Supplement the data fitted to a linear model with model fit statistics.
If you have missing values in your model data, you may need to refit
the model with na.action = na.exclude
.
## S3 method for class 'lm' fortify(model, data = model$model, ...)
model |
linear model |
data |
data set, defaults to data used to fit model |
... |
not used by this method |
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, |
.fitted |
Fitted values of model |
.resid |
Residuals |
.stdresid |
Standardised residuals |
mod <- lm(mpg ~ wt, data = mtcars) head(fortify(mod)) head(fortify(mod, mtcars)) plot(mod, which = 1) ggplot(mod, aes(.fitted, .resid)) + geom_point() + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) ggplot(mod, aes(.fitted, .stdresid)) + geom_point() + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) 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) 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) ggplot(mod, aes(.hat, .stdresid)) + geom_point(aes(size = .cooksd)) + geom_smooth(se = FALSE, size = 0.5) 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() ggplot(mod, aes(.hat, .cooksd)) + geom_point(aes(size = .cooksd / .hat)) + scale_size_area()
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