| fortify.lm {ggplot2} | R Documentation |
Supplement the data fitted to a linear model with model fit statistics.
fortify.lm(model, data = model$model, ...)
model |
linear model |
data |
data set, defaults to data used to fit model |
... |
not used |
The following statistics will be added to the data frame:
.hatDiagonal of the hat matrix
.sigmaEstimate of residual standard deviation when corresponding observation is dropped from model
.cooksdCooks distance, cooks.distance
.fittedFitted values of model
.residResiduals
.stdresidStandardised residuals
If you have missing values in your model data, you may need to refit
the model with na.action = na.preserve.
Hadley Wickham <h.wickham@gmail.com>
mod <- lm(mpg ~ wt, data = mtcars) head(fortify(mod)) head(fortify(mod, mtcars)) plot(mod, which = 1) qplot(.fitted, .resid, data = mod) + geom_hline() + geom_smooth(se = FALSE) qplot(.fitted, .stdresid, data = mod) + geom_hline() + 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, data = mod)) + geom_vline(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()