# funData: 'S3' Class for functional datasets. A class for univariate or... In gmfd: Inference and Clustering of Functional Data

## Description

`S3` Class for functional datasets. A class for univariate or multivariate functional dataset.

## Usage

 `1` ```funData(grid, data) ```

## Arguments

 `grid` the grid over which the functional dataset is defined. `data` a vector, a matrix or a `list` of vectors or matrices containing the functional data.

## Value

The function returns a `S3` object of class `funData`, containing the `grid` over which the functional dataset is defined and a matrix or a `list` of vectors or matrices containing the functional data

`gmfd_simulate`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```# Define parameters n <- 50 P <- 100 K <- 150 # Grid of the functional dataset t <- seq( 0, 1, length.out = P ) # Define the means and the parameters to use in the simulation m1 <- t^2 * ( 1 - t ) m2 <- t * ( 1 - t )^2 rho <- rep( 0, K ) theta <- matrix( 0, K, P ) for ( k in 1:K) { rho[k] <- 1 / ( k + 1 )^2 if ( k%%2 == 0 ) theta[k, ] <- sqrt( 2 ) * sin( k * pi * t ) else if ( k%%2 != 0 && k != 1 ) theta[k, ] <- sqrt( 2 ) * cos( ( k - 1 ) * pi * t ) else theta[k, ] <- rep( 1, P ) } # Simulate the functional data x1 <- gmfd_simulate( n, m1, rho = rho, theta = theta ) x2 <- gmfd_simulate( n, m2, rho = rho, theta = theta ) FD <- funData( t, list( x1, x2 ) ) ```

### Example output

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gmfd documentation built on May 2, 2019, 10:57 a.m.