eval.posfd | 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 |
... |
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.
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
eval.fd
,
eval.monfd
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(fd(matrix(0,smallbasis$nbasis,1), smallbasis),
harmaccelLfd, lambda)
VanPrecPos <- smooth.pos(day.5, VanPrec, dayfdPar)
# compute fitted values using eval.posfd()
VanPrecPosFit1 <- eval.posfd(day.5, VanPrecPos$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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