View source: R/fitted.l2boost.R
fitted.l2boost | R Documentation |
fitted
is a generic function which extracts fitted values from objects
returned by modeling functions.
## S3 method for class 'l2boost' fitted(object, m = NULL, ...)
object |
an l2boost object |
m |
the iteration number with the l2boost path. (default m=NULL) |
... |
other arguments |
fitted.l2boost
returns the function estimates obtained
from the training set observations of an l2boost model object at any point along the solution path.
The estimate, F_m(x) is evaluated at iteration m using the training data set x. By default,
fitted.l2boost
returns the estimate at the last iteration step M, unless a specific
iteration step m is specified.
The vector of fitted response estimates at the given iteration m. By default, the coefficients are obtained from the last iteration m=M.
fitted
and l2boost
and predict.l2boost
#-------------------------------------------------------------------------- # Example: Diabetes # # See Efron B., Hastie T., Johnstone I., and Tibshirani R. # Least angle regression. Ann. Statist., 32:407-499, 2004. data(diabetes, package="l2boost") l2.object <- l2boost(diabetes$x,diabetes$y, M=1000, nu=.01) # return the fitted values fitted(l2.object) fitted(l2.object, m=500) #' # Create diagnostic plots par(mfrow=c(2,2)) qqnorm(fitted(l2.object), ylim=c(0, 300)) qqline(fitted(l2.object), col=2) qqnorm(fitted(l2.object, m=500), ylim=c(0, 300)) qqline(fitted(l2.object, m=500), col=2) # Tukey-Anscombe's plot plot(y=residuals(l2.object), x=fitted(l2.object), main="Tukey-Anscombe's plot", ylim=c(-3e-13, 3e-13)) lines(smooth.spline(fitted(l2.object), residuals(l2.object), df=4), type="l", lty=2, col="red", lwd=2) abline(h=0, lty=2, col = 'gray') plot(y=residuals(l2.object, m=500), x=fitted(l2.object, m=500), main="Tukey-Anscombe's plot", ylim=c(-3e-13, 3e-13)) lines(smooth.spline(fitted(l2.object,m=500), residuals(l2.object, m=500), df=4), type="l", lty=2, col="red", lwd=2) abline(h=0, lty=2, col = 'gray')
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