Predict from a fitted model
Usage
# S3 method for class 'agri_model'
predict(object, newdata = NULL, interval = FALSE, level = 0.9, ...)Arguments
- object
A model fitted by
train_model().- newdata
Optional data frame of features. When omitted, predictions for the training rows are returned, alongside the out-of-fold predictions, which are the honest ones.
- interval
Attach conformal prediction intervals.
- level
Coverage level for the interval.
- ...
Ignored.
Value
A data frame with the keys, .pred, and when newdata is omitted
.pred_oof; plus .lower and .upper when interval = TRUE.
Examples
p <- agri_project(demo_agri_data(n_units = 12, n_seasons = 3))
p <- build_features(phenology_windows(add_climate(p)))
m <- train_model(p, "yield", algorithm = "lm", k = 3)
#> Warning: 64 features for 36 observations. Unregularised linear learners will be rank deficient here; prefer a tree-based or penalised learner, or pass fewer `stats` to build_features().
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
head(predict(m, interval = TRUE))
#> unit_id season_id .pred .pred_oof .lower .upper
#> 1 F001 2018 7.976 -12.645130 -1064.044 1079.996
#> 2 F001 2019 7.948 17.146673 -1064.072 1079.968
#> 3 F001 2020 7.628 4.753249 -1064.392 1079.648
#> 4 F002 2018 7.518 -11.242862 -1064.502 1079.538
#> 5 F002 2019 7.487 12.529649 -1064.533 1079.507
#> 6 F002 2020 7.165 5.620737 -1064.855 1079.185