Evaluate a Positive Functional Data Object
Evaluate a positive functional data object at specified argument values, or evaluate a derivative of the functional object.
eval.posfd(evalarg, Wfdobj, Lfdobj=int2Lfd(0)) ## S3 method for class 'posfd' predict(object, newdata=NULL, Lfdobj=0, ...) ## S3 method for class 'posfd' fitted(object, ...) ## S3 method for class 'posfd' residuals(object, ...)
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 |
... |
optional arguments required by |
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
a matrix containing the positive function values. The first dimension
corresponds to the argument values in evalarg and the second to
replications.
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$Wfdobj) # compute fitted values using predict() VanPrecPosFit2 <- predict(VanPrecPos, day.5) all.equal(VanPrecPosFit1, VanPrecPosFit2) # compute fitted values using fitted() VanPrecPosFit3 <- fitted(VanPrecPos) # compute residuals VanPrecRes <- resid(VanPrecPos) all.equal(VanPrecRes, VanPrecPos$y-VanPrecPosFit3)
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