Get daily PM2.5 model predictions

Usage

predict_pm25(x, dates, keep_predictors = FALSE)

# S3 method for class 's2cd'
predict_pm25(x, dates = NULL, keep_predictors = FALSE)

# Default S3 method
predict_pm25(x, dates, keep_predictors = FALSE)

# S3 method for class 's2_cell'
predict_pm25(x, dates, keep_predictors = FALSE)

Arguments

x: a vector of s2 cell identifiers (s2_cell object), or a geomarker::s2cd() object containing both locations and prediction dates
dates: a list of date vectors for the predictions, the same length as x; omit for s2cd objects because their stored dates are used
keep_predictors: logical; return values for model predictors alongside PM2.5 estimates?

Value

a list of tibbles the same length as x, each containing columns for the predicted (pm25) and its standard error (pm25_se); with one row per requested date. These numerics are the concentrations of fine particulate matter, measured in micrograms per cubic meter. See the model-performance report for more details on the cross validated accuracy of the daily PM2.5 model predictions.

Details

Internally, loading the model file is cached, so repeated calls in the same R session will not require the overhead of loading the model file for a new prediction.

Examples

if (FALSE) { # \dontrun{
d <- list(
  "8841b39a7c46e25f" = as.Date(c("2023-05-18", "2023-11-06")),
  "8841a45555555555" = as.Date(c("2023-06-22", "2023-08-15"))
)

predict_pm25(x = s2::as_s2_cell(names(d)), dates = d)

# takes less time after called once because model file is cached in memory

d <- list(
  "8841b39a7c46e25f" = as.Date(c("2023-05-13", "2023-11-16")),
  "8841a45555555555" = as.Date(c("2023-06-21", "2023-08-25"))
)
predict_pm25(x = s2::as_s2_cell(names(d)), dates = d)

# locations and dates can also be supplied together
locations <- geomarker::s2cd(s2::as_s2_cell(names(d)), dates = d)
predict_pm25(locations, keep_predictors = TRUE)
} # }