Extract Initial Probabilities from Model

Extract the initial state probability vector from a TNA model object.

Usage

extract_initial_probs(model)

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

Arguments

model

A netobject, TNA model object, mcml object, or a list containing an initial element.

object

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

...

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

Value

For a single network: a named numeric vector of initial state probabilities summing to 1, of class c("nest_initial_probs", "numeric"). The class stamp only adds a summary() method returning a tidy state/prob data frame.

For an mcml object: a named list of such vectors, with macro first and then one element per cluster (taken from each layer's $inits, unstamped).

In summary.nest_initial_probs(): A tidy data frame with columns state and prob, sorted by decreasing probability.

Details

Initial probabilities represent the probability of starting a sequence in each state. If the model doesn't have explicit initial probabilities, this function falls back to a uniform distribution over the states of the transition matrix and warns.

See also

extract_transition_matrix for extracting the transition matrix, 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")
init_probs <- extract_initial_probs(net)
print(init_probs)
#>         A         B         C 
#> 0.6666667 0.3333333 0.0000000 
#> attr(,"class")
#> [1] "nest_initial_probs" "numeric"