Description Usage Arguments Value References Examples

This function allows you to compute the distance between two curves with the chosen metric.

1 |

`grid` |
the grid (of length |

`x` |
a vector containing the first curve. |

`y` |
a vector containing the second curve. |

`metric` |
the chosen distance to be used: |

`p` |
a positive numeric value containing the parameter of the regularizing function for the generalized Mahalanobis distance. |

`lambda` |
a vector containing the eigenvalues of the functional data from which |

`phi` |
a matrix containing the eigenfunctions of the functional data from which |

The function returns a numeric value indicating the distance between the two curves.

Ghiglietti A., Ieva F., Paganoni A. M. (2017). Statistical inference for stochastic processes:
Two-sample hypothesis tests, *Journal of Statistical Planning and Inference*, 180:49-68.

Ghiglietti A., Paganoni A. M. (2017). Exact tests for the means of gaussian stochastic processes.
*Statics & Probability Letters*, 131:102–107.

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 29 30 31 32 | ```
# 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
# with the Karhunen - LoÃ¨ve expansion
m1 <- t^2 * ( 1 - t )
lambda <- rep( 0, K )
theta <- matrix( 0, K, P )
for ( k in 1:K ) {
lambda[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
z <- simulate_KL( t, n, m1, rho = lambda, theta = theta )
# Extract two rows of the functional data
x <- z$data[[1]][1, ]
y <- z$data[[1]][2, ]
d <- funDist( t, x, y, metric = "L2" )
``` |

martinoandrea92/dpdistance documentation built on Nov. 18, 2017, 5:13 p.m.

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