# reslr: advanced

#### Maeve Upton, Andrew Parnell and Niamh Cahill

#### 2023-06-14

## Introduction

In this document we present a range of advanced options that are available in the reslr package. To examine the all other options in the `reslr` package, see the main vignette.

If you have created other examples demonstrating `reslr` capabilities yourself, please contact me so I can include them here for other people to see.

Installing `reslr` and loading it with:

    #install.packages("reslr")
    #devtools::install_github("maeveupton/reslr",force = TRUE,INSTALL_opts = '--no-lock')
    library(reslr)

## Errors in Variables Integrated Gaussian Process with detrended data

The EIV Integrated Gaussian Process model provides the underlying rate of the process directly from the model. If the user requires a model applied to data that has been de-trended this is available in our package for a single site using the `model_type = "eiv_igp_t"`. The data is de-trended using the linear rate which can be provided by the user within the input dataframe along with the uncertainty associated with the rate. Or the linear rate is calculated within the package using the data and this rate is used to de-trend the data. In addition, the user will need to provide the estimated year of core collection.

This technique focuses on 1 site and we do not recommended for multiple proxy sites together. Tide gauge data can be included, however, the user must investigate which tide gauge is suitable and the tide gauge data will use the same linear_rate as the proxy records. As an example, we will filter the example dataset `NAACproxydata` to select one site to demonstrate the process:

    # For 1 site
    CedarIslandNC <- NAACproxydata[NAACproxydata$Site == "Cedar Island",]

Step 1: Load in the data using the `reslr_load` function:

    CedarIslandNC_input_detrend <- reslr_load(
      data = CedarIslandNC,
      include_tide_gauge = FALSE,
      include_linear_rate = TRUE,
      TG_minimum_dist_proxy = FALSE,
      list_preferred_TGs = NULL,
      all_TG_1deg = FALSE,
      prediction_grid_res = 50,
      sediment_average_TG = 10,
      detrend_data = TRUE,
      core_col_year = 2010
    )

For this case, we update the setting `detrend_data = TRUE` and we provide the `core_col_year = 2010` corresponding to the year of the core collection. The linear_rate is required and is included by setting `include_linear_rate = TRUE`.

The output of this function is a list of two dataframes called `data` and `data_grid`. - The `data` dataframe is the inputted data with additional columns for the data_type_id which will contain “ProxyRecord”. It can be accessed by:

    data <- CedarIslandNC_input_detrend$data

- The `data_grid` is a dataframe that is evenly spaced in time based on the `prediction_grid_res` value chosen by the user and is used to create the plots and is accessed by

    data_grid <- CedarIslandNC_input_detrend$data_grid

Step 1a: A brief insight into the outputs of the `reslr_input` function can be obtained using:

    print(CedarIslandNC_input_detrend)
    #> This is a valid reslr input object with 104 observations and  1 site(s).
    #> There are  1  proxy site(s) and  0  tide gauge site(s).
    #> The age units are; Common Era.
    #> Decadally averaged tide gauge data was not included. It is recommended for the ni_gam_decomp model
    #> The linear_rate and linear_rate_err has been included.
    #> Data has been detrended.

Step 2: Plotting the data the raw data with:

    plot(
      x = CedarIslandNC_input_detrend,
      title = "Plot of the raw detrended data",
      xlab = "Year (CE)",
      ylab = "Sea Level (m)",
      plot_proxy_records = TRUE,
      plot_tide_gauges = FALSE
    )

This will produce a plot of Age on the x-axis and Sea Level on the y-axis in meters. Grey boxes represent the uncertainty associated with the vertical and horizontal uncertainty. The black data points are the midpoints of these uncertainty boxes. The extra arguments can be used which allows the user to updated the titles and axis labels. The caption `plot_caption`, included by default, provides the number of proxy sites and tide gauge sites that will be used in the model and can be removed if required `plot_caption = FALSE`. In addition, the user can select to plot the additional tide gauge data, `plot_tide_gauge = TRUE`.

Step 3: To run the eiv_igp_t model the following function should be used:

    res_eiv_igp_t_detrend <- reslr_mcmc(
      input_data = CedarIslandNC_input_detrend,
      model_type = "eiv_igp_t",
      CI = 0.95
    )

This command takes the input data and the user specifies the statistical model, i.e. an integrated Gaussian process using the EIV uncertainty method (“eiv_igp_t”). It tells `reslr` to store the output of the model run in an object called `res_eiv_igp_t`. The `CI` setting allows the user to set the credible intervals, the current default is `CI = 0.95`.

