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)?
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
obtained from slice_clouds
keep_parameters
is TRUE)
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.
slice_clouds
for producing the input for this function. See plot.climate_histories
and summary.climate_histories
for plotting and summary details