far (version 0.6-5)

fdata: Functional Data class

Description

Object of class 'fdata' and its methods.

Usage

as.fdata(object,...) as.fdata.matrix(object,..., col, p, dates, name) as.fdata.list(object,..., dates, name)

Arguments

object
A matrix or a list.
col
A vector giving the names of the variables to include in the 'fdata' object.
p
A real value giving the number of discretization point chosen.
dates
A vector of character containing the dates of the observations.
name
A vector of character containing the names of the variables (generated if not provided).
...
Additional arguments.

Value

An object of class fdata.

Details

Fdata objects are mainly used to modelize functional data in the purpose of computing functional autoregressive model by the far and kerfon functions.

An fdata is composed of one or several variables. Each ones is a functional time series.

To be more precise, every variable got a functional data by element of the dates (explicitly given or implicitly deduced). So the number of functional observations is a common data. In the contrary, each variable can be expressed in a different functional space. For example, if you got two variables, Temperature and Wind, measured during 30 days. Choosing a daily representation, the fdata will contain a 30 elements long dates vector. Nevertheless, the variables measurement can be different. If Temperature is measured every hour and Wind every two hours, the fdata object can handle such a representation. The only constraint is to get a regular measurement: no changes in the methodology. Basically, the fdata objects are discrete measurements but the modelization which can be used on it will make it functional. Indeed, The first methods implemented as far and kerfon use a linear approximation, but more sophisticate modelization, as splines or wavelets approximations may come.

See Also

far, multplot, maxfdata, kerfon.

Examples

# Reading of the data
library(stats)
data(UKDriverDeaths)

# Making the data of class 'fdata'
fUKDriverDeaths <- as.fdata(UKDriverDeaths,col=1,p=12,dates=1969:1984,
                            name="UK Driver Deaths")
summary(fUKDriverDeaths)

# ploting of the data : whole and 1 year
par(mfrow=c(2,1))
plot(fUKDriverDeaths,xval=1969+(1:192)/12,whole=TRUE,
     name="Whole Evolution : ")
plot(fUKDriverDeaths,date="1984",xval=1:12,
     name="Evolution during year 1984 : ")

# Matrix conversion
print(as.fdata(matrix(rnorm(50),10,5)))
print(as.fdata(matrix(rnorm(500),100,5),col=1:2,p=5))

# List Conversions
print(as.fdata(list("X"=matrix(rnorm(100),10,10),
"Z"=matrix(rnorm(50),5,10))))