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```
#' Function to calculate observed-predicted error statistics.
#'
#' @param df Data frame with observed-predicted variables.
#'
#' @param value_model The modelled/predicted variable in \code{"df"}.
#'
#' @param value_observed The observed variable in \code{"df"}.
#'
#' @param testing_only Should only the testing set be used for the calculation
#' of errors?
#'
#' @param as_long Should the returned tibble be in "long" format? This is useful
#' for plotting.
#'
#' @author Stuart K. Grange
#'
#' @return Tibble.
#'
#' @export
rmw_calculate_model_errors <- function(df, value_model = "value_predict",
value_observed = "value",
testing_only = TRUE, as_long = FALSE) {
# Check input
if (!all(c(value_model, value_observed) %in% names(df))) {
cli::cli_abort("`value_model` or `value_observed` not found in the input.")
}
# Filter only to testing set
if (testing_only && "set" %in% names(df)) {
df <- filter(df, set == "testing")
}
# Get observed mean for extra calculation
mean_observed <- df %>%
select(!!value_observed) %>%
pull() %>%
mean(na.rm = TRUE)
# Calculate counts of value pairs
df_select <- df %>%
select(!!value_model,
!!value_observed)
# All observations passed to the function
n_all <- nrow(df_select)
# Only observations that are paired and therefore can be used to calculate
# other statistics
n <- nrow(tidyr::drop_na(df_select))
# Use openair to do the calculations and do some cleaning afterwards
df <- df %>%
openair::modStats(
mod = value_model, obs = value_observed,
statistic = c("MB", "NMB", "MGE", "NMGE", "RMSE", "r", "COE", "IOA")
) %>%
select(-default) %>%
rename(mean_bias = MB,
normalised_mean_bias = NMB,
mean_gross_error = MGE,
normalised_mean_gross_error = NMGE,
root_mean_squared_error = RMSE,
coefficient_of_efficiency = COE,
index_of_agreement = IOA) %>%
mutate(
normalised_root_mean_squared_error = root_mean_squared_error / !!mean_observed,
r_squared = r ^ 2,
n_all = !!n_all,
n = !!n
) %>%
relocate(normalised_root_mean_squared_error,
.after = root_mean_squared_error) %>%
relocate(r_squared,
.after = r) %>%
as_tibble()
# Make data longer if desired
if (as_long) {
df <- df %>%
mutate(group = 1L) %>%
tidyr::pivot_longer(-group, names_to = "statistic") %>%
select(-group)
}
return(df)
}
```

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