# parallelComputeDistMat: Paralleize computing a distance matrix for functional... In maierhofert/classiFunc: Classification of Functional Data

## Description

Uses `parallelMap` to parallelize the computation of the distance matrix. This is done by dividing the data into batches and computing the distance matrix for each batch. For details on distance computation see `computeDistMat`.

## Usage

 ```1 2``` ```parallelComputeDistMat(x, y = NULL, method = "Euclidean", batches = 1L, ...) ```

## Arguments

 `x` [`matrix`] matrix containing the functional observations as rows. `y` [`matrix`] see `x`. The default `NULL` uses `y = x`. `method` [`character(1)`] character string describing the distance function to be used. For a full list execute `metricChoices()`. `Euclidean`equals `Lp` with `p = 2`. This is the default. `Lp, Minkowski`the distance for an Lp-space, takes `p` as an additional argument in `...`. `Manhattan`equals `Lp` with `p = 1`. `supremum, max, maximum`equals `Lp` with `p = Inf`. The supremal pointwise difference between the curves. `and ...`all other available measures for `dist`. `shortEuclidean`Euclidean distance on a limited part of the domain. Additional arguments `dmin` and `dmax` can be specified in `...`, giving the position of the first and the last point to use of an evenly spaced sequence from `0` to `1` of length `length(grid)`. The default values are `dmin = o` and `dmax = 1`, which results in the Euclidean distance on the entire domain. `mean`the absolute similarity of the overall mean values of the observations. `relAreas`the difference of the relation of two areas on parts of the domain given by `dmin1` to `dmax1` and `dmin2` to `dmax2`. They are defined analogously to `dmin` and `dmax` and take the same default values. `jump`the similarity of jump heights at points `t1` and `t2`, i.e. `x[t1 * length(x)] - x[t2 * length(x)]` for every functional observation `x`. The points `t1` and `t2` are the positions in an evenly spaced sequence from `0` to `1` of length `length(grid)` for which to compare the jump height. The default values are `t1 = 0` and `t2 = 1`. `globMax`the difference of the curves global maxima. `globMin`the difference of the curves global minima. `points`the mean absolute differences at certain observation points `.poi`, also called "points of impact". These are specified as a vector `.poi` of arbitrary length with values between `0` and `1`, encoding the the index of the points of observations. The default value is `.poi = seq(0, 1, length.out = length(grid))`, which results in the Manhattan distance. `custom.metric`your own semimetric will be used. Specify your own distance function in the argument `custom.metric`. `amplitudeDistance,phaseDistance`The amplitude distance or phase distance as described in Srivastava, A. and E. P. Klassen (2016). Functional and Shape Data Analysis. Springer. `FisherRao, elasticMetric`the elastic distance of the square root velocity of the curves as described in Srivastava and Klassen (2016). This equates to the Fisher Rao metric. `elasticDistance`weighted mean of the amplitude and the phase distance using the implementation in `elastic.distance`. Additional arguments are the numeric the penalization parameters `a,b,c` for the amplitude distance (`a^2`) and the phase distance (`b^2`). The default values are `a = 1/2, b = 1`. Alternatively `c` denotes the ratio of `2*a` and `b`. `lambda` is the additional penalization parameter for the warping allowed before calculating the elastic distance. The default is 1. `rucrdtw, rucred`Dynamic Time Warping Distance and Euclidean Distance from package `rucrdtw`. Implemented in Boersch-Supan (2016) and originally described in Rakthanmanon et al. (2012). `batches` [`integer(1)`] Number of roughly equal-sized batches to split data into. The distance computation is then carried out for each batch. `...` additional parameters to the (semi-)metrics.

## Value

a matrix of dimensions `nrow(x)` by `nrow(y)` containing the distances of the functional observations contained in `x` and `y`, if `y` is specified. Otherwise a matrix containing the distances of all functional observations within `x` to each other.

maierhofert/classiFunc documentation built on April 3, 2018, 9:27 p.m.