R: Methods for 'TSClusters'

tsclusters-methods {dtwclust} | R Documentation

## Methods for `TSClusters`

### Description

Methods associated with TSClusters and derived objects.

### Usage

    ## S4 method for signature 'TSClusters'
    initialize(.Object, ..., override.family = TRUE)

    ## S4 method for signature 'TSClusters'
    show(object)

    ## S3 method for class 'TSClusters'
    update(object, ..., evaluate = TRUE)

    ## S4 method for signature 'TSClusters'
    update(object, ..., evaluate = TRUE)

    ## S3 method for class 'TSClusters'
    predict(object, newdata = NULL, ...)

    ## S4 method for signature 'TSClusters'
    predict(object, newdata = NULL, ...)

    ## S3 method for class 'TSClusters'
    plot(
      x,
      y,
      ...,
      clus = seq_len(x@k),
      labs.arg = NULL,
      series = NULL,
      time = NULL,
      plot = TRUE,
      type = NULL,
      labels = NULL
    )

    ## S4 method for signature 'TSClusters,missing'
    plot(
      x,
      y,
      ...,
      clus = seq_len(x@k),
      labs.arg = NULL,
      series = NULL,
      time = NULL,
      plot = TRUE,
      type = NULL,
      labels = NULL
    )

### Arguments

`.Object` | A `TSClusters` prototype. You shouldn't use this, see Initialize section and the examples.
`...` | For `initialize`, any valid slots. For `plot`, passed to `ggplot2::geom_line()` for the plotting of the cluster centroids, or to `stats::plot.hclust()`; see Plotting section and the examples. For `update`, any supported argument. Otherwise ignored.
`override.family` | Logical. Attempt to substitute the default family with one that conforms to the provided elements? See Initialize section.
`object, x` | An object that inherits from TSClusters as returned by `tsclust()`.
`evaluate` | Logical. Defaults to `TRUE` and evaluates the updated call, which will result in a new `TSClusters` object. Otherwise, it returns the unevaluated call.
`newdata` | New data to be assigned to a cluster. It can take any of the supported formats of `tsclust()`. Note that for multivariate series, this means that it must be a list of matrices, even if the list has only one matrix.
`y` | Ignored.
`clus` | A numeric vector indicating which clusters to plot.
`labs.arg` | A list with arguments to change the title and/or axis labels. See the examples and `ggplot2::labs()` for more information.
`series` | Optionally, the data in the same format as it was provided to `tsclust()`.
`time` | Optional values for the time axis. If series have different lengths, provide the time values of the longest series.
`plot` | Logical flag. You can set this to `FALSE` in case you want to save the ggplot object without printing anything to screen
`type` | What to plot. `NULL` means default. See details.
`labels` | Whether to include labels in the plot (not for dendrogram plots). See details and note that this is subject to randomness.

### Details

The `update` method takes the original function call, replaces any provided argument and optionally evaluates the call again. Use `evaluate = FALSE` if you want to get the unevaluated call. If no arguments are provided, the object is updated to a new version if necessary (this is due to changes in the internal functions of the package, here for backward compatibility).

### Value

The plot method returns a `gg` object (or `NULL` for dendrogram plot) invisibly.

### Initialize

The initialize method is used when calling `methods::new()`. The `family` slot can be substituted with an appropriate one if certain elements are provided by the user. The initialize methods of derived classes also inherit the family and can use it to calculate other slots. In order to get a fully functional object, at least the following slots should be provided:

- `type`: "partitional", "hierarchical", "fuzzy" or "tadpole".

- `datalist`: The data in one of the supported formats.

- `centroids`: The time series centroids in one of the supported formats.

- `cluster`: The cluster indices for each series in the `datalist`.

- `control*`: A tsclust-controls object with the desired parameters.

- `distance*`: A string indicating the distance that should be used.

- `centroid*`: A string indicating the centroid to use (only necessary for partitional clustering).

*Necessary when overriding the default family for the calculation of other slots, CVIs or prediction. Maybe not always needed, e.g. for plotting.

