fitted.l2boost: Extract the fitted model estimates along the solution path... In ehrlinger/l2boost: Exploring Friedman's Boosting Algorithm for Regularized Linear Regression

Description

`fitted` is a generic function which extracts fitted values from objects returned by modeling functions.

Usage

 ```1 2``` ```## S3 method for class 'l2boost' fitted(object, m = NULL, ...) ```

Arguments

 `object` an l2boost object `m` the iteration number with the l2boost path. (default m=NULL) `...` other arguments

Details

`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.

Value

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`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```#-------------------------------------------------------------------------- # 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') ```