fortify.lm {ggplot2} | R Documentation |
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) qplot(.fitted, .resid, data = mod) + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) qplot(.fitted, .stdresid, data = mod) + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) qplot(.fitted, .stdresid, data = fortify(mod, mtcars), colour = factor(cyl)) qplot(mpg, .stdresid, data = fortify(mod, mtcars), colour = factor(cyl)) plot(mod, which = 2) # qplot(sample =.stdresid, data = mod, stat = "qq") + geom_abline() plot(mod, which = 3) qplot(.fitted, sqrt(abs(.stdresid)), data = mod) + geom_smooth(se = FALSE) plot(mod, which = 4) qplot(seq_along(.cooksd), .cooksd, data = mod, geom = "bar", stat="identity") plot(mod, which = 5) qplot(.hat, .stdresid, data = mod) + geom_smooth(se = FALSE) 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) qplot(.hat, .stdresid, data = mod, 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() qplot(.hat, .cooksd, size = .cooksd / .hat, data = mod) + scale_area()