View source: R/regression_functions.R
get_regression_points | R Documentation |
Output information on each point/observation used in an lm()
regression in
"tidy" format. This function is a wrapper function for broom::augment()
and renames the variables to have more intuitive names.
get_regression_points( model, digits = 3, print = FALSE, newdata = NULL, ID = NULL )
model |
an |
digits |
number of digits precision in output table |
print |
If TRUE, return in print format suitable for R Markdown |
newdata |
A new data frame of points/observations to apply |
ID |
A string indicating which variable in either the original data used to fit
|
A tibble-formatted regression table of outcome/response variable, all explanatory/predictor variables, the fitted/predicted value, and residual.
augment()
, get_regression_table()
, get_regression_summaries()
library(dplyr) library(tibble) # Convert rownames to column mtcars <- mtcars %>% rownames_to_column(var = "automobile") # Fit lm() regression: mpg_model <- lm(mpg ~ cyl, data = mtcars) # Get information on all points in regression: get_regression_points(mpg_model, ID = "automobile") # Create training and test set based on mtcars: training_set <- mtcars %>% sample_frac(0.5) test_set <- mtcars %>% anti_join(training_set, by = "automobile") # Fit model to training set: mpg_model_train <- lm(mpg ~ cyl, data = training_set) # Make predictions on test set: get_regression_points(mpg_model_train, newdata = test_set, ID = "automobile")
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