eval.posfd: Evaluate a Positive Functional Data Object

Description

Evaluate a positive functional data object at specified argument values, or evaluate a derivative of the functional object.

Usage

eval.posfd(evalarg, Wfdobj, Lfdobj=int2Lfd(0), returnMatrix=FALSE)
# S3 method for posfd
predict(object, newdata=NULL, Lfdobj=0,
                       returnMatrix=FALSE, ...)
# S3 method for posfd
fitted(object, ...)
# S3 method for posfd
residuals(object, ...)

Arguments

evalarg, newdata

a vector of argument values at which the functional data object is to be evaluated.

Wfdobj

a functional data object that defines the positive function to be evaluated. Only univariate functions are permitted.

Lfdobj

a nonnegative integer specifying a derivative to be evaluated. At this time of writing, permissible derivative values are 0, 1 or 2. A linear differential operator is not allowed.

object

an object of class posfd that defines the positive function to be evaluated. Only univariate functions are permitted.

returnMatrix

logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package.

…

optional arguments required by predict; not currently used.

Value

a matrix containing the positive function values. The first dimension corresponds to the argument values in evalarg and the second to replications.

Details

A positive function data object $h(t)$ is defined by $h(t) =[exp Wfd](t)$. The function Wfdobj that defines the positive function is usually estimated by positive smoothing function smooth.pos

Examples

Run this code

# NOT RUN {
harmaccelLfd <- vec2Lfd(c(0,(2*pi/365)^2,0), c(0, 365))
smallbasis   <- create.fourier.basis(c(0, 365), 65)
index        <- (1:35)[CanadianWeather$place == "Vancouver"]
VanPrec      <- CanadianWeather$dailyAv[,index, "Precipitation.mm"]
lambda       <- 1e4
dayfdPar     <- fdPar(smallbasis, harmaccelLfd, lambda)
VanPrecPos   <- smooth.pos(day.5, VanPrec, dayfdPar)
#  compute fitted values using eval.posfd()
VanPrecPosFit1 <- eval.posfd(day.5, VanPrecPos$Wfdobj)
#  compute fitted values using predict()
VanPrecPosFit2 <- predict(VanPrecPos, day.5)
# }
# NOT RUN {
all.equal(VanPrecPosFit1, VanPrecPosFit2)
# }
# NOT RUN {
#  compute fitted values using fitted()
VanPrecPosFit3 <- fitted(VanPrecPos)
#  compute residuals
VanPrecRes <- resid(VanPrecPos)
# }
# NOT RUN {
all.equal(VanPrecRes, VanPrecPos$y-VanPrecPosFit3)
# }
# NOT RUN {
# }