int_to_prob | R Documentation |
int_to_prob
transforms a set of Multinomial logit regression
intercepts to the corresponding state transition or categorical emission
observation probabilities. Note that the first state or category is assumed
to be the reference category, hence no intercept is to specified for the
first state or category.
int_to_prob(int_matrix)
int_matrix |
A matrix with (number of states OR categories - 1) columns
and number of rows to be determined by the user. For obtaining the set of
probabilities of the complete transition probability matrix gamma or
categorical emission distribution matrix, the number of rows equals the
number of states |
Designed to ease the specification of informative hyper-prior values for the
mean intercepts of the transition probability matrix gamma and categorical
emission distribution(s) of the multilevel hidden Markov model through the
functions prior_gamma
and prior_emiss_cat
. No
check is performed on correct specifications of the dimensions.
int_to_prob
returns a matrix containing probabilities with
each row summing to one, with the number of columns equal to the number of
states / categories and the number of rows equal to the number rows
specified in the input matrix.
prob_to_int
for transforming a set of probabilities to
a set of Multinomial logit regression intercepts, prior_gamma
and prior_emiss_cat
for specifying informative hyper-priors
for the the multilevel hidden Markov model and mHMM
to fit a
multilevel hidden Markov model.
# example for transition probability matrix gamma with 3 states
m <- 3
gamma_int <- matrix(c(-1, -1,
3, 0,
0, 2), ncol = m-1, nrow = m, byrow = TRUE)
gamma_prob <- int_to_prob(gamma_int)
gamma_prob
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