# dataf2rawfd: Transform a 'dataf' Object to Raw Functional Data In ddalpha: Depth-Based Classification and Calculation of Data Depth

 dataf2rawfd R Documentation

## Transform a `dataf` Object to Raw Functional Data

### Description

From a (possibly multivariate) functional data object `dataf` constructs an array of the functional values evaluated at an equi-distant grid of points.

### Usage

``````dataf2rawfd(dataf, range = NULL, d = 101)
``````

### Arguments

 `dataf` Functions to be transformed, represented by a (possibly multivariate) `dataf` object of their arguments and functional values. `m` stands for the number of functions. The grid of observation points for the functions in `dataf` may not be the same. `range` The common range of the domain where the functions `dataf` are observed. Vector of length 2 with the left and the right end of the interval. Must contain all arguments given in `dataf`. If the range is not provided, the smallest interval in which all the arguments from the data functions are contained is chosen as the domain. `d` Grid size to which all the functional data are transformed. All functional observations are transformed into vectors of their functional values of length `d` corresponding to equi-spaced points in the domain given by the interval `range`. Functional values in these points are reconstructed using linear interpolation, and extrapolation, see Nagy et al. (2016).

### Value

If the functional data are univariate (scalar-valued), a matrix of size `m*d` is given, with each row corresponding to one function. If the functional data are `k`-variate with k>1, an array of size `m*d*k` of the functional values is given.

### Author(s)

Stanislav Nagy, nagy@karlin.mff.cuni.cz

`rawfd2dataf`

`depthf.fd1`

`depthf.fd2`

### Examples

``````## transform a matrix into a functional data set and back
n = 5
d = 21
X = matrix(rnorm(n*d),ncol=d)
R = rawfd2dataf(X,range=c(0,1))
R2 = dataf2rawfd(R,range=c(0,1),d=d)
all.equal(X,R2)

## transform a functional dataset into a raw matrix of functional values
dataf = dataf.population()\$dataf
dataf2rawfd(dataf,range=c(1950,2015),d=66)

## transform an array into a multivariate functional data set and back
k = 3
X = array(rnorm(n*d*k),dim=c(n,d,k))
R = rawfd2dataf(X,range=c(-1,1))
dataf2rawfd(R,range=c(-1,1),d=50)

``````

ddalpha documentation built on May 29, 2024, 1:12 a.m.