Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/gavinsimpson/inqua23

Slides and code for my session keynote at INQUA 2023
https://github.com/gavinsimpson/inqua23

Last synced: 23 days ago
JSON representation

Slides and code for my session keynote at INQUA 2023

Awesome Lists containing this project

README 

        
# Applying statistical thinking to palaeo data through GAMs



This repo contains the slide deck and R code for my session keynote at INQUA 2023 in Roma.



[![DOI](https://zenodo.org/badge/665432680.svg)](https://zenodo.org/badge/latestdoi/665432680)



Rendered slides: [HTML Slide Deck](https://gavinsimpson.github.io/inqua23/slides/)



### Gavin Simpson



### Department of Animal and Veterinary Sciences, Aarhus University, Denmark



#### Abstract



Palaeoecological and palaeoenvironmental stratigraphic data exhibit a number of

features that make them complex to analyse; for example they



1. are autocorrelated in time (and, potentially, also in space),

2. are generally irregularly spaced in time, and

3. have complex variance properties due to time averaging and sampling

processes.



This complexity has some unfortunate consequences. All too often, data are not

subject to any form of statistical analysis, and, when they are, often that

analysis uses inappropriate statistical methods. Furthermore, analysts will

often invent their own *ad hoc* approaches to get answers to their specific

questions. The statistical properties of such *ad hoc* approaches are rarely

known, however.



A number statistical methods have been developed over recent years and decades

that can handle the complex needs of palaeo data. Among these methods is the

generalized additive model (GAM) and related extensions. The use of GAMs to

model temporal trends in palaeo data has increased steadily in recent years,

thanks, in part, to the availability of high quality, open source software like

the R package mgcv. GAMs are relatively simple models that will be familiar to

anyone who has taken a graduate course in ecological statistics, and yet GAMs

are incredibly versatile models that can be adapted to suit many pressing

questions that palaeo scientists wish to address.



In this talk I will briefly introduce GAMs and show how penalised splines, which underpin the GAM, work, before moving on to cover some recent developments from

my own work that extend GAMs to new areas and questions. In particular, I will

discuss;



i. models that go beyond the mean to investigate trends in other moments, such

as variances of palaeo time series,

ii. using derivatives of splines fitted in a GAM as estimates of rates of

change in palaeo time series, and

iii. copula GAMs that can estimate how the correlation between two variables

itself changes over time.



In each case I present a relevant motivating example and supplement the talk

with fully worked examples in R that are available on GitHub.



#### Citation



Please cite as:



Simpson, Gavin L. (2023, July 13-20) _Applying statistical thinking to palaeo data through GAMs_ [Session keynote]. XXI Congress of the International Union for Quaternary Research (INQUA), Rome, Italy. [10.5281/zenodo.8186141](https://doi.org/10.5281/zenodo.8186141)