starting values should always be near the edges of the data. |

`prop_var` |
Either NA valued or a list of named lists. If list, the names of the lists should be "cp2","cp1", and "cp0". Each list posessing one of those aforementioned names is a list of proposal covariance matrices identical to what would be given if the changepoint algorithm were to be run with the corresponding number of specified changepoints. |

`cp_prop_var` |
The proposal variance-covariance matrix for the changepoints. Can either be NA or a named list. If list, the names of the list items should be "cp2", "cp1" where each is the appropriate proposal variance/covariance matrix for the number of changepoints. |

`tol_edge` |
This parameter controls how close changepoint proposals can be to the edge of the data before getting automatically rejected. For example, a value of 10 means that the changepoint will be automatically rejected if either of the proposal changepoints is within a distance of 10 x-values from either edge. |

`tol_cp` |
This parameter controls how close changepoint proposals can be to each other before getting automatically rejected. For example, a value of 10 means that the changepoint will be automatically rejected if either of the proposal changepoints is within a distance of 10 x-values from either each other. |

`warmup` |
The number of warmup iterations. This should be set to a very small number of iterations, as using too many iterations as warmup risks moving past the changepoints and getting stuck in a local mode. Default is set to 500. |

`verbose` |
Logical value indicating whether to print the iteration number and the parameter proposals. |

`prior_numcp` |
This is a vector with four elements giving the prior probabilities for the zero changepoint model, the one changepoint on the left model, the one changepoint on the right model, and the two changepoint model, in that order. Note that, practically, because the likelihood values are so large, only very strong priors will influence the results. |

`est_impute_par` |
Logical value indicating whether parameters for the Gaussian process imputation should be estimated before actually doing the imputation. Default is FALSE, in which case the default imputation standard deviation is 0.8 and the length scale is 15. The covariance function is a squared exponential. These values have worked well in testing. |

`impute_par` |
A two element vector containing the standard deviation and length scale (in that order) to use for the Gaussian process imputation. These values will not be used if the est_impute_par argument is set to TRUE. |

A named list containing the output from variable_cp_gibbs function, the range of data that was actually used for the changepoint algorithm (since it doesn't impute values past the outermost non-missing values), and the estimated groove locations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# Fake data
sim_groove <- function(beta = c(-0.28,0.28), a = 125)
{
x <- seq(from = 0, to = 2158, by = 20)
med <- median(x)
y <- 1*(x <= a)*(beta[1]*(x - med) - beta[1]*(a - med)) +
1*(x >= 2158 - a)*(beta[2]*(x - med) - beta[2]*(2158 - a - med))
return(data.frame("x" = x, "y" = y))
}
fake_groove <- sim_groove()
cp_gibbs2 <- detect_cp(data = fake_groove,
verbose = FALSE,
tol_edge = 50,
tol_cp = 1000,
iter = 300,
warmup = 100,
est_impute_par = FALSE)
``` |

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