Supplement the data fitted to a linear model with model fit statistics.

This method is deprecated because using broom::augment() is a better solution to supplement data from a linear model. If you have missing values in your model data, you may need to refit the model with na.action = na.exclude.

Usage

# S3 method for class 'lm'
fortify(model, data = model$model, ...)

Arguments

model

linear model

data

data set, defaults to data used to fit model

...

not used by this method

Value

The original data with extra columns:

.hat

Diagonal of the hat matrix

.sigma

Estimate of residual standard deviation when corresponding observation is dropped from model

.cooksd

Cooks distance, cooks.distance()

.fitted

Fitted values of model

.resid

Residuals

.stdresid

Standardised residuals

Examples

mod <- lm(mpg ~ wt, data = mtcars)

# Show augmented model
head(augment(mod))
#> # A tibble: 6 × 9
#>   .rownames   mpg    wt .fitted .resid   .hat .sigma .cooksd .std.resid
#>   <chr>     <dbl> <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>      <dbl>
#> 1 Mazda RX4  21    2.62    23.3 -2.28  0.0433   3.07 1.33e-2    -0.766 
#> 2 Mazda RX…  21    2.88    21.9 -0.920 0.0352   3.09 1.72e-3    -0.307 
#> 3 Datsun 7…  22.8  2.32    24.9 -2.09  0.0584   3.07 1.54e-2    -0.706 
#> 4 Hornet 4…  21.4  3.22    20.1  1.30  0.0313   3.09 3.02e-3     0.433 
#> 5 Hornet S…  18.7  3.44    18.9 -0.200 0.0329   3.10 7.60e-5    -0.0668
#> 6 Valiant    18.1  3.46    18.8 -0.693 0.0332   3.10 9.21e-4    -0.231 
head(fortify(mod))
#> Warning: `fortify(<lm>)` was deprecated in ggplot2 3.6.0.
#> ℹ Please use `broom::augment(<lm>)` instead.
#>                    mpg    wt       .hat   .sigma      .cooksd  .fitted
#> Mazda RX4         21.0 2.620 0.04326896 3.067494 1.327407e-02 23.28261
#> Mazda RX4 Wag     21.0 2.875 0.03519677 3.093068 1.723963e-03 21.91977
#> Datsun 710        22.8 2.320 0.05837573 3.072127 1.543937e-02 24.88595
#> Hornet 4 Drive    21.4 3.215 0.03125017 3.088268 3.020558e-03 20.10265
#> Hornet Sportabout 18.7 3.440 0.03292182 3.097722 7.599578e-05 18.90014
#> Valiant           18.1 3.460 0.03323551 3.095184 9.210650e-04 18.79325
#>                       .resid   .stdresid
#> Mazda RX4         -2.2826106 -0.76616765
#> Mazda RX4 Wag     -0.9197704 -0.30743051
#> Datsun 710        -2.0859521 -0.70575249
#> Hornet 4 Drive     1.2973499  0.43275114
#> Hornet Sportabout -0.2001440 -0.06681879
#> Valiant           -0.6932545 -0.23148309

# Using augment to convert model to ready-to-plot data
ggplot(augment(mod), aes(.fitted, .resid)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


# Colouring by original data not included in the model
ggplot(augment(mod, mtcars), aes(.fitted, .std.resid, colour = factor(cyl))) +
  geom_point()