Description Objects from the Class Slots Extends Methods Author(s) See Also Examples

An object of class `"PSTr"`

is a node of a probabilistic suffix tree (PST). The slot `prob`

contains one or several probability distributions (if the PST is segmented) and the slot `counts`

contains the empirical - possibly weighted - counts from which the probabilities are computed. The slot `leaf`

indicates whether the node (segment) is a terminal node (segment).
The 'flat' representation of a PST is an object of class `"PSTf"`

), that is a list that contains one element for each level of the tree. Each element of the list is itself a list whose elements are nodes, that is objects of class `PSTr`

.
The 'nested' representation of a probabilistic suffix tree (PST) is a nested list whose elements are children nodes of class `"PSTr"`

. This representation is used for printing and plotting PST, in which case the flat representation of a PST, i.e., an object of class `"PSTf"`

is turned into an object of class `"PSTr"`

by using the `as`

function.

Objects are created when calling the `pstree`

function.

`.Data`

:Object of class

`"list"`

. In the nested representation of a PST, the elements of the list are the children nodes. Otherwise the list is empty.`alphabet`

:Object of class

`"character"`

. Alphabet on which the sequences, and the PST are built. This slot is non-empty only for the root node of the nested representation of a PST.`labels`

:Object of class

`"character"`

containing the long state labels. This slot is non-empty only for the root node of the nested representation of a PST.`cpal`

:Object of class

`"character"`

. Color palette used to represent each state of the alphabet. This slot is non-empty only for the root node of the nested representation of a PST.`index`

:Object of class

`"matrix"`

. When the PST is segmented, indicates the id of the segment corresponding to each group.`counts`

:Object of class

`"matrix"`

. The counts to which the probability distributions are computed.`n`

:Object of class

`"matrix"`

. The number of occurrences of the context in the learning sample, see`cprob`

.`prob`

:Object of class

`"matrix"`

. The probability distributions computed from the counts.`path`

:Object of class

`"character"`

. The node label, i.e. the context which is the path from the node to the root node of the tree.`order`

:Object of class

`"integer"`

. The depth of the node in the tree, i.e., the order of the probability distribution(s) stored in the node.`leaf`

:Object of class

`"matrix"`

. Indicates whether the node (segment) is a terminal node (segment).`pruned`

:Object of class

`"matrix"`

. If the PST was pruned with the`delete=FALSE`

option, indicates whether the node (segment) is actually pruned. See`prune`

.

Class `"list"`

, from data part.
Class `"vector"`

, by class "list", distance 2.

- [[
`signature(x = "PSTr")`

: extract sub-branches of a nested representation of a PST.- plot
`signature(x = "PSTr", y = "ANY")`

: plot a PST, see`plot,PSTr,ANY-method`

.`signature(x = "PSTr")`

: print a PST, see`print,PSTr-method`

.- summary
`signature(object = "PSTr")`

: see`summary,PSTr-method`

.

Alexis Gabadinho

1 | ```
showClass("PSTr")
``` |

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