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
)an lm() model object
number of digits precision in output table
If TRUE, return in print format suitable for R Markdown
A new data frame of points/observations to apply model to
obtain new fitted values and/or predicted values y-hat. Note the format of
newdata must match the format of the original data used to fit
model.
A string indicating which variable in either the original data used to fit
model or newdata should be used as
an identification variable to distinguish the observational units
in each row. This variable will be the left-most variable in the output data
frame. If ID is unspecified, a column ID with values 1 through the number of
rows is returned as the identification variable.
A tibble-formatted regression table of outcome/response variable, all explanatory/predictor variables, the fitted/predicted value, and residual.
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")
#> # A tibble: 32 × 5
#> automobile mpg cyl mpg_hat residual
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 20.6 0.37
#> 2 Mazda RX4 Wag 21 6 20.6 0.37
#> 3 Datsun 710 22.8 4 26.4 -3.58
#> 4 Hornet 4 Drive 21.4 6 20.6 0.77
#> 5 Hornet Sportabout 18.7 8 14.9 3.82
#> 6 Valiant 18.1 6 20.6 -2.53
#> 7 Duster 360 14.3 8 14.9 -0.578
#> 8 Merc 240D 24.4 4 26.4 -1.98
#> 9 Merc 230 22.8 4 26.4 -3.58
#> 10 Merc 280 19.2 6 20.6 -1.43
#> # ℹ 22 more rows
# 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")
#> # A tibble: 16 × 5
#> automobile mpg cyl mpg_hat residual
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 20.3 0.664
#> 2 Datsun 710 22.8 4 25.3 -2.48
#> 3 Duster 360 14.3 8 15.4 -1.10
#> 4 Merc 240D 24.4 4 25.3 -0.876
#> 5 Merc 280 19.2 6 20.3 -1.14
#> 6 Merc 450SLC 15.2 8 15.4 -0.197
#> 7 Lincoln Continental 10.4 8 15.4 -5.00
#> 8 Chrysler Imperial 14.7 8 15.4 -0.697
#> 9 Fiat 128 32.4 4 25.3 7.12
#> 10 Honda Civic 30.4 4 25.3 5.12
#> 11 Toyota Corolla 33.9 4 25.3 8.62
#> 12 Toyota Corona 21.5 4 25.3 -3.78
#> 13 Camaro Z28 13.3 8 15.4 -2.10
#> 14 Pontiac Firebird 19.2 8 15.4 3.80
#> 15 Ferrari Dino 19.7 6 20.3 -0.636
#> 16 Maserati Bora 15 8 15.4 -0.397