calculate_wet_season_average_PRM {CalcThemAll.PRM}

Calculate Wet Season Average Pesticide Risk Metric Values Using Multiple Imputation

Description

Calculate Wet Season Average Pesticide Risk Metric Values Using Multiple Imputation

Usage

calculate_wet_season_average_PRM(
  daily_PRM_data,
  PRM_group = "Total PRM",
  imputations = 1000,
  min_sampling_days = 12,
  wet_season_length = 182
)

Arguments

daily_PRM_data

A data set of calculated daily average PRM values. This data set should also include a "Date", "Sampling Year" and "Site Name" column.

PRM_group

This specifies the name of the column to run the calculations on. The daily average calculations gives PRM for each pesticide type and total in different columns so this selects which to run. "Total" is set as the default as it is the PRM of all pesticides.

imputations

This sets the number of imputations to run. The more imputations the greater the reliability, however it also increases calculation time. You can increase imputations beyond 1000 however the improvement of the confidence interval on imputed values may not be sufficient to warrant increased computing time. We recommend 1000 :)

min_sampling_days

This is the minimum number of sampling days a site-year combination must have to calculate a wet season average PRM. No less than 12 is the default (1 for each month) for reliability but more is recommended.

wet_season_length

The length of the wet season in days.

Value

A data frame

Examples

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
Canto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Canto_pesticides_LOR_treated)
Celestial_City_2019_2020_daily_PRM <- Canto_daily_PRM %>%
dplyr::filter(`Site Name` == "Celestial City" & `Sampling Year` == "2019-2020")
CC2019_2020_wet_season_PSII_PRM <- calculate_wet_season_average_PRM(daily_PRM_data =
Celestial_City_2019_2020_daily_PRM, PRM_group = "PSII Herbicide PRM")
CC2019_2020_wet_season_PSII_PRM