Skip to contents

Get regression points

Source: R/regression_functions.R
get_regression_points.Rd

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() or glm() 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.

See also

augment(), get_regression_table(), get_regression_summaries()

Examples

library(dplyr)
library(moderndive)

# Fit lm() regression:
life_exp_model <- lm(
  life_expectancy_2022 ~ gdp_per_capita,
  data = un_member_states_2024
)

# Get information on all points in regression:
get_regression_points(life_exp_model, ID = "country")
#> # A tibble: 188 × 5
#>    country   life_expectancy_2022 gdp_per_capita life_expectancy_2022…¹ residual
#>    <chr>                    <dbl>          <dbl>                  <dbl>    <dbl>
#>  1 Afghanis…                 53.6           356.                   71.5   -17.8 
#>  2 Albania                   79.5          6810.                   72.3     7.17
#>  3 Algeria                   78.0          4343.                   72.0     6.05
#>  4 Andorra                   83.4         41993.                   76.9     6.49
#>  5 Angola                    62.1          3000.                   71.8    -9.69
#>  6 Antigua …                 77.8         19920.                   74.0     3.77
#>  7 Argentina                 78.3         13651.                   73.2     5.11
#>  8 Armenia                   76.1          7018.                   72.3     3.80
#>  9 Australia                 83.1         65100.                   80.0     3.13
#> 10 Austria                   82.3         52085.                   78.3     4.02
#> # ℹ 178 more rows
#> # ℹ abbreviated name: ¹​life_expectancy_2022_hat

# Create training and test set based on un_member_states_2024:
training_set <- un_member_states_2024 %>%
  sample_frac(0.5)
test_set <- un_member_states_2024 %>%
  anti_join(training_set, by = "country")

# Fit model to training set:
life_exp_model_train <- lm(
  life_expectancy_2022 ~ gdp_per_capita,
  data = training_set
)

# Make predictions on test set:
get_regression_points(life_exp_model_train, newdata = test_set, ID = "country")
#> # A tibble: 94 × 5
#>    country   life_expectancy_2022 gdp_per_capita life_expectancy_2022…¹ residual
#>    <chr>                    <dbl>          <dbl>                  <dbl>    <dbl>
#>  1 Afghanis…                 53.6           356.                   72.4  -18.7  
#>  2 Algeria                   78.0          4343.                   72.9    5.14 
#>  3 Antigua …                 77.8         19920.                   74.8    3.00 
#>  4 Barbados                  78.6         20239.                   74.8    3.71 
#>  5 Belarus                   74.3          7888.                   73.3    0.96 
#>  6 Belize                    75.8          6984.                   73.2    2.61 
#>  7 Benin                     62.2          1303.                   72.5  -10.3  
#>  8 Bhutan                    72.3          3560.                   72.8   -0.479
#>  9 Bolivia                   72.5          3600.                   72.8   -0.294
#> 10 Burkina …                 63.4           830.                   72.5   -9.01 
#> # ℹ 84 more rows
#> # ℹ abbreviated name: ¹​life_expectancy_2022_hat