Run linear model with crossvalidation over multiple dependent and all numeric independent variables in a data frame

The function runs a linear model on a list of x and list of y variables and conducts a k-folds cross validation, which returns a data frame containing the following: The r^2, p-value, slope, intercept of the global lm model & average r^2, average RMSE, average MAE from the crossvalidated model

extract_lm_cv_all(
  parameters,
  df,
  train_method = "lm",
  control_method = "repeatedcv",
  folds = 3,
  nrepeats = 5
)

Arguments

parameters

the list of dependent variables to be evaluated

df

data frame containing the values for parameter and algorithm arguments

train_method

A string specifying which classification or regression model to use (Default = "lm"). See ?caret::train for more details

control_method

A string specifying the resampling method (Default = "repeatedcv"). See ?caret::trainControl for more details

folds

the number of folds to be used in the cross validation model

nrepeats

the number of iterations to be used in the cross validation model

Value

A data frame of the model results

References

Johansen, Richard; et al. (2018). Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations. Harmful Algae. 76. 10.1016/j.hal.2018.05.001.

R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2018). caret: Classification and Regression Training. R package version 6.0-81. https://CRAN.R-project.org/package=caret

See also

Other extract_lm: extract_lm_cv_multi(), extract_lm_cv(), extract_lm()