reslr: quick start guide

In reslr: Modelling Relative Sea Level Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"#,
  #fig.path = "qs_vig/"
)
options(rmarkdown.html_vignette.check_title = FALSE)

Step 1: install reslr

Use:

# Not on CRAN yet
#install.packages("reslr")
#devtools::install_github("maeveupton/reslr")
install_github("maeveupton/reslr")

then,

library(reslr)

Note: The JAGS software is a requirement for this instruction sheet and refer back to main vignettes for more information.

Step 2: load in the data into reslr

There is a large example dataset included in the reslr package called NAACproxydata. In this example, we demonstrate how to include proxy record data which is stored in a csv file. This csv file of data can be found in the package and the readr function reads the csv file:

path_to_data <- system.file("extdata", "one_data_site_ex.csv", package = "reslr")
example_one_datasite <- read.csv(path_to_data)

Using the reslr_load function to read in the data into the reslr package:

example_one_site_input <- reslr_load(
  data = example_one_datasite)

Step 3: plot the data

plot(
  x = example_one_site_input,
  title = "Plot of the raw data",
  xlab = "Year (CE)",
  ylab = "Relative Sea Level (m)",
  plot_tide_gauges = FALSE,
  plot_caption = TRUE
)

Step 4: Run your statistical model and check convergence

Select your modelling technique from the modelling options available:

| Statistical Model | Model Information | model_type code | | ---- | ------- | -- | | Errors in variables simple linear regression | A straight line of best fit taking account of any age and measurement errors in the RSL values using the method of Cahill et al (2015). Use for single proxy site. | "eiv_slr_t" | | Errors in variables change point model | An extension of the linear regression modelling process. It uses piece-wise linear sections and estimates where/when trend changes occur in the data (Cahill et al. 2015). | "eiv_cp_t" | | Errors in variables integrated Gaussian Process | A non linear fit that utilities a Gaussian process prior on the rate of sea-level change that is then integrated (Cahill et al. 2015). | "eiv_igp_t" | | Noisy Input spline in time | A non-linear fit using regression splines using the method of Upton et al (2023). | "ni_spline_t" | | Noisy Input spline in space and time | A non-linear fit for a set of sites across a region using the method of Upton et al (2023). | "ni_spline_st"| | Noisy Input Generalised Additive model for the decomposition of the RSL signal | A non-linear fit for a set of sites across a region and provides a decomposition of the signal into regional, local-linear (commonly GIA) and local non-linear components. Again this full model is as described in Upton et al (2023). | "ni_gam_decomp" |

For this example, it is a single site and we are interested in how it varies over time select the Noisy Input spline in time. If it was multiple sites, we recommend using a spatial temporal model, i.e. Noisy Input spline in space and time, or for decomposing the signal, i.e. Noisy Input Generalised Additive model.

Once the model is chosen use the reslr_mcmc function to run it:

res_one_site_example <- reslr_mcmc(
  input_data = example_one_site_input,
  model_type = "ni_spline_t",
  CI = 0.95
)

The convergence of the algorithm is examined and he parameter estimates from the model can be investigated using the following:

summary(res_one_site_example)

Step 5: Plot the results

The model fit results can be visualised using the following function:

plot(res_one_site_example,
  xlab = "Year (CE)",
  ylab = "Relative Sea Level (m)",
  plot_type = "model_fit_plot"
)

For the rate of change plot use:

plot(res_one_site_example,
  plot_type = "rate_plot"
)

To examine the data creating these plots the user types the following:

output_dataframes <- res_one_site_example$output_dataframes
head(output_dataframes)

To examine the additional options in the reslr package, see the main vignette.

reslr documentation built on July 9, 2023, 7:54 p.m.