wt_lm: wt_lm

In MoritzFeigl/wateRtemp: Water temperature prediction modelling

Description

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.

Usage

wt_lm(
  train_data,
  test_data = NULL,
  catchment = NULL,
  type = "stepLM",
  cv_mode = "repCV",
  model_name = NULL,
  no_cores = parallel::detectCores() - 1,
  seed = NULL
)

Arguments

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.

Examples

## 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)

MoritzFeigl/wateRtemp documentation built on Sept. 6, 2021, 6:58 a.m.