Description Usage Arguments Details Value References See Also

Runs a number of algorithms to create climate histories for a given set of slice clouds (from `slice_clouds`

and a set of chronologies. For examples why not see the wonderful Bclim vignette (available at https://cran.r-project.org/web/packages/Bclim/index.html) and the author's personal webpage (https://maths.ucd.ie/parnell)?

1 2 3 4 5 6 7 8 9 10 11 | ```
climate_histories(slice_clouds, chronology, time_grid, n_mix = 10,
mix_warnings = FALSE, n_chron = 2000, keep_parameters = TRUE,
control_mcmc = list(iterations = 1e+05, burnin = 20000, thinby = 40, report
= 100), control_chains = list(v_mh_sd = 2, phi1_mh_sd = 1, phi2_mh_sd = 10,
v_start = statmod::rinvgauss(slice_clouds$n_slices - 1, 2, 1), Z_start =
sample(1:n_mix, slice_clouds$n_slices, replace = TRUE), phi1_start = rep(3,
slice_clouds$n_dimensions), phi2_start = rep(20, slice_clouds$n_dimensions)),
control_priors = list(phi1_dl_mean = rep(1.275, slice_clouds$n_dimensions),
phi1_dl_sd = rep(0.076, slice_clouds$n_dimensions), phi2_dl_mean = rep(4.231,
slice_clouds$n_dimensions), phi2dl_sd = rep(0.271,
slice_clouds$n_dimensions)))
``` |

`slice_clouds` |
An object of class |

`chronology` |
A set of chronologies given as a matrix. These should be provided in thousands of years before present. See details below |

`time_grid` |
The time grid on which to create the climate histories |

`n_mix` |
The number of mixture components for the Mclust mixture algorithm |

`mix_warnings` |
Whether to display warnings related to the mixture algorithm |

`n_chron` |
The number of chronologies to use |

`keep_parameters` |
Whether to keep latent parameters or not. Useful for convergence checking so default is TRUE |

`control_mcmc` |
A list containing elements that control the MCMC, including the number of iterations, the size of the burn-in period, the amount to thinby, and how often for the algorithm to report its progress |

`control_chains` |
A list containing elements that control the starting values of the parameters (v_start, Z_start, phi1_start and phi2_start) and the Metropolis-Hastings proposal standard deviation for v, phi1 and phi2 |

`control_priors` |
A list containing the prior parameters for the volatilities, given by phi1 and phi2, both of which should be the log-mean and log-sd of the log-normal distribution. The values provided here are for the GISP2 ice core for the period 0 to 10k years BP |

This function takes the slice_clouds produced by `slice_clouds`

uses a set of algorithms to produce climate histories on the provided time grid. The full details are in the paper referenced below. The options listed above allow quite a detailed level of control over the behaviour of the algorithm, and convergence should be checked using suitable means (see e.g. the R package boa or coda).

One of the key inputs to this function is a chronology. This should be a matrix of n_chron by n_slices containing sample chronologies as produced by, e.g. the R package Bchron. These are used by the `climate_histories`

function to take account of chronological uncertainty. In the (unlikely) event that there is no chronological uncertainty, the rows of the chronologies can be identical.

A list object with the following elements

v.store Samples of the posterior estimated volatilities

chron.store Samples of the used chronologies

c.store Samples of the posterior estimated climates

z.store Samples of the posterior mixture indices

phi1 Values used for the IG prior on v for each climate dimension

phi2 Values used for the IG prior on v for each climate dimension

chron.loc A character string giving the location of the chronology file

nchron The number of chronologies in the chronology file

parameters A list containing further latent parameter values for convergence checking (only if

`keep_parameters`

is TRUE)

Parnell, A. C., et al. (2015), Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64: 115–138.

`slice_clouds`

for producing the input for this function. See `plot.climate_histories`

and `summary.climate_histories`

for plotting and summary details

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