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Orchestrate Exploratory Data Analysis

Source: R/framework-eda.R
framework_eda.Rd

First, this method identifies change points in the original annual maximum series data. Then, the user is given the option to split the dataset into two or more homogenous subperiods (trend-free or with monotonic trends). Finally, this method performs a collection of statistical tests for identifying monotonic nonstationarity in the mean and variability of each subperiod (if the dataset was split) or of the entire dataset (if it was not split). The results of EDA can help guide FFA approach selection (stationary or nonstationary) and FFA model determination.

Usage

framework_eda(
  data,
  years,
  ns_splits = NULL,
  generate_report = TRUE,
  report_path = NULL,
  report_formats = "html",
  ...
)

Arguments

data

Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.

years

Numeric vector of observation years corresponding to data. Must be the same length as data and strictly increasing.

ns_splits

An integer vector of years used to split the data into homogeneous subperiods. For S-FFA, set to NULL (default). For NS-FFA, specify an integer vector of years with physical justification for change points, or NULL if no such years exist. In R, integers have the suffix L, so 1950L is a valid input to ns_splits, but 1950 is not (since R may interpret it as a floating point number).

generate_report

If TRUE (default), generate a report.

report_path

A character scalar, the file path for the generated report. If NULL (default), the report will be saved to a new temporary directory.

report_formats

A character vector specifying the output format for the report. Supported values are "md", "pdf", "html", and "json".

...

Additional arguments. See the "Optional Arguments" section for a complete list.

Value

eda_recommendations: A list containing the recommended FFA approach, split point(s) and nonstationary structure(s) from EDA:

  • approach: Either "S-FFA", "NS-FFA" (for a single homogeneous period), or "Piecewise NS-FFA" (for multiple homogeneous subperiods).

  • ns_splits: The split point(s) identified by the change point detection test with the the lowest statistically significant p-value, or NULL if no such point exists.

  • ns_structures: A list of structure objects for each homogeneous subperiod. Each structure is a list with boolean items location and scale, which represent a linear trend in the in the mean or variability of the data, respectively. If no trends were found in any homogeneous subperiod, ns_structures will be NULL.

submodule_results: A list of lists of statistical tests. Each list contains:

  • name: Either "Change Point Detection" or "Trend Detection".

  • start: The first year of the homogeneous subperiod.

  • end: The last year of the homogeneous subperiod.

  • Additional items from the statistical tests within the submodule.

Optional Arguments

  • alpha: The numeric significance level for all statistical tests (default is 0.05).

  • bbmk_samples: The number of samples used in the Block-Bootstrap Mann-Kendall (BBMK) test (default is 10000). Must be an integer.

  • window_size: The size of the window used to compute the variability series.

  • window_step: The number of years between successive moving windows. Used to compute the variability series.

See also

eda_pettitt_test(), eda_mks_test(), eda_mk_test(), eda_spearman_test(), eda_bbmk_test(), eda_pp_test(), eda_kpss_test(), eda_sens_trend(), eda_runs_test(), eda_white_test()