residuals.fRegress {fda} | R Documentation |
Residuals from a model object of class fRegress
.
## S3 method for class 'fRegress': residuals(object, ...)
object |
Object of class inheriting from lmWinsor
|
... |
additional arguments for other methods |
object$y - predict(object)
The residuals produced by resid.fRegress
or
residuals.fRegress
are either a vector or a functional
parameter (class fdPar
) object, matching
the class of object\$y
.
Spencer Graves
fRegress
predict.fRegress
residuals
## ## example from help('lm') ## ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2,10,20, labels=c("Ctl","Trt")) weight <- c(ctl, trt) fRegress.D9 <- fRegress(weight ~ group) resid.fR.D9 <- resid(fRegress.D9) # Now compare with 'lm' lm.D9 <- lm(weight ~ group) resid.lm.D9 <- resid(lm.D9) all.equal(as.vector(resid.fR.D9), as.vector(resid.lm.D9)) ## ## resid from knee angle prediciton from hip angle; ## (gaittime <- as.numeric(dimnames(gait)[[1]])*20) gaitrange <- c(0,20) gaitbasis <- create.fourier.basis(gaitrange, nbasis=21) harmaccelLfd <- vec2Lfd(c(0, (2*pi/20)^2, 0), rangeval=gaitrange) gaitfd <- smooth.basisPar(gaittime, gait, gaitbasis, Lfdobj=harmaccelLfd, lambda=1e-2)$fd hipfd <- gaitfd[,1] kneefd <- gaitfd[,2] knee.hip.f <- fRegress(kneefd ~ hipfd) #knee.hip.e <- resid(knee.hip.f) #plot(knee.hip.e)