Description Usage Arguments Details Value References See Also

This function takes a set of pollen data and turns it slice-by-slice into climate estimates. 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 | ```
slice_clouds(pollen, path_to_rs = "https://maths.ucd.ie/parnell/",
n_samples = 1000)
``` |

`pollen` |
A matrix or data frame of pollen counts (they can be normalised or not) which contains an unspecified number of rows and precisely 28 columns. These columns should represent counts of the following taxa in order: Abies Alnus Betula Carpinus Castanea Cedrus Corylus Ephedra Fagus Juniperus Larix Olea Ostrya Phillyrea Picea Pinus.D Pinus.H Pistacia Quercus.D Quercus.E Salix Tilia Ulmus Artemisia Chenopodiaceae Cyperaceae Ericales Gramineae |

`path_to_rs` |
A web address which links to the file |

`n_samples` |
The number of samples taken for each slice cloud. Default is 1000 |

A slice cloud is a multivariate probability distribution of the three climate dimensions (Growing Degree Days above 5C, GDD5; Mean Temperature of Coldest Month, MTCO; the ratio of actual to potential evapotranspiration, AET/PET) given the pollen information at that slice only. This function loops through each slice in the core to produce slice clouds which represent the information about climate obtained only from that slice of pollen. See references below for the technical details of this technique

A list object the the following elements

slice_cloudsThe slice clouds, an n_samples x n_slices x n_dimensions array

n_samplesThe number of slices (i.e. the number of rows in the pollen file)

n_dimensionsThe number of climate dimensions (currently always 3)

Fore more detail on the algorithm see: Salter-Townshend, M. and J. Haslett (2012). Fast Inversion of a Flexible Regression Model for Multivariate, Zero-Inflated Pollen Counts. Environmetrics. Sweeney, J. (2012). Advances in Bayesian Model Development and Inversion in Multivariate Inverse Inference Problems with application to palaeoclimate reconstruction. Ph. D. thesis, Trinity College Dublin. 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.

`climate_histories`

, `plot.slice_clouds`

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