eval.posfd {fda} | R Documentation |
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 posfd that defines the positive function
to be evaluated. Only univariate functions are permitted.
|
... |
optional arguments required by predict ; not currently used.
|
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.
harmaccelLfd365 <- 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, harmaccelLfd365, lambda) Van.pos <- smooth.pos(day.5, VanPrec, dayfdPar) VanPrecposvec <- eval.posfd(day.5, Van.pos$Wfdobj) VanPrecpos <- predict(Van.pos, day.5) all.equal(VanPrecposvec, VanPrecpos) VanPrecFit <- fitted(Van.pos) VanPrecRes <- resid(Van.pos) all.equal(VanPrecRes, Van.pos$y-VanPrecFit)