Description Usage Arguments Examples
Multiple linear regression and step-wise linear regression implementation for stream water temperature prediction including Bayesian hyperparameter optimization. All results are stored automatically in the folder catchment/model_name.
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train_data |
Data frame containing training data created by using wt_preprocessing() |
test_data |
Data frame containing test data created by using wt_preprocessing() |
catchment |
Catchment name as string, used for storing results in current working directory. |
type |
Can be either "LM" for a multiple regression model of the form wt ~ Ta + Q, or "stepLM" for a step-wise linear regression model using all available variables and their interactions. |
cv_mode |
Cross-validation mode. Only relvenat when using type="stepLM". Can either be "repCV" for a 5times repeated 10-fold CV or "timeseriesCV" for a timeslice CV using intial window=730, horizon=90 and skip=60. |
model_name |
Name of this particular model run as string, used for storing results in the catchment folder. |
no_cores |
Number of cores used for computation. If NULL parallel::detectCores() - 1 is applied. |
seed |
Random seed. |
1 2 3 4 5 6 7 8 9 | ## Not run:
data(test_catchment)
wt_preprocess(test_catchment)
train_data <- feather::read_feather("test_catchment/train_data.feather")
test_data <- feather::read_feather("test_catchment/test_data.feather")
wt_lm(train_data, test_data, "test_catchment", "stepLM", "repCV", "standard_stepLM")
## End(Not run)
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