The row-stochastic transition-probability matrix \(P(\text{to} \mid
\text{from})\): each row holds the distribution over next states out of
a state, so the entries in a row sum to 1 (up to structural zeros).
This is the transition-probability matrix a Transition Network Analysis
reads; pair it with initial() for the initial-state probabilities.
Usage
transition_probabilities(fit)
# S3 method for class 'lsa'
transition_probabilities(fit)
# S3 method for class 'lsa_group'
transition_probabilities(fit)Arguments
- fit
An
lsafit fromlsa().
Value
A square numeric matrix with from states in rows and to
states in columns. For an lsa_group, a named list of such matrices,
one per group.
See also
initial() (initial-state probabilities), transitions()
(the tidy per-edge view), nodes()
Examples
transition_probabilities(lsa(group_regulation))
#> adapt cohesion consensus coregulate discuss emotion
#> adapt 0.0000000000 0.27308448 0.47740668 0.02161100 0.05893910 0.11984283
#> cohesion 0.0029498525 0.02713864 0.49793510 0.11917404 0.05958702 0.11563422
#> consensus 0.0047400853 0.01485227 0.08200348 0.18770738 0.18802338 0.07268131
#> coregulate 0.0162436548 0.03604061 0.13451777 0.02335025 0.27360406 0.17208122
#> discuss 0.0713743356 0.04758289 0.32118451 0.08428246 0.19488737 0.10579600
#> emotion 0.0024673951 0.32534367 0.32040888 0.03419105 0.10186817 0.07684173
#> monitor 0.0111653873 0.05582694 0.15910677 0.05792045 0.37543615 0.09071877
#> plan 0.0009745006 0.02517460 0.29040117 0.01721618 0.06789021 0.14682475
#> synthesis 0.2346625767 0.03374233 0.46625767 0.04447853 0.06288344 0.07055215
#> monitor plan synthesis
#> adapt 0.03339882 0.01571709 0.000000000
#> cohesion 0.03303835 0.14100295 0.003539823
#> consensus 0.04661084 0.39579712 0.007584137
#> coregulate 0.08629442 0.23908629 0.018781726
#> discuss 0.02227284 0.01164262 0.140976968
#> emotion 0.03630596 0.09975326 0.002819880
#> monitor 0.01814375 0.21563154 0.016050244
#> plan 0.07552379 0.37420822 0.001786584
#> synthesis 0.01226994 0.07515337 0.000000000