Step 3a: A brief insight into the outputs of the `reslr_output` function can be obtained using:

    print(res_eiv_igp_t_detrend)

Step 4: The convergence of the algorithm is examined and he parameter estimates from the model can be investigated using the following:

    summary(res_eiv_igp_t_detrend)

If the model run has the package will print: “No convergence issues detected”. If the package prints: “Convergence issues detected, a longer run is necessary”. The user is required to update the `reslr_mcmc` function with additional iterations in the following manner:

    res_eiv_igp_t_detrend <- reslr_mcmc(
      input_data = CedarIslandNC_input_detrend,
      model_type = "eiv_igp_t",
      # Update these values
      n_iterations = 6000, # Number of iterations
      n_burnin = 1000, # Number of iterations to discard at the beginning
      n_thin = 4, # Reduces number of output samples to save memory and computation time
      n_chains = 3 # Number of Markov chains
    )

For the parameter estimates, the length scale parameter, “rho” is the correlation parameter and “nu” is the standard deviation of the rate process. “sigma_y” is the variation of the model.

Step 5: The results from the EIV IGP model for the de-trended data can be illustrated using:

    plot(res_eiv_igp_t_detrend,
      plot_type = "model_fit_plot",
      xlab = "Year (CE)",
      ylab = "Sea Level (m)",
      plot_proxy_records = TRUE,
      plot_tide_gauges = FALSE
    )

The output of this function is a graph of the input data, i.e. Age and SL and associated uncertainty boxes, and the model fit with 95 % credible interval. The caption provides the model type used and number of proxy sites and tide gauge sites used and can be removed if necessary with `plot_caption = FALSE`. In order to view the rate of change plot, the following setting should be used:

    plot(res_eiv_igp_t_detrend,
      plot_type = "rate_plot",
      xlab = "Year (CE)",
      y_rate_lab = "Rate of Change (mm per year)"
    )

This prints the plot of the rate of change with 95 % credible intervals. The caption provides the model type, the number of proxy sites and tide gauge sites that were used.

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

    output_dataframes <- res_eiv_igp_t_detrend$output_dataframes

## Using a different input age unit

The default input age for the reslr package is in units of years in the Common Era. The user can select to input the age value in Before Present by updating the `input_age_type = "BP"` within the `reslr_load` function. Inside the package, the Age is converted into CE in order to run the models. The plots are then altered to account for the input age in BP and the x-axis is be reversed.

Load in the data using the `reslr_load` function:

    CedarIslandNC_input_age_BP <- reslr_load(
      data = data_age_bp,
      input_age_type = "BP"
    )

The output of this function is a list of two dataframes called `data` and `data_grid`. Both dataframes will contain two new columns which identify the `Age_type = "BP"` and an additional column is included called `Age_BP` which contains the original input Age in units Before Present.If the user is recreating these plots, they are required to use the `Age_BP` column along the x-axis as it provides the original input age in units Before Present (BP) and the x-axis will need to be reversed.

## Including Tide Gauge data

In this package, we have described three ways to select a tide gauge and in the example we showed how to include the closest tide gauge to the proxy site. Here we are demonstrating the other options available to the user.

    # For 2 site
    multi_site <- NAACproxydata[NAACproxydata$Site %in% c("Cedar Island", "Nassau"),]

If the user has a list of chosen tide gauges from PSMSL website, include them in a list in the `reslr_load` function as demonstrated:

    multi_site <- reslr_load(
      data = multi_site,
      include_tide_gauge = TRUE,
      include_linear_rate = TRUE,
      TG_minimum_dist_proxy = FALSE,
      # There is no limit to the number of tide gauges provided in the list
      list_preferred_TGs = c(
        "ARGENTIA", "MAYPORT",
        "JACKSONVILLE", "LAKE WORTH PIER",
        "MAYPORT (BAR PILOTS DOCK), FLORIDA"
      ),
      all_TG_1deg = FALSE,
      prediction_grid_res = 50,
      sediment_average_TG = 10
    )

Here is how to plot this example:

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

If the user requires all tide gauges within 1 degree of the proxy site, which is the equivalent of 111kms, this can be done by updating `all_TG_1deg=TRUE` in the following way:

    multi_site <- reslr_load(
      data = multi_site,
      include_tide_gauge = TRUE,
      include_linear_rate = TRUE,
      TG_minimum_dist_proxy = FALSE,
      list_preferred_TGs = NULL,
      all_TG_1deg = TRUE,
      prediction_grid_res = 50
    )

Here is how to plot this example:

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

## Plotting techniques

In the package, all plot labels for results, i.e. x and y labels and titles, can be updated in the following manner:

    # Example
    final_plots <- plot(x = reslr_mcmc(CedarIslandNC, model_type = "ni_spline_t"))
    final_plots$plot_result
    # Adding new title to the total model fit plot
    final_plots$plot_result + ggplot2::ggtitle("New Title Added as Example")
    final_plots$plot_result + ggplot2::xlab("New x axis label Added as Example")
    final_plots$plot_result + ggplot2::ylab("New y axis label Added as Example")

The user can access the dataframes creating these plots in order to recreate the output plots in their own style. To access the data frame used to create the raw plot use:

    data <- CedarIslandNC_input_detrend$data

To access the dataframe creating the output model fit plot use:

    data <- res_eiv_igp_t_detrend$output_dataframes

This dataframes contains the model fit under the column `pred` and the rate of change column is `rate_pred`.

