```
rFUNTA(Data, centered = FALSE, type.inner = "max", type.outer = "median", tick.dist = 1,
nObs = nrow(Data))
```

Data

a matrix. Enter the discretized values of a functional data set in a n times T matrix, where n is the number of functional observations and T is the number of time points.

centered

boolean. If the data are already centered, that means, the mean of each row of

`Data`

is 0, this can be set to `TRUE`

to save computation time. Default value is `FALSE`

.
type.inner

One of

`"max"`

(default), `"median"`

, `"mean"`

. Note that only the default setting produces rFUNTA values as introduced in Kuhnt and Rehage (2016). The other options can be used if not the maximum intersection angle of each pair of functions is of interest, but the median or mean intersection angle.
type.outer

One of *median* value of the n-1 weighted intersection angles of each function is of interest, but the maximum or mean of it.

`"max"`

, `"median"`

(default), `"mean"`

. Note that only the default setting produces rFUNTA values as introduced in Kuhnt and Rehage (2016). The other options can be used if not the tick.dist

atomic vector. The distance between two neighbored time points can be set here. Default value is

`1`

.
nObs

atomic vector. If the dataset has more than one dimension, specify

`nObs`

with the number of observations per dimension. `Data`

then has to be in the style of `rbind(Dim1, Dim2, ...)`

. Note that `tick.dist`

has to be equal for all the dimensions.`Data`

corresponds to first element of `FUNTA`

.
x <- seq(0, 2*pi, by = 0.01) y1 <- sin(x) y2 <- sin(1.02*x) y3 <- cos(x) y <- rbind(y1, y2, y3) rFUNTA(y, tick.dist = 0.01)