Converts raw data or other functional data classes into fdata class.
Create a functional data object of class fdata from (matrix,
data.frame, numeric, integer, fd, fds, fts or
sfts) class data.
fdata(mdata, argvals = NULL, rangeval = NULL, names = NULL, fdata2d = FALSE)
mdata | 
 Matrix of set cases with dimension (  | 
argvals | 
 Argvals, by default:   | 
rangeval | 
 (optional) Range of discretization points, by default:
range(  | 
names | 
 (optional) list with tree components:   | 
fdata2d | 
 TRUE class fdata2d, the functional data is observed in at
least a two grids (the   | 
Return fdata class object with: 
"data": matrix of set cases with dimension (n x m),
where n is the number of curves and m are the points observed
in each curve 
"rangeval": the discretizations points values, if not provided: 1:m 
"rangeval": range of the discretizations points values, by default: range(argvals) 
"names": (optional) list with main an overall title, xlab
title for x axis and ylab title for y axis.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
See Also as plot.fdata
## Not run: data(phoneme) mlearn<-phoneme$learn[1:4,1:150] # Center curves fdata.c=fdata.cen(mlearn)$Xcen par(mfrow=c(2,1)) plot(mlearn,type="l") plot(fdata.c,type="l") # Convert from class fda to fdata bsp1 <- create.bspline.basis(c(1,150),21) fd1 <- Data2fd(1:150,y=t(mlearn$data),basisobj=bsp1) fdataobj=fdata(fd1) # Convert from class fds, fts or sfts to fdata #require(fds) #a=fds(x = 1:20, y = Simulationdata$y, xname = "x", # yname = "Simulated value") #b=fts(x = 15:49, y = Australiasmoothfertility$y, xname = "Age", # yname = "Fertility rate") #c=sfts(ts(as.numeric(ElNino_ERSST_region_1and2$y), frequency = 12), xname = "Month", #yname = "Sea surface temperature") #class(a);class(b);class(c) #fdataobj=fdata(b) ## End(Not run)
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