Get daily PM2.5 model predictions
Arguments
- x
a vector of s2 cell identifers (
s2_cellobject)- dates
a list of date vectors for the predictions, must be the same length as
x- 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 date in dates. These numerics are the concentrations of fine
particulate matter, measured in micrograms per cubic meter. See vignette("cv-model-performance")
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
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)
#> ℹ (down)loading random forest model
#> loaded rf_pm_v1 in 14s
#> ✔ (down)loading random forest model [15.4s]
#>
#> ℹ checking that s2 are within the contiguous US
#> ✔ checking that s2 are within the contiguous US [56ms]
#>
#> ℹ adding coordinates
#> ✔ adding coordinates [14ms]
#>
#> ℹ adding elevation
#> ✔ adding elevation [79ms]
#>
#> ℹ adding HMS smoke data
#> ✔ adding HMS smoke data [1s]
#>
#> ℹ adding NARR
#> ✔ adding NARR [478ms]
#>
#> ℹ adding gridMET
#> ✔ adding gridMET [419ms]
#>
#> ℹ adding MERRA
#> ✔ adding MERRA [5s]
#>
#> ℹ adding time components
#> ✔ adding time components [10ms]
#>
#> [[1]]
#> # A tibble: 2 × 2
#> pm25 pm25_se
#> <dbl> <dbl>
#> 1 7.94 0.868
#> 2 9.02 1.31
#>
#> [[2]]
#> # A tibble: 2 × 2
#> pm25 pm25_se
#> <dbl> <dbl>
#> 1 4.84 0.467
#> 2 6.02 0.469
#>
# 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)
#> ℹ (down)loading random forest model
#> loaded rf_pm_v1 in 0s
#> ✔ (down)loading random forest model [8ms]
#>
#> ℹ checking that s2 are within the contiguous US
#> ✔ checking that s2 are within the contiguous US [60ms]
#>
#> ℹ adding coordinates
#> ✔ adding coordinates [23ms]
#>
#> ℹ adding elevation
#> ✔ adding elevation [104ms]
#>
#> ℹ adding HMS smoke data
#> ✔ adding HMS smoke data [1s]
#>
#> ℹ adding NARR
#> ✔ adding NARR [494ms]
#>
#> ℹ adding gridMET
#> ✔ adding gridMET [432ms]
#>
#> ℹ adding MERRA
#> ✔ adding MERRA [565ms]
#>
#> ℹ adding time components
#> ✔ adding time components [12ms]
#>
#> [[1]]
#> # A tibble: 2 × 2
#> pm25 pm25_se
#> <dbl> <dbl>
#> 1 8.59 0.618
#> 2 14.9 2.87
#>
#> [[2]]
#> # A tibble: 2 × 2
#> pm25 pm25_se
#> <dbl> <dbl>
#> 1 11.1 1.07
#> 2 16.8 1.20
#>