calc_dt_CI: Computing bootstrapped confidence intervals for dt

In PuwasalaG/conduits: CONDitional UI for Time Series normalisation

Computing bootstrapped confidence intervals for dt

Description

This function computes the bootstrapped confidence intervals for dt. It resample the residuals from the various models used in the conditional cross-correlation calculation to generate new data. As the residuals are serially correlated, a sieve bootstrap approach to capture the autocorrelation structure in the data.

Usage

calc_dt_CI(x, m, new_data = NULL)

Arguments

x	Model object of class "conditional_ccf" returned from conditional_ccf
m	number of replications for boostrap confidence intervals
new_data	the dataset with the some predictors that are set to the median value (if required). Default is set to NULL.

Value

A tibble with estimated time lag "dt"

Author(s)

Priyanga Dilini Talagala & Puwasala Gamakumara

Examples

## Not run: 
old_ts <- NEON_PRIN_5min_cleaned |>
  dplyr::select(
    Timestamp, site, turbidity, level, temperature
  ) |>
  tidyr::pivot_wider(
    names_from = site,
    values_from = turbidity:temperature
  )
fit_mean_y <- old_ts |>
  conditional_mean(turbidity_downstream ~
    s(level_upstream, k = 5) +
    s(temperature_upstream, k = 5)
  )
fit_var_y <- old_ts |>
  conditional_var(
    turbidity_downstream ~
      s(level_upstream, k = 4) +
      s(temperature_upstream, k = 4),
    family = "Gamma",
    fit_mean = fit_mean_y
  )
fit_mean_x <- old_ts |>
  conditional_mean(turbidity_upstream ~
    s(level_upstream, k = 5) +
    s(temperature_upstream, k = 5)
  )
fit_var_x <- old_ts |>
  conditional_var(
    turbidity_upstream ~
      s(level_upstream, k = 4) +
      s(temperature_upstream, k = 4),
    family = "Gamma",
    fit_mean = fit_mean_x
  )
fit_c_ccf <- old_ts |>
  tidyr::drop_na() |>
  conditional_ccf(
    I(turbidity_upstream * turbidity_downstream) ~
      splines::ns(level_upstream, df = 3) +
      splines::ns(temperature_upstream, df = 3),
    lag_max = 10,
    fit_mean_x = fit_mean_x, fit_var_x = fit_var_x,
    fit_mean_y = fit_mean_y, fit_var_y = fit_var_y,
    df_correlation = c(3, 3)
  )
df_dt <- fit_c_ccf |> calc_dt_CI(100)

# Calculate  dt vs an  upstream covariate while holding the
# remaining upstream covariates at their medians
new_data <- fit_c_ccf$data
new_data <- new_data |>
  dplyr::mutate(temperature_upstream = median(temperature_upstream))
df_dt2 <- fit_c_ccf |> calc_dt_CI(100, new_data)

## End(Not run)

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