For the `ni_gam_decomp` model, there are a separate dataframe for each component and the rate of change for each component has a corresponding dataframe. This ensures the decomposition of the RSL signal can be plotted with all four components together, using separate colours to identify the different drivers of RSL change.

## Accessing the posterior samples

In the package the user has the ability access all the posterior sample for each unknown parameter in the following way:

    # Example
    CedarIslandNC_input <- reslr_load(
      data = CedarIslandNC)
    res_eiv_slr_t <-
      reslr_mcmc(CedarIslandNC_input,
                 model_type = "eiv_slr_t")
    # Accessing the slope of the EIV simple linear regression
    beta <- res_eiv_slr_t$noisy_model_run_output$BUGSoutput$sims.list$beta

## Smoothing settings for splines

When constructing a spline piecewise polynomials join together at knots to form smooth curves. The number of knots for these models can be reduced to achieve a more smooth model fit or increased for more flexibility. In the `reslr` package, tests have been carried out to select the correct number of knots for the spline in time, spline in space time and the GAM. However, if the situation arise where the model fits require additional flexibility or require additional smoothing, the user can adjust these settings. To adjust the setting for the number of knots in the spline in time update the reslr function in the following way:

    res_ni_sp_t <-
      reslr_mcmc(CedarIslandNC_input,
                 model_type = "ni_spline_t",
                 spline_nseg = NULL)

By updating the `spline_nseg` function, the number of knots we change. Note the minimum value for this setting is 2. The default for this setting is `NULL` as we use a rule of thumb calculation to identify the number of knots based on the range of observations. Similarly, for the spline in space time. The number of knots is updated using:

    res_ni_sp_t <-
      reslr_mcmc(CedarIslandNC_input,
                 model_type = "ni_spline_st",
                 spline_nseg = NULL)

By updating the `spline_nseg` function, the number of knots we change. Note the minimum value for this setting is 2. The default for this setting is `NULL` as we use a rule of thumb calculation to identify the number of knots based on the range of observations.

When using the NI GAM approach, the number of knots for the separate components can be altered. To control the smoothness of the regional component (i.e. spline in time) the setting to vary is the `spline_nseg_t`. To vary the non-linear local component (i.e. spline in space time), the user can vary the `spline_nseg_st` setting. The following example will demonstrate these settings:

    res_ni_sp_t <-
      reslr_mcmc(CedarIslandNC_input,
                 model_type = "ni_gam_decomp",
                 spline_nseg_t = 20,
                 spline_nseg_st = 6)

The default settings have been tested using cross validation techniques to incorporate the require flexibility for both components.

## Cross Validation tests for Spline and NI-GAM

In the previous section, we discussed altering the smoothness of the splines by varying the number of knots in the model. The user can test the validity of the model and the number of knots selected using this cross validation function with in the package. For example, the user can run different values for the knots and compare the model fits for each setting using the following cross validation function.

    data1site_example <- NAACproxydata[NAACproxydata$Site == "Cedar Island",]
    # Cross Validation test
    cv <- cross_val_check(data = data1site_example,
                          model_type ="ni_spline_t",
                          n_iterations = 1000,
                          n_burnin = 100,
                          n_thin = 5,
                          n_chains = 2,
                          spline_nseg = NULL,# User the package to calculate the number of knots
                          # n_fold allows the user to alter the cross validation, i.e. 3, 5, 10 fold
                          n_fold = 3,
                          #To reproducible results,seed stores the output of the random selection
                          seed = NULL,
                          CI = 0.95)# Size of the credible intervals and prediction intervals

To alter the number of knots in the model, include the `spline_nseg` or `spline_nseg_t` or `spline_nseg_st` arguments in this function. The different meanings for these arguments are described in the section above. By altering the `CI` argument, the user can change the size of the prediction intervals.

The `n_fold = 3` argument allows the user to change the number of folds used in the cross validation test. We recommend a minimum of 3 folds and the ideal number of folds is 5 or 10, however, using 10 folds will result in longer run times.

By updating the `model_type` argument, the user can undertake cross validation for the noisy input spline in time, the noisy input spline in space time or the noisy input generalised additive model. This function will print a statement regarding model convergence. If the convergence issues are detected, the package will inform the user to increase the number of iterations in the `cross_val_check` function.