### Prediction

The `predict` generic can take the usual `newdata` argument. If `NULL`, the method simply returns the obtained cluster indices. Otherwise, a nearest-neighbor classification based on the centroids obtained from clustering is performed:

1. `newdata` is preprocessed with `object@family@preproc` using the parameters in `object@args$preproc`.

2. A cross-distance matrix between the processed series and `object@centroids` is computed with `object@family@dist` using the parameters in `object@args$dist`.

3. For non-fuzzy clustering, the series are assigned to their nearest centroid's cluster. For fuzzy clustering, the fuzzy membership matrix for the series is calculated. In both cases, the function in `object@family@cluster` is used.

### Plotting

The plot method uses the `ggplot2` plotting system (see `ggplot2::ggplot()`).

The default depends on whether a hierarchical method was used or not. In those cases, the dendrogram is plotted by default; you can pass any extra parameters to `stats::plot.hclust()` via the ellipsis (`...`).

Otherwise, the function plots the time series of each cluster along with the obtained centroid. The default values for cluster centroids are: `linetype = "dashed"`, `size = 1.5`, `colour = "black"`, `alpha = 0.5`. You can change this by means of the ellipsis (`...`).

You can choose what to plot with the `type` parameter. Possible options are:

- `"dendrogram"`: Only available for hierarchical clustering.

- `"series"`: Plot the time series divided into clusters without including centroids.

- `"centroids"`: Plot the obtained centroids only.

- `"sc"`: Plot both series and centroids

In order to enable labels on the (non-dendrogram) plot, you have to select an option that plots the series and at least provide an empty list in the `labels` argument. This list can contain arguments for `ggrepel::geom_label_repel()` and will be passed along. The following are set by the plot method if they are not provided:

- `"mapping"`: set to aes_string(x = "t", y = "value", label = "label")

- `"data"`: a data frame with as many rows as series in the `datalist` and 4 columns:

  - `t`: x coordinate of the label for each series.

  - `value`: y coordinate of the label for each series.

  - `cl`: index of the cluster to which the series belongs (i.e. `x@cluster`).

  - `label`: the label for the given series (i.e. `names(x@datalist)`).

You can provide your own data frame if you want, but it must have those columns and, even if you override `mapping`, the `cl` column must have that name. The method will attempt to spread the labels across the plot, but note that this is subject to randomness, so be careful if you need reproducibility of any commands used after plotting (see examples).

If created, the function returns the `gg` object invisibly, in case you want to modify it to your liking. You might want to look at `ggplot2::ggplot_build()` if that's the case.

If you want to free the scale of the X axis, you can do the following:

`plot(x, plot = FALSE)` `+` `facet_wrap(~cl, scales = "free")`

For more complicated changes, you're better off looking at the source code at https://github.com/asardaes/dtwclust/blob/master/R/S4-TSClusters-methods.R and creating your own plotting function.

### Examples

    data(uciCT)

    # Assuming this was generated by some clustering procedure
    centroids <- CharTraj[seq(1L, 100L, 5L)]
    cluster <- unclass(CharTrajLabels)

    pc_obj <- new("PartitionalTSClusters",
                  type = "partitional", datalist = CharTraj,
                  centroids = centroids, cluster = cluster,
                  distance = "sbd", centroid = "dba",
                  control = partitional_control(),
                  args = tsclust_args(cent = list(window.size = 8L, norm = "L2")))

    fc_obj <- new("FuzzyTSClusters",
                  type = "fuzzy", datalist = CharTraj,
                  centroids = centroids, cluster = cluster,
                  distance = "sbd", centroid = "fcm",
                  control = fuzzy_control())

    show(fc_obj)

    ## Not run:
    plot(pc_obj, type = "c", linetype = "solid",
         labs.arg = list(title = "Clusters' centroids"))

    set.seed(15L)
    plot(pc_obj, labels = list(nudge_x = -5, nudge_y = 0.2),
         clus = c(1L,4L))

    ## End(Not run)

[Package dtwclust version 5.5.7 Index]