Get regression points

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.

Usage

get_regression_points(
  model,
  digits = 3,
  print = FALSE,
  newdata = NULL,
  ID = NULL
)

Arguments

model: an lm() model object
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 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.
ID: 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.

Value

A tibble-formatted regression table of outcome/response variable, all explanatory/predictor variables, the fitted/predicted value, and residual.

Examples

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

Usage

Arguments

Value

See also

Examples