4 fitres() and fitresdf()
The function fitres() will look similar to those who have used augment() from broom. It creates a matrix of the fitted values, residuals, and residuals as a proportion (percent) based on the actual dependent variable’s values. When the data input is specified, the function produces a dataframe that merges the fitted values and residual variables as columns to said specified dataset. The function fitresdf() acts similarly except that its output is a data frame.
4.1 Without specifying data
model.lm <- lm(data = mtcars, formula = mpg ~ wt + gear)
head(fitres(model.lm, fit_type = 'response'))## fit residual residual_pct
## Mazda RX4 23.26669 -2.2666926 -0.10793774
## Mazda RX4 Wag 21.86801 -0.8680127 -0.04133394
## Datsun 710 24.91220 -2.1121984 -0.09264028
## Hornet 4 Drive 20.32266 1.0773414 0.05034305
## Hornet Sportabout 19.08853 -0.3885293 -0.02077697
## Valiant 18.97883 -0.8788289 -0.048554084.2 With specifying data
model.lm <- lm(data = mtcars, formula = mpg ~ wt + gear)
head(fitres(model = model.lm,
data = mtcars,
fit_type = 'response'))## mpg cyl disp hp drat wt qsec vs am gear carb fit
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 23.26669
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 21.86801
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 24.91220
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 20.32266
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 19.08853
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 18.97883
## residual residual_pct
## Mazda RX4 -2.2666926 -0.10793774
## Mazda RX4 Wag -0.8680127 -0.04133394
## Datsun 710 -2.1121984 -0.09264028
## Hornet 4 Drive 1.0773414 0.05034305
## Hornet Sportabout -0.3885293 -0.02077697
## Valiant -0.8788289 -0.04855408