Extract Transition Matrix from Model

Extract the transition probability matrix from a TNA model object.

Usage

extract_transition_matrix(model, type = c("raw", "scaled"))

# S3 method for class 'nest_transition_matrix'
summary(object, ...)

Arguments

model

A netobject, TNA model object, mcml object, a list containing a weights element, or a bare weight matrix.

type

Character. Type of matrix to return:

"raw"

The raw weight matrix as stored in the model.

"scaled"

Row-normalized to ensure rows sum to 1.

Default: "raw".

object

For the summary() method: an object of class nest_transition_matrix.

...

In summary.nest_transition_matrix(): Additional arguments (ignored).

Value

For a single network: a square numeric matrix with row and column names as state names, of class c("nest_transition_matrix", "matrix", "array"). It behaves as an ordinary matrix; the class stamp only adds a summary() method returning a tidy from/to/weight data frame.

For an mcml object: a named list of such matrices, with macro first and then one element per cluster.

In summary.nest_transition_matrix(): A tidy data frame with columns from, to, weight, with one row per non-zero entry.

Details

TNA models store transition weights in different locations depending on the model type. This function handles the extraction automatically.

For "scaled" type, each row is divided by its sum to create valid transition probabilities. This is useful when the original weights don't sum to 1.

See also

extract_initial_probs for extracting initial probabilities, extract_edges for extracting an edge list.

Examples

seqs <- data.frame(V1 = c("A","B","A"), V2 = c("B","A","C"), V3 = c("A","C","B"))
net <- build_network(seqs, method = "relative")
trans_mat <- extract_transition_matrix(net)
print(trans_mat)
#>   A         B         C
#> A 0 0.3333333 0.6666667
#> B 1 0.0000000 0.0000000
#> C 0 1.0000000 0.0000000
#> attr(,"class")
#> [1] "nest_transition_matrix" "matrix"                 "array"