PST (version 0.81)

generate: Generate sequences using a probabilistic suffix tree

Description

Generate sequences using a probabilistic suffix tree

Usage

## S3 method for class 'PSTf':
generate(object, l, n, s1, p1, method, L)

Arguments

object

a probabilistic suffix tree, i.e., an object of class "PSTf" as returned by the pstree, prune or

l

integer. Length of the sequence(s) to generate.

n

integer. Number of the sequence(s) to generate.

s1

character. The first state in the sequences. The length of the vector should equal n. If specified, the first state in the sequence(s) is not randomly generated but taken from s1.

p1

numeric. An optional probability vector for generating the first position state in the sequence(s). If specified, the first state in the sequence(s) is randomly generated using the probability distribution in p1 instead of the probability dis

method

character. If method=pmax, at each position the state having the highest probability is chosen. If method=prob, at each position the state is generated using the corresponding probability distribution taken from object

L

Integer: Maximal depth used to extract the probability distributions from the PST object.

Value

• A state sequence object (an object of class stslist) containing n sequences. This object can be passed as argument to all the functions for visualization and analysis provided by the TraMineR package.

Details

As a probabilistic suffix tree (PST) represents a generating model, it can be used to generate artificial sequence data sets. Sequences are built by generating the states at each successive position. The process is similar to sequence prediction (see predict), except that the retrieved conditional probability distributions provided by the PST are used to generate a symbol instead of computing the probability of an existing state.

Examples

data(s1)
s1.seq <- seqdef(s1)
S1 <- pstree(s1.seq, L=3)

## Generating 10 sequences
generate(S1, n=10, l=10, method="prob")

## First state is generated with p(a)=0.9 and p(b)=0.1
generate(S1, n=10, l=10, method="prob", p1=c(0.9, 0.1))