The outputs of the `cross_val_check` function are stored as a list. To examine the true versus predicted plot use the following command.

    cv$true_pred_plot

This figure presents the true RSL value in metres versus the predicted RSL value in metres for each data site. The predicted means are the red dots and the vertical lines over each point are the prediction intervals.

To access the dataframe creating this plot use:

    CV_model_df <- cv$CV_model_df

This dataframe contains the `true_RSL` values from the test set and the corresponding predicted values `pred_RSL`. In this dataframe, there are the prediction intervals `upr_PI` and `lwr_PI`. The `SiteName` column identifies each site present in the dataframe and allows for site specific analysis of the model if required.

The model performance is examined using a variety of different test. The first is the out of sample empirical coverage. The empirical coverage provides the percentage of occasions that the true RSL observation is within the model prediction interval (PI) for RSL and is given by:

    # Overall coverage
    total_empirical_coverage <- cv$total_coverage
    total_empirical_coverage
    #> [1] 0.9903846
    # Coverage by site
    coverage_by_site <- cv$coverage_by_site
    coverage_by_site
    #> # A tibble: 1 × 2
    #>   SiteName                         coverage_by_site
    #>   <fct>                                       <dbl>
    #> 1 "Cedar Island,\n North Carolina"            0.990
    # Size of the prediction intervals
    prediction_interval_size <- cv$prediction_interval_size
    prediction_interval_size
    #> # A tibble: 1 × 2
    #>   SiteName                         PI_width
    #>   <fct>                               <dbl>
    #> 1 "Cedar Island,\n North Carolina"   -0.254

The prediction intervals are created using posterior predictive simulations with the full error structure, i.e. \(\hat{y}_{ij} \sim N(\hat{f}_{ij}, \sigma_{y_{ij}}^2 + \sigma_{{t}_{ij}}^2 + \sigma^2)\) where \(\sigma_{y_{ij}}^2\) is the observed uncertainty associated with the RSL and \(\sigma_{{t}_{ij}}^2\) is calculated to account for the noisy input uncertainty associated with the input (time). The size of these prediction intervals for each site can be examined to understand how the coverage is calculated and using different prediction intervals, e.g. 95% versus 50%, the model validity can be further examined.

The remaining tools used to examine model performance within the `reslr` package are the Root Mean Squared Error (RMSE), mean error (ME) and mean absolute error (MAE). The RMSE provides insight into prediction performance in the same units as the response (meters). The RMSE can be examined by site or for each fold in the cross validation test. The overall mean error (ME) by finding the difference between the predicted observation and the true RSL observation. The mean absolute error (MAE) is calculated by taking the absolute value of the mean error, in turn, this provides the degree to which our model is biased. For each test, the recommendation is the lower the value the better the model fit and these values can be accessed by:

    # Overall
    ME_MAE_RSME_overall <- cv$ME_MAE_RSME_overall
    ME_MAE_RSME_overall
    #>         RSME        MAE           ME
    #> 1 0.04672331 0.03622631 -0.004581598
    # By fold and site
    ME_MAE_RSME_fold_site <- cv$ME_MAE_RSME_fold_site
    ME_MAE_RSME_fold_site
    #> # A tibble: 3 × 5
    #>   SiteName                         CV_fold_number    RSME    MAE        ME
    #>   <fct>                            <fct>            <dbl>  <dbl>     <dbl>
    #> 1 "Cedar Island,\n North Carolina" 1              0.112   0.0446 -0.0189
    #> 2 "Cedar Island,\n North Carolina" 2              0.00518 0.0302 -0.000888
    #> 3 "Cedar Island,\n North Carolina" 3              0.0363  0.0337  0.00614
    # By site
    ME_MAE_RSME_site <- cv$ME_MAE_RSME_site
    ME_MAE_RSME_site
    #> # A tibble: 1 × 4
    #>   SiteName                           RSME    MAE       ME
    #>   <fct>                             <dbl>  <dbl>    <dbl>
    #> 1 "Cedar Island,\n North Carolina" 0.0467 0.0362 -0.00458
    # By fold
    ME_MAE_RSME_fold <- cv$ME_MAE_RSME_fold
    ME_MAE_RSME_fold
    #> # A tibble: 3 × 4
    #>   CV_fold_number    RSME    MAE        ME
    #>   <fct>            <dbl>  <dbl>     <dbl>
    #> 1 1              0.112   0.0446 -0.0189
    #> 2 2              0.00518 0.0302 -0.000888
    #> 3 3              0.0363  0.0337  0.00614

For more information about the outputs to these cross validation tests for RSL data refer to Upton et al. 2023 paper.