lm.boot | R Documentation |
Bootstrapping of linear model fits (using lm
). Bootstrapping
can be done by either resampling rows of the original data frame or
resampling residuals from the original model fit.
lm.boot(lm.object, R, rows = TRUE, new.xpts = NULL, ngrid = 100,
weights = NULL)
lm.object |
A linear model fit, produced by |
R |
The number of bootstrap replicates to use. |
rows |
Should we resample rows? Setting |
new.xpts |
Values at which you wish to make new predictions. If specified, fitted values from each bootstrap sample will be stored. |
ngrid |
If |
weights |
Reseampling weights; a vector of length equal to the number of observations. |
Currently, "lm.simpleboot"
objects have a simple print
method (which shows the original fit), a summary
method and a
plot
method.
An object of class "lm.simpleboot"
(which is a list) containing the
elements:
method |
Which method of bootstrapping was used (rows or residuals). |
boot.list |
A list containing values from each of the bootstrap samples. Currently, bootstrapped values are model coefficients, residual sum of squares, R-square, and fitted values for predictions. |
orig.lm |
The original model fit. |
new.xpts |
The locations where predictions were made. |
weights |
The resampling weights. If none were used, this
component is |
Roger D. Peng
The plot.lm.simpleboot
method.
data(airquality)
attach(airquality)
set.seed(30)
lmodel <- lm(Ozone ~ Wind)
lboot <- lm.boot(lmodel, R = 1000)
summary(lboot)
## With weighting
w <- runif(nrow(model.frame(lmodel)))
lbootw <- lm.boot(lmodel, R = 1000, weights = w)
summary(lbootw)
## Resample residuals
lboot2 <- lm.boot(lmodel, R = 1000, rows = FALSE)
summary(lboot2)
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