eval.monfd | Language Reference for FDA Library
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eval.monfd(evalarg, Wfd, Lfdobj=int2Lfd(0))
>evalarg
> and
the second to replications.
A monotone function data object h(t) is defined by h(t) =
lsfit
.
The function
Wfd
that defines the monotone function is
usually estimated by monotone smoothing function
smooth.monotone.
# Estimate the acceleration functions for growth curves # See the analyses of the growth data. # Set up the ages of height measurements for Berkeley data age <- c( seq(1, 2, 0.25), seq(3, 8, 1), seq(8.5, 18, 0.5)) # Range of observations rng <- c(1,18) # First set up a basis for monotone smooth # We use b-spline basis functions of order 6 # Knots are positioned at the ages of observation. norder <- 6 nbasis <- nage + norder - 2 wbasis <- create.bspline.basis(rng, nbasis, norder, age) # starting values for coefficient cvec0 <- matrix(0,nbasis,1) Wfd0 <- fd(cvec0, wbasis) # set up functional parameter object Lfdobj <- 3 # penalize curvature of acceleration lambda <- 10^(-0.5) # smoothing parameter growfdPar <- fdPar(Wfd0, Lfdobj, lambda) # Set up wgt vector wgt <- rep(1,nage) # Smooth the data for the first girl hgt1 = hgtf[,1] result <- smooth.monotone(age, hgt1, wgt, growfdPar) # Extract the functional data object and regression # coefficients Wfd <- result$Wfdobj beta <- result$beta # Evaluate the fitted height curve over a fine mesh agefine <- seq(1,18,len=101) hgtfine <- beta[1] + beta[2]*eval.monfd(agefine, Wfd) # Plot the data and the curve plot(age, hgt1, type="p") lines(agefine, hgtfine) # Evaluate the acceleration curve accfine <- beta[2]*eval.monfd(agefine, Wfd, 2) # Plot the acceleration curve plot(agefine, accfine, type="l") lines(c(1,18),c(0,0),lty=4)