augment.conditional_acf: Augment data with information from a conditional...

In PuwasalaG/conduits: CONDitional UI for Time Series normalisation

Augment data with information from a conditional auto-correlation fit

Description

This function produces estimated conditional autocorrelation between $x_t$ and $y_t$ at lag $k$, i.e. $r_k = E(x_ty_t+k|z_t)$.

Usage

## S3 method for class 'conditional_acf'
augment(x, ...)

Arguments

x	Model object of class "conditional_acf" returned from conditional_acf with information to append to observations.
...	Additional arguments, not currently used.

Value

A tibble with information about data points.

Examples

old_ts <- NEON_PRIN_5min_cleaned |>
  dplyr::select(
    Timestamp, site, turbidity, level,
    conductance, temperature
  ) |>
  tidyr::pivot_wider(
    names_from = site,
    values_from = turbidity:temperature
  )

fit_mean <- old_ts |>
  conditional_mean(turbidity_downstream ~
    s(level_upstream, k = 8) +
    s(conductance_upstream, k = 8) +
    s(temperature_upstream, k = 8))

fit_var <- old_ts |>
  conditional_var(
    turbidity_downstream ~
      s(level_upstream, k = 7) +
      s(conductance_upstream, k = 7) +
      s(temperature_upstream, k = 7),
    family = "Gamma",
    fit_mean = fit_mean
  )
fit_c_acf <- old_ts |>
  tidyr::drop_na() |>
  conditional_acf(
    turbidity_upstream ~ splines::ns(level_upstream, df = 5) +
      splines::ns(conductance_upstream, df = 5),
    lag_max = 10, fit_mean = fit_mean, fit_var = fit_var,
    df_correlation = c(5, 5)
  )

data_inf <- fit_c_acf |> augment()

PuwasalaG/conduits documentation built on April 22, 2023, 3:40 p.m.