as.fdata(object,...)
as.fdata.matrix(object,..., col, p, dates, name)
as.fdata.list(object,..., dates, name)
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.
far
, multplot
,
maxfdata
, kerfon
.# 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))))