ordinal_location_2 function

Computes the estimated location of an ordinal time series with respect to the lowest category

ordinal_location_2 computes the estimated location of an ordinal time series with respect to the lowest category UTF-8

ordinal_location_2(series, states, distance = "Block", normalize = FALSE)

Arguments

• series: An OTS.
• states: A numerical vector containing the corresponding states.
• distance: A function defining the underlying distance between states. The Hamming, block and Euclidean distances are already implemented by means of the arguments "Hamming", "Block" (default) and "Euclidean". Otherwise, a function taking as input two states must be provided.
• normalize: Logical. If normalize = FALSE (default), the value of the standard estimated location is returned. Otherwise, the function returns the normalized standard estimated location.

Returns

The estimated location with respect to the lowest category.

Details

Given an OTS of length $T$ with range $\mathcal{S}=\{s_0, s_1, s_2, \ldots, s_n\}$ ($s_0 < s_1 < s_2 < \ldots < s_n$), $\overline{X}_t=\{\overline{X}_1,\ldots, \overline{X}_T\}$, the function computes the estimated location with respect to the lowest state, that is, the state $s_j$ such that $a_j=d(s_j, s_0)$ is the closest to $\frac{1}{T}\sum_{t=1}^Td\big(\overline{X}_t, s_0\big)$ is determined, where $d(\cdot, \cdot)$ is a distance between ordinal states.

Examples

estimated_location <- ordinal_location_2(series = AustrianWages$data[[100]],
states = 0 : 5) # Computing the location estimate
# with respect to the lowest state for one series in dataset AustrianWages

References

Rdpack::insert_ref(key="weiss2019distance",package="otsfeatures")

Author(s)

Ángel López-Oriona, José A. Vilar