decode_sequence_states: Decode hidden states from a fitted HMM

In gp3sequences: Transparent Analysis of Ordered Categorical Sequences

Decode hidden states from a fitted HMM

Description

Decode hidden states from a fitted HMM

Usage

decode_sequence_states(
  model,
  data = NULL,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  method = c("viterbi", "posterior"),
  component = NULL
)

Arguments

model	A fitted single HMM or HMM mixture.
data	Optional new long-format data. Training sequences are used when omitted.
sequence_id_col, order_col, state_col	Sequence columns for new data.
method	"viterbi" or "posterior".
component	Mixture component to decode. When omitted for a mixture, each sequence uses its highest-responsibility component.

Value

A long data frame containing decoded latent states and posterior probabilities where available.

Examples

sequences <- data.frame(
  sequence_id = rep(c("s1", "s2", "s3", "s4"), each = 4L),
  sequence_order = rep(1:4, times = 4L),
  state = c("A", "B", "C", "D", "A", "B", "C", "C",
            "D", "C", "B", "A", "D", "C", "A", "A"),
  group = rep(c("g1", "g2"), each = 8L),
  stringsAsFactors = FALSE
)
model <- fit_sequence_hmm(sequences, 2L, max_iter = 5L)
decode_sequence_states(model)

gp3sequences documentation built on Aug. 23, 2026, 5:10 p.m.