linearRegress: Linear Regression.
In amanda-park/easytidymodels: Make tidymodels even easier to run!

Description Usage Arguments Details Value Examples

Runs a linear regression model, and either:

fits a basic lm() model and shows diagnostics and model fit
Uses the tidymodels approach: evaluates it on training and testing set, and tunes hyperparameters.

linearRegress(
  response = response,
  computeMarginalEffects = FALSE,
  data = df,
  train = train_df,
  test = test_df,
  tidyModelVersion = FALSE,
  recipe = rec,
  folds = folds,
  evalMetric = "rmse"
)

`response`	Character. The variable that is the response for analysis.
`computeMarginalEffects`	Logical. Compute marginal effects for lm model?
`data`	The entire data frame. Used for tidyModelVersion = FALSE.
`train`	Data frame/tibble. The training data set.
`test`	Data frame/tibble. The testing data set.
`tidyModelVersion`	Logical. Run a tidymodel version of linear regression? If yes, will tune hyperparameters and return a tidymodels regression model. If no, will fit an lm() object and return output based on that computation.
`recipe`	A recipes::recipe object.
`folds`	A rsample::vfolds_cv object.
`evalMetric`	Character. The regression metric you want to evaluate the model's accuracy on (tidymodels only). Default is RMSE. Can choose from the following: rmse mae rsq mase ccc icc huber_loss

Note: Tidymodels version tunes the following parameters:

penalty: The total amount of regularization in the model. Also known as lambda.
mixture: The mixture amounts of different types of regularization (see below). If 1, amounts to LASSO regression. If 0, amounts to Ridge Regression. Also known as alpha.

A list.

If tidyModelVersion = TRUE:

Training set predictions
Training set evaluation on RMSE and MAE
Testing set predictions
Testing set evaluation on RMSE and MAE
Tuned model object

If tidyModelVersion = FALSE:

lm() model object
broom() cleaned object of summary of lm() model
diagnostic plots

library(easytidymodels)
library(dplyr)
library(recipes)
utils::data(penguins, package = "modeldata")

#Define your response variable and formula object here
resp <- "bill_length_mm"
formula <- stats::as.formula(paste(resp, ".", sep="~"))

#Split data into training and testing sets
split <- trainTestSplit(penguins, responseVar = resp)

#Create recipe for feature engineering for dataset, varies based on data working with
rec <- recipe(formula, split$train) %>% prep()
train_df <- bake(rec, split$train)
test_df <- bake(rec, split$test)
folds <- cvFolds(train_df)

#Fit a linear regression model (commented out only due to long run time)
#linReg <- linearRegress(recipe = rec, response = resp, data = penguins, tidyModelVersion = FALSE,
#folds = folds, train = train_df, test = test_df, evalMetric = "rmse")

#Visualize training data and its predictions
#linReg$trainPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for training data
#linReg$trainScore

#Visualize testing data and its predictions
#linReg$testPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for testing data
#linReg$testScore

#See the final model chosen by KNN based on optimizing for your chosen evaluation metric
#linReg$final

#See how model fit looks based on another evaluation metric
#linReg$tune %>% tune::show_best("mae")