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

Source: R/fortify-lm.r

If you have missing values in your model data, you may need to refit the model with na.action = na.exclude.

# S3 method for 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)
head(fortify(mod))
#>                    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
head(fortify(mod, mtcars))
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb       .hat
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 0.04326896
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 0.03519677
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 0.05837573
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 0.03125017
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 0.03292182
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 0.03323551
#>                     .sigma      .cooksd  .fitted     .resid   .stdresid
#> Mazda RX4         3.067494 1.327407e-02 23.28261 -2.2826106 -0.76616765
#> Mazda RX4 Wag     3.093068 1.723963e-03 21.91977 -0.9197704 -0.30743051
#> Datsun 710        3.072127 1.543937e-02 24.88595 -2.0859521 -0.70575249
#> Hornet 4 Drive    3.088268 3.020558e-03 20.10265  1.2973499  0.43275114
#> Hornet Sportabout 3.097722 7.599578e-05 18.90014 -0.2001440 -0.06681879
#> Valiant           3.095184 9.210650e-04 18.79325 -0.6932545 -0.23148309

plot(mod, which = 1)

ggplot(mod, aes(.fitted, .resid)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mod, aes(.fitted, .stdresid)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(fortify(mod, mtcars), aes(.fitted, .stdresid)) +
  geom_point(aes(colour = factor(cyl)))

ggplot(fortify(mod, mtcars), aes(mpg, .stdresid)) +
  geom_point(aes(colour = factor(cyl)))

plot(mod, which = 2)
ggplot(mod) +
  stat_qq(aes(sample = .stdresid)) +
  geom_abline()

plot(mod, which = 3)
ggplot(mod, aes(.fitted, sqrt(abs(.stdresid)))) +
  geom_point() +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

plot(mod, which = 4)
ggplot(mod, aes(seq_along(.cooksd), .cooksd)) +
  geom_col()

plot(mod, which = 5)
ggplot(mod, aes(.hat, .stdresid)) +
  geom_vline(size = 2, colour = "white", xintercept = 0) +
  geom_hline(size = 2, colour = "white", yintercept = 0) +
  geom_point() + geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mod, aes(.hat, .stdresid)) +
  geom_point(aes(size = .cooksd)) +
  geom_smooth(se = FALSE, size = 0.5)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

plot(mod, which = 6)
ggplot(mod, aes(.hat, .cooksd)) +
  geom_vline(xintercept = 0, colour = NA) +
  geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") +
  geom_smooth(se = FALSE) +
  geom_point()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mod, aes(.hat, .cooksd)) +
  geom_point(aes(size = .cooksd / .hat)) +
  scale_size_area()