augment.conditional_ccf | R Documentation |
This function produces estimated conditional cross-correlation between $x_t$ and $y_t$ at lag $k$, i.e. $r_k = E(x_ty_t+k|z_t)$.
## S3 method for class 'conditional_ccf'
augment(x, ...)
x |
Model object of class "conditional_ccf" returned from
|
... |
Additional arguments, not currently used. |
A tibble
with information
about data points.
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_y <- old_ts |>
conditional_mean(turbidity_downstream ~
s(level_upstream, k = 8) +
s(conductance_upstream, k = 8) +
s(temperature_upstream, k = 8))
fit_var_y <- 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_y
)
fit_mean_x <- old_ts |>
conditional_mean(turbidity_upstream ~
s(level_upstream, k = 8) +
s(conductance_upstream, k = 8) +
s(temperature_upstream, k = 8))
fit_var_x <- old_ts |>
conditional_var(
turbidity_upstream ~
s(level_upstream, k = 7) +
s(conductance_upstream, k = 7) +
s(temperature_upstream, k = 7),
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 = 5
) +
splines::ns(conductance_upstream, df = 5),
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(5, 5)
)
data_inf <- fit_c_ccf |> augment()
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